mcera investment retirement board agenda date: …

MCERA INVESTMENT RETIREMENT BOARD AGENDA

DATE: THURSDAY, MAY 24, 2018 MERCED COUNTY EMPLOYEES’ RETIREMENT ASSOCIATION

3199 ‘M’ STREET, MERCED, CA 95348

Please turn your cell phone, beeper or other electronic device to non-audible mode and please refrain from using them during the Board meeting.

CALL TO ORDER: 8:15 A.M.

● ROLL CALL. ● APPROVAL OF MINUTES – May 10, 2018.

PUBLIC COMMENT Members of the public may comment on any item under the Board’s jurisdiction. Matters presented under this item will not be discussed or acted upon by the Board at this time. For agenda items, the public may make comments at the time the item comes up for Board consideration. Persons addressing the Board will be limited to a maximum of five (5) minutes in total. Please state your name for the record. CLOSED SESSION As provided in the Ralph M. Brown Act, Government Code sections 54950 et seq., the Board may meet in closed session with members of its staff, county employees and its attorneys. These sessions are not open to the public and may not be attended by members of the public. The matters the Board will meet on in closed session are identified below. Any public reports of action taken in the closed session will be made in accordance with Government Code sections 54957.1.

(1) CONFERENCE WITH LEGAL COUNSEL - EXISTING LITIGATION

(Govt. Code § 54956.9(a)) 1. In re Wilmington Trust Securities Litigation, 10-cv-00990-SLR-SRF.

(2) CONFERENCE WITH LEGAL COUNSEL – SIGNIFICANT EXPOSURE TO

LITIGATION Pursuant to Government Code Section 54956.9(d)(2) – two cases.

(3) DISCUSSION AND POSSIBLE ACTION REGARDING INVESTMENTS IN RECOMMEDED FUNDS, ROLL CALL VOTE REQUIRED. (Govt. Code § 54956.81)

1. Discussion and possible adoption of private equity investment manager and fund recommendation – Cliffwater.

RETURN TO OPEN SESSION Report on action taken in closed session.

BOARD ACTION1/DISCUSSION Pursuant to Govt. Code § 31594 and MCERA’s Investment Objectives & Policy Statement due diligence analysis requirement:

1. Presentation and possible action of the April 2018 Investment Performance Report and

market update with possible board action on any funds - Meketa. 2. Presentation and possible action to approve functional asset allocation categorization and

Phase I asset allocation review - Meketa. 3. Presentation and possible action to support by adoption the reclassification of Retirement

Benefit Specialist and Retirement Officer classifications to Staff Services Analyst classification - Staff.

4. Review calendar of any training sessions and authorize expenditures for Trustees and Plan Administrator. Pursuant to Govt. Code § 31522.8 and MCERA’s Trustees Education and Training Policy requirements. Examples of upcoming training and educational sessions:

a. CALAPRS Trustee Round Table, June 8, 2018 Oakland, CA. b. CALAPRS Administrators’ Round Table, June 22, 2018, Oakland, CA. c. SACRS UC Berkeley Program, July 15-18, 2018, Berkeley, CA. d. CALAPRS Principles of Pension Management for Trustees, August 27-30, 2018,

Malibu, CA. e. CALAPRS Benefits and Attorney Round Table and Course in Disability

Administration, September 20-21, 2018, Glendale, CA. f. CALAPRS Administrators Institute, September 26-28, 2018, Carmel, CA. g. SACRS Fall Conference, November 13-18, 2018, Indian Wells, CA.

INFORMATION ONLY ADJOURNMENT

All supporting documentation is available for public review in the office of the Merced County Employees’ Retirement Association, 3199 “M” Street, Merced, California, 95348 during regular business hours, 8:00 a.m. – 5:00 p.m., Monday through Friday.

The agenda is available online at www.co.merced.ca.us/retirement

Any material related to an item on this Agenda submitted to the Merced County Employees’ Retirement Association, after distribution of the Agenda packet is available for public inspection in the office of the Merced County Employees’ Retirement Association. Persons who require accommodation for a disability in order to review an agenda, or to participate in a meeting of the Merced County Employees’ Retirement Association per the American Disabilities Act (ADA), may obtain assistance by requesting such accommodation in writing addressed to Merced County Employees’ Association, 3199 “M” Street, Merced, CA 95348 or telephonically by calling (209) 726-2724. Any such request for accommodation should be made at least 48 hours prior to the scheduled meeting for which assistance is requested.

1 “Action” means that the Board may dispose of any item by any action, including but not limited to the following acts: approve, disapprove, authorize, modify, defer, table, take no action, or receive and file.

http://www.co.merced.ca.us/retirement

MCERA RETIREMENT BOARD MEETING MINUTES THURSDAY, MAY 10, 2018

MERCED COUNTY EMPLOYEES’ RETIREMENT ASSOCIATION

3199 M STREET, MERCED, CA 95348 ROLL CALL: 8:15 A.M. Board Members Present: Darlene Ingersoll, Ryan Paskin, Al Peterson, Jerald O’Banion, David Ness, Janey Cabral, Jason Goins, Samuel Spangler, Michael Rhodes. Counsel: Forrest Hansen. Staff: Kristie Santos, Angelo Lamas, Mark Harman, Michelle Lee, Sheri Villagrana and Brenda Mojica. Absent: Karen Adams, Sandy Teague. APPROVAL OF MINUTES: April 26, 2018. Motion to approve the April 26, 2018 meeting minutes. Peterson/ O’Banion U/A (8-0) PUBLIC COMMENT No public comment.

CLOSED SESSION The meeting went into closed session.

RETURN TO OPEN SESSION Reorder of items.

(1) CONFERENCE WITH LEGAL COUNSEL - EXISTING LITIGATION

(Govt. Code § 54956.9(a)) Alameda County Deputy Sheriff’s Assn. et al v. Alameda County Employees’ Retirement Assn., et al. (2018) 19 Cal.App.5th 61. No reportable items.

(2) DISABILITY RETIREMENT APPLICATIONS: PERSONNEL EXCEPTION Disability update. No action required.

CONSENT CALENDAR

RETIREMENTS: All items of earnable compensation for service or disability retirements listed below are in compliance with the pay code schedule approved by the Board of Retirement. The retirement is authorized; however, administrative adjustments may be necessary to alter the amount due to: audit, late arrival of data, court order, etc.

MCERA Retirement Board Meeting Minutes – May 10, 2018 Page 2

______________________________________________________________________________________________________________________________________ 1“Action” means that the Board may dispose of any item by any action, including but not limited to the following acts: approve, disapprove, authorize, modify, defer, table, take no action, or receive and file

a. Ortega, Lory Auditor 21 Yrs. Svc. Eff. 04/14/2018 b. Mochel, Marilyn Public Health 9 Yrs. Svc. Eff. 03/30/2018 c. Cader, Mohamed DPW 1 Yrs. Svc. Eff. 03/06/2018* d. Smith, Derrell Weights & Measures 13 Yrs. Svc. Eff. 04/06/2018 e. Lopez, Debbie H.S.A. 19 Yrs. Svc. Eff. 05/13/2018 f. Burnett, Donald H.S.A. 12 Yrs. Svc. Eff. 04/28/2018 g. Morris, Anthony W. Fire 8 Yrs. Svc. Eff. 08/02/2017**

*Member deferred age 70**Reciprocal retirement date with CalPERS.YTD fiscal year 2017/2018 retirees: 093 YTD fiscal year 2016/2017 retirees: 082 YTD fiscal year 2015/2016 retirees: 065

REFUND OF SERVICE PURCHASE: None DEATH BENEFIT: None MONTHLY BUDGET REPORT: Submitted

Motion to approve Consent Calendar. O’Banion/Paskin U/A (8-0)

REGULAR CALENDAR

BOARD ACTION1/DISCUSSION

1. Discussion and possible action to support reclassification of selected MCERA classification; Retirement Fiscal Supervisor to Fiscal Manager - Staff.Motion to support the reclassification of MCERA’s vacant position of Retirement Fiscal Supervisor to Fiscal Manager.Trustee O’Banion recused himself from the vote on this item.Paskin/Peterson U/A (7-0)

2. Discussion and possible action on MCERA 1099R Policy - Staff.Plan Administrator discussed the purpose of implementing a 1099R Policy.Motion to approve the MCERA 1099R Policy.O’Banion/Ness U/A (8-0)

3. Discussion and possible action to adopt changes to the Disability Retirement Handbook - Staff. Motion to adopt changes and board recommended amendments to the Disability Retirement Handbook.Ness/Peterson U/A (8-0)

4. Presentation, discussion and possible adoption of Annual Budget for MCERA FY 2018/2019 (per Govt. Code § 31580.2) - Staff & Budget Committee.Plan Administrator and Accountant Mark Harman presented the Annual Budget for FY 2018/2019. Motion to adopt MCERA FY 2018/2019 budget. Peterson/Paskin U/A (8-0)

MCERA Retirement Board Meeting Minutes – May 10, 2018 Page 3

______________________________________________________________________________________________________________________________________ 1“Action” means that the Board may dispose of any item by any action, including but not limited to the following acts: approve, disapprove, authorize, modify, defer, table, take no action, or receive and file

5. Review and possible action on SACRS business packet to vote on proposed legislation (action taken in April 12, 2018 covered Board’s vote on proposed officer election slate and appointed Plan Administrator as voting delegate) - Staff. Direction provided to voting delegate on Senate Bill 1270. O’Banion/Peterson U/A (8-0)

6. Review and possible action on Legislative update as of April 20, 2018 - Staff. No action required.

7. Review calendar of any training sessions and authorize expenditures for Trustees and Plan Administrator. Pursuant to Govt. Code § 31522.8 and MCERA’s Trustees Education and Training Policy requirements. Examples of upcoming training and educational sessions:

a. SACRS Spring Conference, May 15-18, 2018, Anaheim, CA. b. CALAPRS Trustee Round Table, June 8, 2018 Oakland, CA. c. CALAPRS Administrators’ Round Table, June 22, 2018, Oakland, CA. d. SACRS UC Berkeley Program, July 15-18, 2018, Berkeley, CA. e. CALAPRS Principles of Pension Management for Trustees, August 27-30, 2018, Malibu, CA. f. CALAPRS Administrators Institute, September 26-28, 2018, Carmel, CA. g. SACRS Fall Conference, November 13-18, 2018, Indian Wells, CA.

Approval for Trustee Peterson to attend the SACRS Fall Conference in Indian Wells, California on November 13-16, 2018. O’Banion /Paskin U/A (8-0)

INFORMATION ONLY

ADJOURNMENT

The meeting adjourned at 9:55 a.m. Respectfully submitted,

Darlene Ingersoll, Chair

Al Peterson, Secretary

Date

Prelim

inary

M E K E T A I N V E S T M E N T G R O U P

B O S T O N M A S S A C H U S E T T S

C H IC A G O I L L I N O I S

M I A M I F LO R I D A

P O R T L A N D O R E G O N

S A N D IE G O C A L I F O R N I A

LO N D O N U N I T E D K I N G D O M

www. m ek et a gr ou p . com

I N T E R I M P E R F O R M A N C E U P D A T E

Merced County Employees’ Retirement Association

April 30, 2018

Confidentiality: This evaluation is prepared by Meketa Investment Group, Inc. for the exclusive use of the Merced County Employees’ Retirement Association.This evaluation is not to be used for any other purpose or by any parties other than it’s Board Members, employees, agents, attorneys, and/or consultants.

No other parties are authorized to review or utilize the information contained herein without expressed written consent.

Item 1

Allocation vs. Targets and PolicyCurrent

BalanceCurrent

Allocation Policy Difference Policy Range Within IPSRange?

_

US Equity $232,335,832 28.6% 27.0% 1.6% 22.0% - 32.0% YesInternational Equity $143,026,375 17.6% 16.0% 1.6% 14.0% - 18.0% YesEmerging Markets Equity $63,215,803 7.8% 7.0% 0.8% 5.0% - 9.0% YesUS Fixed Income $170,313,605 21.0% 17.0% 4.0% 15.0% - 19.0% NoBank Loans -- -- 5.0% -5.0% 4.0% - 6.0% NoHedge Funds $45,313,283 5.6% 5.0% 0.6% 4.0% - 6.0% YesPrivate Real Estate $59,698,686 7.4% 8.0% -0.6% 6.5% - 9.5% YesPrivate Equity $34,066,381 4.2% 9.0% -4.8% 7.0% - 11.0% NoReal Assets $52,451,151 6.5% 6.0% 0.5% 4.5% - 7.5% YesCash $10,677,776 1.3% 0.0% 1.3% 0.0% - 5.0% YesTotal $811,098,891 100.0% 100.0%

XXXXX

Merced County Employees' Retirement Association

Total FundAs of April 30, 2018

Prepared by Meketa Investment Group

Cash range displayed for illustrative purposes only.

Page 2 of 23




Page 3 of 23

Asset Class Performance Summary

Market Value($)

% ofPortfolio

1 Mo(%)

YTD(%)

FiscalYTD(%)

1 Yr(%)

3 Yrs(%)

5 Yrs(%)

10 Yrs(%)

Inception(%)

InceptionDate

_

Total Fund (Net) 811,098,891 100.0 0.4 0.0 8.3 10.2 6.4 7.6 -- -- Dec-94Total Fund (Gross) 0.4 0.1 8.5 10.4 6.7 8.0 5.8 --

Policy Index 0.6 0.1 7.7 10.0 7.0 7.8 -- -- Dec-94Total Fund w/o Alternatives (Net) 608,891,614 75.1 0.4 -0.4 8.5 10.7 6.6 7.7 -- -- Dec-94Total Fund w/o Alternatives (Gross) 0.4 -0.3 8.7 11.0 6.9 8.0 -- --

Policy Index w/o AI 0.5 -0.1 8.2 10.4 6.5 7.2 -- -- Dec-94US Equity (Net) 232,335,832 28.6 0.4 -0.2 11.6 13.6 11.3 13.8 -- -- Dec-94US Equity (Gross) 0.4 -0.1 11.7 13.8 11.5 14.1 9.4 --

80% R1000 / 20% R2000 0.4 -0.1 10.8 12.9 10.2 12.6 9.1 9.9 Dec-94International Equity (Net) 206,242,178 25.4 1.3 0.6 12.2 16.4 5.0 5.6 -- -- Dec-98International Equity (Gross) 1.3 0.7 12.6 16.9 5.5 6.2 3.8 --

International Equity Custom 1.5 1.0 13.0 17.2 6.0 6.3 2.7 4.8 Dec-98US Fixed Income (Net) 170,313,605 21.0 -0.6 -1.7 -0.3 0.3 1.8 2.0 -- -- Dec-94US Fixed Income (Gross) -0.6 -1.6 -0.2 0.5 2.0 2.2 3.6 --

US Fixed Custom -0.7 -1.4 0.0 0.6 1.9 2.0 3.9 5.6 Dec-94Hedge Fund (Net) 45,313,283 5.6 0.4 2.6 6.7 10.0 3.5 -- -- 4.3 Jun-14Hedge Fund (Gross) 0.4 2.7 6.9 10.2 3.5 -- -- 4.4

Hedge Fund Custom 0.2 0.8 5.3 5.5 2.9 -- -- 3.3 Jun-14Private Equity (Net) 34,066,381 4.2 0.0 2.4 12.5 12.5 7.4 9.8 6.8 7.5 Jun-05Private Equity (Gross) 0.0 2.4 12.5 12.5 7.4 9.8 7.0 7.7

Thomson Reuters Cambridge Private Equity Index 0.0 0.0 9.3 14.1 12.7 15.8 -- -- Jun-05Real Assets (Net) 112,149,837 13.8 0.6 -0.1 6.1 7.0 7.1 7.5 -- -- Mar-99Real Assets (Gross) 0.6 -0.1 6.1 7.0 7.8 8.4 -- --

Real Asset Custom 1.6 1.2 8.6 10.4 -- -- -- -- Mar-99




Page 4 of 23




Market Value($)

% ofPortfolio

1 Mo(%)

YTD(%)

FiscalYTD(%)

1 Yr(%)

3 Yrs(%)

5 Yrs(%)

10 Yrs(%)

Inception(%)

InceptionDate

_

Private Real Estate (Net) 59,698,686 7.4 0.0 3.0 7.7 9.0 7.8 7.9 -- -- Mar-99Private Real Estate (Gross) 0.0 3.0 7.7 9.0 8.6 8.9 5.4 8.8

NCREIF ODCE (net) 0.0 2.0 5.6 7.1 9.0 10.4 4.2 7.5 Mar-99Private Infrastructure (Net) 13,262,286 1.6 0.0 0.0 11.7 16.8 3.8 -- -- 5.7 Dec-14Private Infrastructure (Gross) 0.0 0.0 11.7 16.8 3.8 -- -- 5.7

S&P Global Infrastructure Net TR USD 2.4 -3.4 1.0 5.0 2.9 5.5 -- 3.6 Dec-14Private Natural Resources (Net) 6,899,875 0.9 0.0 2.3 5.9 6.6 -- -- -- 19.6 Sep-15Private Natural Resources (Gross) 0.0 2.3 5.9 6.6 -- -- -- 19.6

S&P Global Natural Resources Index TR USD 5.0 3.3 24.2 23.7 6.9 3.7 -0.3 22.4 Sep-15Cash (Net) 10,677,776 1.3 0.1 -- -- -- -- -- -- --Cash (Gross) 0.1 -- -- -- -- -- -- --

XXXXX

Page 5 of 23

Trailing Net Performance

Market Value($)

% ofPortfolio

% ofSector

1 Mo(%)

YTD(%)

FiscalYTD(%)

1 Yr(%)

3 Yrs(%)

5 Yrs(%)

10 Yrs(%)

Inception(%)

InceptionDate

_

Total Fund (Net) 811,098,891 100.0 -- 0.4 0.0 8.3 10.2 6.4 7.6 -- -- Dec-94Policy Index 0.6 0.1 7.7 10.0 7.0 7.8 -- -- Dec-94

Total Fund w/o Alternatives (Net) 608,891,614 75.1 75.1 0.4 -0.4 8.5 10.7 6.6 7.7 -- -- Dec-94Policy Index w/o AI 0.5 -0.1 8.2 10.4 6.5 7.2 -- -- Dec-94

US Equity (Net) 232,335,832 28.6 38.2 0.4 -0.2 11.6 13.6 11.3 13.8 -- -- Dec-9480% R1000 / 20% R2000 0.4 -0.1 10.8 12.9 10.2 12.6 9.1 9.9 Dec-94

Mellon Dynamic US Equity (Net) 73,866,512 9.1 31.8 0.0 -1.2 12.7 15.7 12.7 15.6 -- 18.0 Dec-12S&P 500 0.4 -0.4 11.0 13.3 10.6 13.0 9.0 14.7 Dec-12

Mellon Large Cap (Net) 118,130,823 14.6 50.8 0.3 -0.3 11.0 13.1 -- -- -- 15.2 Mar-16Russell 1000 0.3 -0.4 11.0 13.2 10.3 12.8 9.1 15.2 Mar-16

DFA Small Cap (Net) 20,187,161 2.5 8.7 0.8 -0.5 9.0 9.2 10.0 -- -- 8.8 Jun-14Russell 2000 0.9 0.8 10.0 11.5 9.6 11.7 9.5 8.4 Jun-14

PanAgora (Net) 20,151,336 2.5 8.7 1.1 4.4 13.1 12.3 9.8 -- -- 10.9 Sep-13Russell 2000 0.9 0.8 10.0 11.5 9.6 11.7 9.5 9.7 Sep-13

International Equity (Net) 206,242,178 25.4 33.9 1.3 0.6 12.2 16.4 5.0 5.6 -- -- Dec-98International Equity Custom 1.5 1.0 13.0 17.2 6.0 6.3 2.7 4.8 Dec-98

Copper Rock (Net) 23,650,544 2.9 11.5 1.7 -0.3 13.1 18.4 5.6 -- -- 8.4 Sep-13MSCI World ex USA Small Cap 1.5 1.0 14.7 18.8 10.0 9.4 5.7 8.5 Sep-13

EARNEST Partners (Net) 97,857 0.0 0.0

Mellon International (Net) 119,277,973 14.7 57.8 2.5 0.9 11.1 15.1 -- -- -- 14.4 Mar-16MSCI EAFE 2.3 0.7 10.7 14.5 4.9 5.9 2.4 13.9 Mar-16




The market value for Earnest Partners was provided by BNY Mellon.

Page 6 of 23




Market Value($)

% ofPortfolio

% ofSector

1 Mo(%)

YTD(%)

FiscalYTD(%)

1 Yr(%)

3 Yrs(%)

5 Yrs(%)

10 Yrs(%)

Inception(%)

InceptionDate

_

Wells Capital (Net) 63,215,803 7.8 30.7 -1.1 0.5 14.0 17.9 8.2 4.6 -- 4.3 Mar-12MSCI Emerging Markets -0.4 1.0 17.0 21.7 6.0 4.7 2.2 4.3 Mar-12

US Fixed Income (Net) 170,313,605 21.0 28.0 -0.6 -1.7 -0.3 0.3 1.8 2.0 -- -- Dec-94US Fixed Custom -0.7 -1.4 0.0 0.6 1.9 2.0 3.9 5.6 Dec-94

Barrow Hanley (Net) 133,819,010 16.5 78.6 -0.7 -2.2 -1.0 -0.3 1.1 1.4 -- -- Mar-10BBgBarc US Aggregate TR -0.7 -2.2 -1.0 -0.3 1.1 1.5 3.6 3.1 Mar-10

Vanguard Short-Term Treasury Index Fund (Net) 36,494,594 4.5 21.4 -0.2 -- -- -- -- -- -- 0.0 Feb-18BBgBarc US Govt 1-5 Yr TR -0.4 -0.7 -0.9 -0.8 0.3 0.5 1.7 0.0 Feb-18

Hedge Fund (Net) 45,313,283 5.6 5.6 0.4 2.6 6.7 10.0 3.5 -- -- 4.3 Jun-14Hedge Fund Custom 0.2 0.8 5.3 5.5 2.9 -- -- 3.3 Jun-14

OZ Domestic II (Net) 17,573,955 2.2 38.8 0.0 2.5 6.6 9.8 5.1 -- -- 6.1 Jun-14

Graham Absolute Return (Net) 3,128,143 0.4 6.9 2.0 5.2 -- -- -- -- -- 4.1 Aug-17

Wellington-Archipelago (Net) 6,117,860 0.8 13.5 -1.1 3.0 -- -- -- -- -- 4.7 Aug-17

KLS Diversified (Net) 5,046,988 0.6 11.1 0.2 0.8 -- -- -- -- -- 1.1 Oct-17

Winton (Net) 3,091,345 0.4 6.8 1.6 0.4 -- -- -- -- -- 3.0 Oct-17

Marshall Wace Eureka (Net) 3,144,977 0.4 6.9 0.2 3.9 -- -- -- -- -- 4.6 Nov-17

Silver Point Capital (Net) 7,210,016 0.9 15.9 1.5 3.3 -- -- -- -- -- 3.9 Nov-17HFRI Fund of Funds Composite Index 0.2 0.5 4.9 5.2 1.8 3.3 1.5 1.4 Nov-17

Page 7 of 23




Adams Street includes Adams Street 2005 NON, Adams Street 2005 US, Adams Street 2007 GL, and Adams Street 2011.

Pantheon I includes Pantheon US Fund VI and Pantheon Europe Fund I.Pantheon II includes Pantheon US Fund IX, Pantheon Asia Fund VI, and Pantheon Europe Fund V.Pantheon Secondary includes Pantheon GLO SEC III B.

The market value for Invesco IV and Invesco VI reflect a 9/30/2017 capital account balance.

Market Value($)

% ofPortfolio

% ofSector

1 Mo(%)

YTD(%)

FiscalYTD(%)

1 Yr(%)

3 Yrs(%)

5 Yrs(%)

10 Yrs(%)

Inception(%)

InceptionDate

_

Private Equity (Net) 34,066,381 4.2 4.2 0.0 2.4 12.5 12.5 7.4 9.8 6.8 7.5 Jun-05Thomson Reuters Cambridge Private Equity Index 0.0 0.0 9.3 14.1 12.7 15.8 -- -- Jun-05

Adams Street (Net) 8,093,552 1.0 23.8 0.0 4.7 13.1 13.1 10.8 12.7 8.4 6.5 Sep-05

Invesco IV (Net) 1,310,312 0.2 3.8 0.0 0.0 2.8 2.8 5.7 12.3 -- -- Jun-05

Invesco VI (Net) 5,539,203 0.7 16.3 0.0 0.0 10.6 10.6 8.6 -- -- 10.8 Jun-13

Ocean Avenue II (Net) 7,839,612 1.0 23.0 0.0 0.8 21.2 21.2 11.6 -- -- 7.5 Jun-14

Pantheon I (Net) 1,181,206 0.1 3.5 0.0 0.0 7.9 7.9 4.6 7.0 -- -- Dec-05

Pantheon II (Net) 3,813,457 0.5 11.2 0.0 0.0 12.7 12.7 12.5 13.1 -- 11.1 Dec-11

Pantheon Secondary (Net) 1,297,227 0.2 3.8 0.0 0.0 6.5 6.5 1.6 2.7 -- -- Jun-07

Raven Asset Fund II (Net) 4,741,812 0.6 13.9 0.0 8.4 10.1 10.1 -2.5 -- -- -3.8 Aug-14

Davidson Kempner Long-Term Distressed OpportunitiesFund IV (Net) 250,000 0.0 0.7 0.0 -- -- -- -- -- -- 0.0 Apr-18

Real Assets (Net) 112,149,837 13.8 13.8 0.6 -0.1 6.1 7.0 7.1 7.5 -- -- Mar-99Real Asset Custom 1.6 1.2 8.6 10.4 -- -- -- -- Mar-99

SSgA (Net) 32,288,990 4.0 28.8 2.3 -0.5 7.5 7.0 -- -- -- 7.0 Apr-17Real Asset Custom 1.6 1.2 8.6 10.4 -- -- -- 10.4 Apr-17

Page 8 of 23




Market Value($)

% ofPortfolio

% ofSector

1 Mo(%)

YTD(%)

FiscalYTD(%)

1 Yr(%)

3 Yrs(%)

5 Yrs(%)

10 Yrs(%)

Inception(%)

InceptionDate

_

Private Real Estate (Net) 59,698,686 7.4 53.2 0.0 3.0 7.7 9.0 7.8 7.9 -- -- Mar-99NCREIF ODCE (net) 0.0 2.0 5.6 7.1 9.0 10.4 4.2 7.5 Mar-99

Greenfield Gap VII (Net) 13,044,345 1.6 21.9 0.0 6.6 15.5 15.5 15.2 -- -- 13.7 Dec-14

Patron Capital V (Net) 5,109,044 0.6 8.6 0.0 0.0 19.3 36.8 -- -- -- 8.4 Jan-16

UBS Trumbull Property (Net) 41,491,091 5.1 69.5 0.0 2.4 4.7 6.0 7.3 8.6 -- -- Mar-99

Carlyle Realty VIII (Net) 54,206 0.0 0.1 0.0 -27.1 -- -- -- -- -- -27.1 Dec-17NCREIF-ODCE 0.0 2.2 6.3 8.1 10.0 11.4 5.1 2.2 Dec-17

Private Infrastructure (Net) 13,262,286 1.6 11.8 0.0 0.0 11.7 16.8 3.8 -- -- 5.7 Dec-14S&P Global Infrastructure Net TR USD 2.4 -3.4 1.0 5.0 2.9 5.5 -- 3.6 Dec-14

KKR Global II (Net) 8,725,906 1.1 65.8 0.0 0.0 5.8 14.1 8.0 -- -- 9.6 Dec-14

North Haven Infrastructure II (Net) 4,536,380 0.6 34.2 0.0 0.0 22.1 22.1 -- -- -- 5.1 May-15S&P Global Infrastructure Net TR USD 2.4 -3.4 1.0 5.0 2.9 5.5 -- 3.5 May-15

Private Natural Resources (Net) 6,899,875 0.9 6.2 0.0 2.3 5.9 6.6 -- -- -- 19.6 Sep-15S&P Global Natural Resources Index TR USD 5.0 3.3 24.2 23.7 6.9 3.7 -0.3 22.4 Sep-15

EnCap XI (Net) 308,419 0.0 4.5 0.0 -12.4 -- -- -- -- -- -39.6 Jul-17

EnCap IV (Net) 132,798 0.0 1.9 0.0 -- -- -- -- -- -- 0.0 Feb-18

GSO Energy Opportunities (Net) 3,441,860 0.4 49.9 0.0 4.0 11.3 11.3 -- -- -- 21.2 Nov-15

Taurus Mining (Net) 1,906,943 0.2 27.6 0.0 1.5 -0.3 1.4 -- -- -- 17.2 Sep-15

Taurus Mining Annex (Net) 1,109,855 0.1 16.1 0.0 0.0 10.0 10.6 -- -- -- 29.7 Jan-17S&P Global Natural Resources Index TR USD 5.0 3.3 24.2 23.7 6.9 3.7 -0.3 16.7 Jan-17

Page 9 of 23



Market Value($)

% ofPortfolio

% ofSector

1 Mo(%)

YTD(%)

FiscalYTD(%)

1 Yr(%)

3 Yrs(%)

5 Yrs(%)

10 Yrs(%)

Inception(%)

InceptionDate

_

Cash (Net) 10,677,776 1.3 1.3 0.1 -- -- -- -- -- -- --

Cash (Net) 6,486,389 0.8 60.7 0.1 0.5 1.2 1.4 0.8 0.8 -- -- Sep-03

Treasury Cash (Net) 4,191,387 0.5 39.3 XXXXX


Page 10 of 23

Calendar Year Performance2017

(%)2016

(%)2015

(%)2014

(%)2013

(%)2012

(%)2011

(%)2010

(%)2009

(%)2008

(%)_

Total Fund 16.6 7.4 0.0 6.1 15.7 11.8 -- -- -- --Policy Index 16.4 9.1 0.1 6.4 15.9 13.5 -- -- -- --

Total Fund w/o Alternatives 18.8 7.9 -1.5 5.4 17.1 12.6 4.3 -- -- --Policy Index w/o AI 17.7 8.3 -1.4 4.9 14.9 -- -- -- -- --

US Equity 21.8 15.7 -0.1 13.9 35.6 15.0 16.9 -- -- --80% R1000 / 20% R2000 20.3 13.9 -0.1 11.9 33.8 16.4 1.0 16.9 28.3 -37.3

Mellon Dynamic US Equity 28.3 14.5 0.3 19.4 38.8 -- -- -- -- --S&P 500 21.8 12.0 1.4 13.7 32.4 16.0 2.1 15.1 26.5 -37.0

Mellon Large Cap 21.6 -- -- -- -- -- -- -- -- --Russell 1000 21.7 12.1 0.9 13.2 33.1 16.4 1.5 16.1 28.4 -37.6

DFA Small Cap 12.0 24.4 -2.6 -- -- -- -- -- -- --Russell 2000 14.6 21.3 -4.4 4.9 38.8 16.3 -4.2 26.9 27.2 -33.8

PanAgora 10.8 20.4 -4.3 9.7 -- -- -- -- -- --Russell 2000 14.6 21.3 -4.4 4.9 38.8 16.3 -4.2 26.9 27.2 -33.8

International Equity 29.1 2.2 -5.2 -4.1 16.1 17.2 -10.6 -- -- --International Equity Custom 29.2 5.0 -5.3 -3.4 17.8 17.5 -11.7 8.2 32.5 -43.1

Copper Rock 31.6 -8.1 9.5 0.7 -- -- -- -- -- --MSCI World ex USA Small Cap 31.0 4.3 5.5 -5.3 25.6 17.5 -15.8 24.5 50.8 -48.0

EARNEST Partners

Mellon International 25.7 -- -- -- -- -- -- -- -- --MSCI EAFE 25.0 1.0 -0.8 -4.9 22.8 17.3 -12.1 7.8 31.8 -43.4




Page 11 of 23

2017(%)

2016(%)

2015(%)

2014(%)

2013(%)

2012(%)

2011(%)

2010(%)

2009(%)

2008(%)

_

Wells Capital 35.7 12.3 -12.3 -4.7 -1.6 -- -- -- -- --MSCI Emerging Markets 37.3 11.2 -14.9 -2.2 -2.6 18.2 -18.4 18.9 78.5 -53.3

US Fixed Income 3.7 5.6 -0.1 4.5 0.1 7.4 6.1 -- -- --US Fixed Custom 3.7 5.9 -0.4 5.0 -1.5 4.2 7.8 6.5 5.9 5.2

Barrow Hanley 3.4 2.7 0.8 5.5 -2.2 5.7 5.0 -- -- --BBgBarc US Aggregate TR 3.5 2.6 0.5 6.0 -2.0 4.2 7.8 6.5 5.9 5.2

Vanguard Short-Term Treasury Index Fund -- -- -- -- -- -- -- -- -- --BBgBarc US Govt 1-5 Yr TR 0.7 1.0 0.9 1.2 -0.1 1.0 3.2 3.6 1.0 8.4

Hedge Fund 10.9 0.2 0.1 -- -- -- -- -- -- --Hedge Fund Custom 7.3 3.4 0.2 -- -- -- -- -- -- --

OZ Domestic II 13.0 4.2 0.0 -- -- -- -- -- -- --

Graham Absolute Return -- -- -- -- -- -- -- -- -- --

Wellington-Archipelago -- -- -- -- -- -- -- -- -- --

KLS Diversified -- -- -- -- -- -- -- -- -- --

Winton -- -- -- -- -- -- -- -- -- --

Marshall Wace Eureka -- -- -- -- -- -- -- -- -- --

Silver Point Capital -- -- -- -- -- -- -- -- -- --HFRI Fund of Funds Composite Index 7.8 0.5 -0.3 3.4 9.0 4.8 -5.7 5.7 11.5 -21.4

Private Equity 13.3 3.7 9.3 14.9 2.3 -3.1 29.6 13.2 -18.4 12.6Thomson Reuters Cambridge Private Equity Index 22.2 18.0 2.5 20.8 38.0 21.7 -- -- -- --




Page 12 of 23

2017(%)

2016(%)

2015(%)

2014(%)

2013(%)

2012(%)

2011(%)

2010(%)

2009(%)

2008(%)

_

Adams Street 12.7 9.1 12.8 18.0 13.5 3.3 26.4 8.4 -18.6 -1.7

Invesco IV 12.3 6.9 11.2 23.9 -6.8 -4.5 -- -- -- --

Invesco VI 15.8 3.7 27.0 13.6 -- -- -- -- -- --

Ocean Avenue II 36.1 0.2 9.0 -- -- -- -- -- -- --

Pantheon I 11.8 1.4 2.7 10.1 8.3 -3.4 -- -- -- --

Pantheon II 21.7 9.7 7.5 23.8 13.6 -3.6 -- -- -- --

Pantheon Secondary 7.1 -1.1 0.6 4.3 -2.3 -7.6 -- -- -- --

Raven Asset Fund II -17.0 -0.1 -3.6 -- -- -- -- -- -- --

Davidson Kempner Long-Term Distressed Opportunities Fund IV -- -- -- -- -- -- -- -- -- --

Real Assets 9.6 6.4 8.5 8.8 7.7 13.3 8.1 -- -- --Real Asset Custom 12.9 -- -- -- -- -- -- -- -- --

SSgA -- -- -- -- -- -- -- -- -- --Real Asset Custom 12.9 -- -- -- -- -- -- -- -- --

Private Real Estate 8.0 6.1 9.1 8.9 7.7 13.1 8.1 -- -- --NCREIF ODCE (net) 6.7 7.8 14.0 11.5 12.9 9.8 15.0 15.3 -30.4 -10.7

Greenfield Gap VII 14.7 13.5 10.6 -- -- -- -- -- -- --

Patron Capital V 36.0 -- -- -- -- -- -- -- -- --

UBS Trumbull Property 4.5 6.0 11.7 10.5 9.7 9.3 -- 16.3 -22.3 -7.5




Page 13 of 23

2017(%)

2016(%)

2015(%)

2014(%)

2013(%)

2012(%)

2011(%)

2010(%)

2009(%)

2008(%)

_

Carlyle Realty VIII -- -- -- -- -- -- -- -- -- --NCREIF-ODCE 7.6 8.8 15.0 12.5 13.9 10.9 16.0 16.4 -29.8 -10.0

Private Infrastructure 21.1 -0.6 0.1 -- -- -- -- -- -- --S&P Global Infrastructure Net TR USD 19.1 11.4 -12.2 12.1 14.0 10.9 -1.3 4.8 -- --

KKR Global II 21.4 -2.8 15.0 -- -- -- -- -- -- --

North Haven Infrastructure II 20.9 4.3 -- -- -- -- -- -- -- --S&P Global Infrastructure Net TR USD 19.1 11.4 -12.2 12.1 14.0 10.9 -1.3 4.8 -- --

Private Natural Resources 10.1 42.4 -- -- -- -- -- -- -- --S&P Global Natural Resources Index TR USD 22.7 32.3 -24.0 -9.7 1.5 7.2 -14.9 11.0 36.1 -38.3

EnCap XI -- -- -- -- -- -- -- -- -- --

EnCap IV -- -- -- -- -- -- -- -- -- --

GSO Energy Opportunities 13.0 35.4 -- -- -- -- -- -- -- --

Taurus Mining 2.0 48.2 -- -- -- -- -- -- -- --

Taurus Mining Annex 38.5 -- -- -- -- -- -- -- -- --S&P Global Natural Resources Index TR USD 22.7 32.3 -24.0 -9.7 1.5 7.2 -14.9 11.0 36.1 -38.3

Cash -- -- -- -- -- -- -- -- -- --

Cash 1.1 0.6 0.5 0.3 1.1 0.1 -36.6 -- -- --

Treasury Cash XXXXX




Page 14 of 23

Fiscal Year PerformanceFiscal2017

(%)

Fiscal2016

(%)

Fiscal2015

(%)

Fiscal2014

(%)

Fiscal2013

(%)

Fiscal2012

(%)

Fiscal2011

(%)

Fiscal2010

(%)

Fiscal2009

(%)

Fiscal2008

(%)_

Total Fund 12.4 0.1 3.0 17.3 11.9 -- -- -- -- --Policy Index 13.9 0.4 3.5 16.7 13.0 -- -- -- -- --

Total Fund w/o Alternatives 14.3 -0.8 2.3 17.9 13.3 -8.2 -- -- -- --Policy Index w/o AI 14.4 -0.9 1.8 16.5 11.7 -- -- -- -- --

US Equity 19.0 4.4 7.9 26.0 22.6 -17.5 -- -- -- --80% R1000 / 20% R2000 19.4 1.0 7.3 25.2 21.5 3.8 32.4 15.7 -26.6 -12.7

Mellon Dynamic US Equity 18.3 10.0 8.4 29.9 -- -- -- -- -- --S&P 500 17.9 4.0 7.4 24.6 20.6 5.4 30.7 14.4 -26.2 -13.1

Mellon Large Cap 18.0 -- -- -- -- -- -- -- -- --Russell 1000 18.0 2.9 7.4 25.4 21.2 4.4 31.9 15.2 -26.7 -12.4

DFA Small Cap 22.7 -3.5 6.9 -- -- -- -- -- -- --Russell 2000 24.6 -6.7 6.5 23.6 24.2 -2.1 37.4 21.5 -25.0 -16.2

PanAgora 19.9 -5.3 8.4 -- -- -- -- -- -- --Russell 2000 24.6 -6.7 6.5 23.6 24.2 -2.1 37.4 21.5 -25.0 -16.2

International Equity 20.2 -11.4 -4.5 22.3 16.3 -15.5 -- -- -- --International Equity Custom 22.2 -9.8 -4.8 22.3 16.4 -13.7 30.9 6.4 -31.0 -10.1

Copper Rock 14.4 -9.5 8.0 -- -- -- -- -- -- --MSCI World ex USA Small Cap 21.3 -3.3 -4.0 29.5 17.8 -15.7 37.0 15.7 -29.6 -17.4

EARNEST Partners

Mellon International 20.6 -- -- -- -- -- -- -- -- --MSCI EAFE 20.3 -10.2 -4.2 23.6 18.6 -13.8 30.4 5.9 -31.4 -10.6




Page 15 of 23

Fiscal2017

(%)

Fiscal2016

(%)

Fiscal2015

(%)

Fiscal2014

(%)

Fiscal2013

(%)

Fiscal2012

(%)

Fiscal2011

(%)

Fiscal2010

(%)

Fiscal2009

(%)

Fiscal2008

(%)_

Wells Capital 21.9 -5.3 -6.8 10.2 4.7 -- -- -- -- --MSCI Emerging Markets 23.7 -12.1 -5.1 14.3 2.9 -16.0 27.8 23.2 -28.1 4.6

US Fixed Income 2.6 4.3 1.5 5.8 1.7 8.0 -- -- -- --US Fixed Custom 2.2 4.7 1.5 5.4 -1.0 7.5 3.9 9.5 6.0 7.1

Barrow Hanley -0.3 6.3 1.8 4.3 -0.5 7.1 -- -- -- --BBgBarc US Aggregate TR -0.3 6.0 1.9 4.4 -0.7 7.5 3.9 9.5 6.0 7.1

Vanguard Short-Term Treasury Index Fund -- -- -- -- -- -- -- -- -- --BBgBarc US Govt 1-5 Yr TR -0.5 2.4 1.3 1.1 0.0 2.2 2.1 4.2 5.9 8.0

Hedge Fund 12.0 -7.9 6.9 -- -- -- -- -- -- --Hedge Fund Custom 6.7 -2.9 4.0 -- -- -- -- -- -- --

OZ Domestic II 15.1 -5.8 8.5 -- -- -- -- -- -- --

Graham Absolute Return -- -- -- -- -- -- -- -- -- --

Wellington-Archipelago -- -- -- -- -- -- -- -- -- --

KLS Diversified -- -- -- -- -- -- -- -- -- --

Winton -- -- -- -- -- -- -- -- -- --

Marshall Wace Eureka -- -- -- -- -- -- -- -- -- --

Silver Point Capital -- -- -- -- -- -- -- -- -- --HFRI Fund of Funds Composite Index 6.5 -5.4 4.0 7.6 7.3 -4.5 6.7 4.7 -15.2 -0.2

Private Equity 7.2 2.8 10.0 17.1 -2.8 7.7 21.1 6.6 -14.4 22.3Thomson Reuters Cambridge Private Equity Index 21.1 2.7 15.4 25.6 35.7 10.7 -- -- -- --




Page 16 of 23

Fiscal2017

(%)

Fiscal2016

(%)

Fiscal2015

(%)

Fiscal2014

(%)

Fiscal2013

(%)

Fiscal2012

(%)

Fiscal2011

(%)

Fiscal2010

(%)

Fiscal2009

(%)

Fiscal2008

(%)_

Adams Street 12.6 7.0 12.6 18.5 10.9 8.1 17.3 9.5 -21.9 8.6

Invesco IV 16.5 -1.4 16.4 29.8 -16.0 -- -- -- -- --

Invesco VI 9.3 6.1 69.4 -24.5 -- -- -- -- -- --

Ocean Avenue II 12.2 2.3 -5.3 -- -- -- -- -- -- --

Pantheon I 7.0 -0.9 3.2 18.7 5.0 2.6 -- -- -- --

Pantheon II 13.0 11.8 3.8 25.1 12.4 -- -- -- -- --

Pantheon Secondary 1.2 -2.7 0.0 9.0 -7.3 4.3 -- -- -- --

Raven Asset Fund II -15.8 0.1 -- -- -- -- -- -- -- --

Davidson Kempner Long-Term Distressed Opportunities Fund IV -- -- -- -- -- -- -- -- -- --

Real Assets 5.6 8.5 7.1 10.1 9.3 7.9 -- -- -- --Real Asset Custom -- -- -- -- -- -- -- -- -- --

SSgA -- -- -- -- -- -- -- -- -- --Real Asset Custom -- -- -- -- -- -- -- -- -- --

Private Real Estate 5.7 9.0 7.1 10.1 9.3 7.7 -- -- -- --NCREIF ODCE (net) 6.9 10.8 13.4 11.8 11.1 11.3 19.4 -6.8 -31.1 7.0

Greenfield Gap VII 11.1 19.0 -- -- -- -- -- -- -- --

Patron Capital V 0.5 -- -- -- -- -- -- -- -- --

UBS Trumbull Property 4.8 9.5 11.5 9.5 8.9 9.7 -- -1.4 -23.0 5.3




Page 17 of 23

Fiscal2017

(%)

Fiscal2016

(%)

Fiscal2015

(%)

Fiscal2014

(%)

Fiscal2013

(%)

Fiscal2012

(%)

Fiscal2011

(%)

Fiscal2010

(%)

Fiscal2009

(%)

Fiscal2008

(%)_

Carlyle Realty VIII -- -- -- -- -- -- -- -- -- --NCREIF-ODCE 7.9 11.8 14.4 12.7 12.2 12.4 20.5 -6.0 -30.5 8.0

Private Infrastructure 6.6 -6.0 -- -- -- -- -- -- -- --S&P Global Infrastructure Net TR USD 11.7 2.8 -5.9 29.4 8.7 -5.0 30.8 -- -- --

KKR Global II 11.6 6.8 -- -- -- -- -- -- -- --

North Haven Infrastructure II -1.3 -4.1 -- -- -- -- -- -- -- --S&P Global Infrastructure Net TR USD 11.7 2.8 -5.9 29.4 8.7 -5.0 30.8 -- -- --

Private Natural Resources 29.1 -- -- -- -- -- -- -- -- --S&P Global Natural Resources Index TR USD 15.3 -8.9 -17.6 21.7 -2.6 -18.3 38.4 1.2 -35.9 25.5

EnCap XI -- -- -- -- -- -- -- -- -- --

EnCap IV -- -- -- -- -- -- -- -- -- --

GSO Energy Opportunities 31.6 -- -- -- -- -- -- -- -- --

Taurus Mining 23.7 -- -- -- -- -- -- -- -- --

Taurus Mining Annex -- -- -- -- -- -- -- -- -- --S&P Global Natural Resources Index TR USD 15.3 -8.9 -17.6 21.7 -2.6 -18.3 38.4 1.2 -35.9 25.5

Cash -- -- -- -- -- -- -- -- -- --

Cash 0.7 0.5 0.5 0.9 0.2 -43.3 -- -- -- --

Treasury Cash XXXXX




Page 18 of 23

Annual Investment Expense AnalysisAs Of April 30, 2018

Name Fee Schedule Market Value Estimated Fee Value Estimated Fee

Total Fund w/o Alternatives $608,891,614US Equity $232,335,832

Mellon Dynamic US Equity 0.30% of Assets $73,866,512 $221,600 0.30%

Mellon Large Cap 0.04% of First 100.0 Mil,0.02% Thereafter $118,130,823 $43,626 0.04%

DFA Small Cap 0.35% of Assets $20,187,161 $70,655 0.35%PanAgora 0.80% of Assets $20,151,336 $161,211 0.80%

International Equity $206,242,178Copper Rock 0.85% of Assets $23,650,544 $201,030 0.85%EARNEST Partners $97,857Mellon International 0.04% of Assets $119,277,973 $47,711 0.04%

Wells Capital 0.90% of First 100.0 Mil,0.85% Thereafter $63,215,803 $568,942 0.90%

US Fixed Income $170,313,605

Barrow Hanley0.30% of First 50.0 Mil,0.20% of Next 100.0 Mil,0.15% Thereafter

$133,819,010 $317,638 0.24%

Vanguard Short-Term Treasury Index Fund 0.05% of Assets $36,494,594 $18,247 0.05%Hedge Fund $45,313,283

OZ Domestic II Performance-based 1.50 and 20.00 $17,573,955 $263,609 1.50%Graham Absolute Return Performance-based 1.75 and 20.00 $3,128,143 $67,183 2.15%Wellington-Archipelago Performance-based 1.00 and 20.00 $6,117,860 $61,179 1.00%KLS Diversified Performance-based 2.00 and 20.00 $5,046,988 $103,112 2.04%Winton Performance-based 0.90 and 20.00 $3,091,345 $37,467 1.21%Marshall Wace Eureka Performance-based 2.00 and 20.00 $3,144,977 $64,066 2.04%Silver Point Capital Performance-based 2.00 and 20.00 $7,210,016 $165,531 2.30%

Private Equity $34,066,381Adams Street $8,093,552




Page 19 of 23

Name Fee Schedule Market Value Estimated Fee Value Estimated Fee

Invesco IV $1,310,312Invesco VI $5,539,203Ocean Avenue II $7,839,612Pantheon I $1,181,206Pantheon II $3,813,457Pantheon Secondary $1,297,227Raven Asset Fund II $4,741,812Davidson Kempner Long-Term Distressed Opportunities Fund IV $250,000

Real Assets $112,149,837

SSgA0.30% of First 50.0 Mil,0.27% of Next 50.0 Mil,0.25% Thereafter

$32,288,990 $96,867 0.30%

Private Real Estate $59,698,686Greenfield Gap VII $13,044,345Patron Capital V $5,109,044UBS Trumbull Property $41,491,091Carlyle Realty VIII $54,206

Private Infrastructure $13,262,286KKR Global II $8,725,906North Haven Infrastructure II $4,536,380

Private Natural Resources $6,899,875EnCap XI $308,419EnCap IV $132,798GSO Energy Opportunities $3,441,860Taurus Mining $1,906,943Taurus Mining Annex $1,109,855

Cash $10,677,776Cash $6,486,389




Page 20 of 23



Excess Performance Vs. Benchmark

Benchmark3 Mo Perf vsBenchmark

(%)

1 Yr Perf vsBenchmark

(%)

3 Yrs Perf vsBenchmark

(%)


(%)_

Total Fund Policy Index -0.4 0.2 -0.6 -0.2Total Fund w/o Alternatives Policy Index w/o AI -0.5 0.3 0.1 0.5

US Equity 80% R1000 / 20% R2000 -0.6 0.7 1.1 1.2Mellon Dynamic US Equity S&P 500 -1.3 2.4 2.1 2.6Mellon Large Cap Russell 1000 0.0 -0.1 -- --DFA Small Cap Russell 2000 -0.7 -2.3 0.4 --PanAgora Russell 2000 1.5 0.8 0.2 --

International Equity International Equity Custom -0.1 -0.8 -1.0 -0.7Copper Rock MSCI World ex USA Small Cap -1.1 -0.4 -4.4 --EARNEST Partners -- -- -- --Mellon International MSCI EAFE 0.2 0.6 -- --Wells Capital MSCI Emerging Markets 0.2 -3.8 2.2 -0.1

US Fixed Income US Fixed Custom -0.1 -0.3 -0.1 0.0Barrow Hanley BBgBarc US Aggregate TR 0.1 0.0 0.0 -0.1Vanguard Short-Term Treasury Index Fund BBgBarc US Govt 1-5 Yr TR -- -- -- --

Hedge Fund Hedge Fund Custom 0.6 4.5 0.6 --OZ Domestic II -- -- -- --Graham Absolute Return -- -- -- --Wellington-Archipelago -- -- -- --KLS Diversified -- -- -- --Winton -- -- -- --Marshall Wace Eureka -- -- -- --Silver Point Capital HFRI Fund of Funds Composite Index 3.5 -- -- --


Page 21 of 23




(%)


(%)


(%)


(%)_

Private Equity Thomson Reuters Cambridge PrivateEquity Index 0.0 -1.6 -5.3 -6.0

Adams Street -- -- -- --Invesco IV -- -- -- --Invesco VI -- -- -- --Ocean Avenue II -- -- -- --Pantheon I -- -- -- --Pantheon II -- -- -- --Pantheon Secondary -- -- -- --Raven Asset Fund II -- -- -- --Davidson Kempner Long-Term Distressed Opportunities Fund IV -- -- -- --

Real Assets Real Asset Custom -0.2 -3.4 -- --SSgA Real Asset Custom -1.3 -3.4 -- --Private Real Estate NCREIF ODCE (net) -2.0 1.9 -1.2 -2.5

Greenfield Gap VII -- -- -- --Patron Capital V NCREIF CEVA 1Q Lag - NET -- -- -- --UBS Trumbull Property -- -- -- --Carlyle Realty VIII NCREIF-ODCE -2.2 -- -- --

Private Infrastructure S&P Global Infrastructure Net TR USD 4.5 11.8 0.9 --KKR Global II -- -- -- --North Haven Infrastructure II S&P Global Infrastructure Net TR USD 4.5 17.1 -- --

Private Natural Resources S&P Global Natural Resources IndexTR USD 2.3 -17.1 -- --

EnCap XI -- -- -- --EnCap IV -- -- -- --GSO Energy Opportunities -- -- -- --


Page 22 of 23




(%)


(%)


(%)


(%)_

Taurus Mining -- -- -- --

Taurus Mining Annex S&P Global Natural Resources Index TRUSD 1.9 -13.1 -- --

Cash -- -- -- --Cash -- -- -- --Treasury Cash -- -- -- --

XXXXX


Page 23 of 23

M E K E T A I N V E S T M E N T G R O U P

B O S T O N M A S S A C H U S E T T S

C H IC A G O I L L I N O I S

M I A M I F LO R I D A

P O R T L A N D O R E G O N

S A N D IE G O C A L I F O R N I A

LO N D O N U N I T E D K I N G D O M

www. m ek et a gr ou p . com


Asset Allocation Phase I Discussion

May 24, 2018

Confidentiality: This evaluation is prepared by Meketa Investment Group, Inc. for the exclusive use of the Merced County Employees’ Retirement Association. This evaluation is not to be used for any other purpose or by any parties other than it’s Board Members, employees, agents, attorneys, and/or consultants.

No other parties are authorized to review or utilize the information contained herein without expressed written consent.

Item 2


Confidentiality


The material contained in this report is confidential and may not be reproduced, disclosed, or distributed, in whole or in part, to any person or entity other than the intended recipient. The data are provided for informational purposes only, may not be complete, and cannot be relied upon for any purpose other than for discussion.

Meketa Investment Group has prepared this report on the basis of sources believed to be reliable. The data are based on matters as they are known as of the date of preparation of the report, and not as of any future date, and will not be updated or otherwise revised to reflect information that subsequently becomes available.

Page 2 of 110


Table of Contents


1. Executive Summary

2. Functional Asset Allocation Recategorization and Proposal

3. 2018 Asset Study and Capital Market Expectations

4. Traditional MVO Risk and Scenario Analysis

5. Factor Based Portfolio Analysis

6. Manager Summaries and Expectations

7. Disclaimer, Glossary, and Notes

Page 3 of 110

Executive Summary

Page 4 of 110


Executive Summary


The enclosed multi-staged review represents Phase I of our review of the asset allocation of MCERA.

The first section of this document shows a re-classification of the portfolio into “Functional” asset allocation categories, through which we believe the Board can focus on what types of characteristics managers within the portfolio have, and where exposure may be lacking. Meketa worked with Cliffwater to categorize each manager on the roster. We look forward to discussing this with the Board, and expect that there may be additional modifications based on feedback received at this meeting.

For the first phase of our portfolio allocation review, we walk through Meketa’s 2018 Capital Market expectations, which help put into context the range of returns that we believe are possible over the next 10 and 20 years.

Then, we look through the Mean Variance Optimization lense, to see what the “expected” return is for the MCERA portfolio over the next 20 years, as well as how the portfolio would be expected to perform under some historical and forward- looking market environments.

The next phase of our review will likely look new and different to the Board. We focus on evaluating the portfolio through a factor-based approach, with specific manager data evaluated (for public market managers only in this round, as we did not have sufficient Hedge Fund or Private Equity data to produce valuable insights). Our focus in this section was to look at left-tail risk (severe equity market downturn) specifically, and evaluate how your current managers might be expected to perform in that (and other) environments. This allows us to “optimize” a portfolio just based on that left-tail focus. We stress again that this is simply for your information at this meeting, and for discussion. We anticipate further refining this work for the June meeting, based on additional feedback and data.

The final component of this Phase I analysis is our subjective research perspective on each of the public market managers on your roster, as well as the “manager expectations” slide we discussed at the March meeting. We hope to review this with you, and, if you find it beneficial, it will become part of future quarterly reports.

Page 5 of 110

Functional Asset Allocation Recategorization

Page 6 of 110

Functional Asset Allocation Proposal



Functional Asset Allocation- Proposed Categories

Both Return and Volatility ranges based on long-term estimates and shown on per annum basis and are based on Meketa Investment Group's 20-yearCapital Market Assumptions. Note this does not aim to “target” how much of the portfolio should be in any one category, but rather to reflect the variouscharacteristics for future focus.

Category

Estimated Annual

Return Objective

(%)

Estimated Volatility

Objective

(std dev %) Description

Growth 5-10 15-25 Growth category is generally the riskiest of potential components and is used to augment long-term performance.

Growth assets tend to experience return swings in greater magnitude relative to the other categories. Often

represented by, but not limited to U.S. and Non U.S. Equities and Private Equity.

Inflation Sensitive 2-8 5-25 Assets that protect the investor from loss of purchasing power. Assets here tend to be less correlated with other

categories, but can be susceptible to significant volatility. Examples include TIPs, Commodities, Natural

Resources, Infrastructure and Real Estate.

Credit Sensitive 3-7 5-25 Debt related category that provides higher income opportunities. Category will largely be driven by credit risk and

tend to do better in risk-on environments, but will generally be less sensitive to the negative impact of rising interest

rates. High Yield Debt, Bank Loan, and EM Debt are natural fits here.

Diversifying Strategies 2-9 1-25 Asset class that provides greater level of diversification by exhibiting a tendency of limited correlation to traditional

assets. Hedge Funds, cash and certain Real Assets are examples for this category.

a. Downside 2-6 1-10 Strategies in this category employ techniques to provide positive returns when equity markets experience a sharp

decrease in value or retain value. The common objective being to avoid large losses. Examples include Market-

Neutral, Cash, Short-Term IG Bonds, and Portfolio Insurance.

b. Volatility 2-6 1-10 Assets in this class are designed to protect investors from higher levels of volatility. The strategy aims to outperform

when market volatility increases, but will lag in risk-on periods, or when volatility is low. Examples would be Risk

Parity, Low Vol, and VIX-oriented strategies.

c. Uncorrelated 4-9 10-25 This group mainly provides an additional level of diversification, without the explicit downside or volatility focus.

Long-short, Event-Driven, Global Macro, and TAA are some strategies that all into this category.

Interest Rate Sensitive 2-5 2-15 Assets that are more sensitive to changes in the interest rate environment, meaning greater return fluctuation

during periods of changing interest rates, relative to other assets. Intermediate-Term and Long-Term Investment

Grade Debt are key examples for this category.

Opportunistic 2-12 2-25 Category is effectively free from constraints or parameters and may be more tactical in nature. Policy target range

would be from 0% with an upper bound, predicated on market environments and opportunities.

Page 7 of 110

Merced Functional Asset Allocation

Meketa and Cliffwater ProposedManager Classifications

Page 8 of 110




Manager Categorizations

MANAGER TRADITIONAL Asset Class Category FUNCTIONAL Asset Class Category

BNY Mellon MCM Dynamic U.S. Equity Fund US EQUITY GROWTH

Mellon Large Cap EB DV Large Cap Stock Index US EQUITY GROWTH

DFA Small Cap US EQUITY GROWTH

PanAgora US Small Cap Core Stock Selector Strategy US EQUITY GROWTH

Copper Rock INTERNATIONAL EQUITY GROWTH

Mellon International EB DV International Stock Index Fund INTERNATIONAL EQUITY GROWTH

Wells Capital INTERNATIONAL EQUITY GROWTH

OZ Domestic II HEDGE FUNDS GROWTH

Wellington-Archipelago HEDGE FUNDS GROWTH

Adams Street PRIVATE EQUITY GROWTH

Invesco IV PRIVATE EQUITY GROWTH

Invesco VI PRIVATE EQUITY GROWTH

Ocean Avenue II PRIVATE EQUITY GROWTH

Pantheon I PRIVATE EQUITY GROWTH

Pantheon II PRIVATE EQUITY GROWTH

Pantheon Secondary PRIVATE EQUITY GROWTH

SSgA REAL ASSETS INFLATION SENSITIVE

Greenfield Gap VII PRIVATE REAL ESTATE INFLATION SENSITIVE

Patron Capital V PRIVATE REAL ESTATE INFLATION SENSITIVE

UBS Trumbull Property PRIVATE REAL ESTATE INFLATION SENSITIVE

Carlyle Realty VIII PRIVATE REAL ESTATE INFLATION SENSITIVE

KKR Global II PRIVATE INFRASTRUCTURE INFLATION SENSITIVE

North Haven Infrastructure II PRIVATE INFRASTRUCTURE INFLATION SENSITIVE

EnCap XI PRIVATE NATURAL RESOURCES INFLATION SENSITIVE

EnCap IV PRIVATE NATURAL RESOURCES INFLATION SENSITIVE

Page 9 of 110




Manager Categorizations (continued)

MANAGER TRADITIONAL Asset Class Category FUNCTIONAL Asset Class Category

Taurus Mining Annex PRIVATE NATURAL RESOURCES INFLATION SENSITIVE

Silver Point Capital HEDGE FUNDS CREDIT SENSITIVE

Raven Asset Fund II PRIVATE EQUITY CREDIT SENSITIVE

Davidson Kempner Long-Term Distressed Opportunities Fund PRIVATE EQUITY CREDIT SENSITIVE

GSO Energy Opportunities PRIVATE NATURAL RESOURCES CREDIT SENSITIVE

Graham Absolute Return HEDGE FUNDS DIVERSIFYING STRATEGIES: Sub: UNCORRELATED ASSETS

KLS Diversified HEDGE FUNDS DIVERSIFYING STRATEGIES: Sub: UNCORRELATED ASSETS

Winton HEDGE FUNDS DIVERSIFYING STRATEGIES: Sub: UNCORRELATED ASSETS

Marshall Wace Eureka HEDGE FUNDS DIVERSIFYING STRATEGIES: Sub: UNCORRELATED ASSETS

Treasury Cash CASH DIVERSIFYING STRATEGIES : Sub: DOWNSIDE PROTECTION

Cash CASH DIVERSIFYING STRATEGIES : Sub: DOWNSIDE PROTECTION

Vanguard Short-Term Treasury Index Fund US FIXED INCOME DIVERSIFYING STRATEGIES : Sub: DOWNSIDE PROTECTION

Barrow Hanley Core Fixed Income Fund US FIXED INCOME INTEREST RATE SENSITIVE

Page 10 of 110


Functional Asset Allocation Recategorization and Proposal


Functional Asset Class Category Breakdown by AUM As of March 31, 2018

Page 11 of 110

2018 Asset Study and Capital Market Expectations

Page 12 of 110


2018 Asset Study and Capital Market Exepectations


Each year, Meketa revises our asset class expectations via our Asset Study.

This involves setting long-term expectations for a variety of asset classes for:

Returns

Standard Deviation

Correlations

Our process relies on both quantitative and qualitative methodologies.

This document represents a selection of information and results from our 2018 Annual Asset Study.

The first step is to build our 10-year forecasts.

Our fundamental models are primarily valuation based.

Each model falls into one of eight groups, based on common factors:

Asset Class Category Major Factors

Equities Dividend Yield, GDP Growth, Valuation

Bonds Yield to Worst, Default Rate, Recovery Rate

Commodities Collateral Yield, Roll Yield, Inflation

Infrastructure Public IS Valuation, Income, Growth

Natural Resources Price per Acre, Income, Public Market Valuation

Real Estate Cap Rate, Yield, Growth

Private Equity EBITDA Multiple, Debt Multiple, Public VC Valuation

Hedge Funds and Other Leverage, Alternative Betas

Page 13 of 110




The next step is to move from 10-year to our 20-year forecasts:

We do this by combining our 10-year forecasts with the historical returns for each asset class.

How much we apply to each depends on our confidence in them (both the model & the data).

The 10-year model weighting varies between 50% and 100%.

It only hits 100% when there is a lack of good historical data.

We then infer a forecast of 10-year returns in ten years (i.e., years 11-20).

This allows us to test our assumptions with finance theory.

Essentially, we assume mean-reversion over the first ten years, then consistency with Capital Asset Pricing Model (“CAPM”) thereafter.

The final step is to make any adjustments:

The Investment Committee reviews the output and may make adjustments due to:

Quality of the underlying data

Confidence in the model

External inputs (e.g., perceived risks)

Page 14 of 110




Don’t forget about the other inputs; standard deviation and correlation.

Standard deviation:

We review the trailing 10-year standard deviation, as well as the trailing 10-year skewness.

Historical standard deviation serves as the base for our assumptions.

We increase or decrease the assumptions based on the size and sign of the historical skewness.

Asset Class Standard Deviation Skewness Assumption

Bank Loans 8.0% -1.9 10.0%

We look at performance during the Global Financial Crisis (“GFC”) to see if further changes are warranted (e.g., hedge funds).

We also adjust for private market asset classes with “smoothed” return streams.

Correlation:

We use trailing 10-year correlations as our guide.

Again, we make adjustments for performance during the GFC and “smoothed” return streams.

Most of our adjustments are conservative in nature (i.e., they increase the standard deviation and correlation).

Page 15 of 110




Asset Allocation Assumption Changes- 20 Year Return Expectations

2017 E(R)

(%) 2018 E(R)

(%) Change

(%) Comments

U.S. Equity 7.5 7.3 -0.2 Higher prices

International Developed Markets Equity 7.3 7.1 -0.2 Higher prices and higher growth expectations

Emerging Markets Equity 9.8 9.4 -0.4 Higher prices

Private Equity/Debt 9.2 8.9 -0.3 Higher prices and lower coupons/yields

Short-term Investment Grade Bonds 3.0 3.1 +0.1 Higher yield

Investment Grade Bonds 3.5 3.6 +0.1 Higher rates offset by tighter spreads

TIPS1 3.5 3.3 -0.2 Lower real rates

REITs1 6.5 6.8 +0.3 Lower prices

Natural Resources (Public)1 7.0 7.2 +0.2 Higher earnings expectations

Real Estate 6.9 6.7 -0.2 Lower cap rates & higher cost of debt

Infrastructure 7.4 7.2 -0.2 Higher prices

Natural Resources (Private) 8.4 8.8 +0.4 Higher earnings expectations

Commodities (naïve)1 4.5 4.6 +0.1 Higher inflation and cash expectations

Cash Equivalents 2.8 2.9 +0.1 Higher yield

Hedge Funds 5.3 5.2 -0.1 Lower equity & yields, partially offset by higher commodity expectations

Due to lower growth expectations and higher valuations, our expectations for global equities have decreased.

Due to higher inflation expectations, our forecasts for commodities have increased.

Forecasts for natural resources increased with higher earnings expectations.

Expectations for investment grade and short-term bonds were adjusted upward due to impacts from higher yields/rates.

1 Estimated from allocation breakdown of the SSgA Real Assets Fund.

Page 16 of 110




Asset Allocation Assumption Changes- 10 Year Return Expectations

2017 E(R)

(%) 2018 E(R)

(%) Change

(%) Comments

U.S. Equity 5.7 5.4 -0.3 Higher prices

International Developed Markets Equity 6.3 6.1 -0.2 Higher prices and higher growth expectations

Emerging Markets Equity 9.6 9.0 -0.6 Higher prices

Private Equity/Debt 8.8 8.2 -0.6 Higher prices and lower coupons/yields

Short-term Investment Grade Bonds 1.7 1.8 +0.1 Higher yield

Investment Grade Bonds 2.5 2.7 +0.2 Higher rates offset by tighter spreads

TIPS1 3.0 2.8 -0.2 Lower real rates

REITs1 6.0 6.2 +0.2 Lower prices

Natural Resources (Public)1 4.8 6.9 +2.1 Higher earnings expectations

Real Estate 5.7 5.7 0.0 Lower cap rates & higher cost of debt

Infrastructure 6.3 7.1 +0.8 Higher prices offset by higher inflation expectations

Natural Resources (Private) 7.9 8.6 +0.7 Higher earnings expectations

Commodities (naïve)1 4.4 5.4 +1.0 Higher inflation and cash expectations

Cash Equivalents 1.5 1.8 +0.3 Higher yield

Hedge Funds 3.8 4.1 +0.3 Lower equity & yields, offset by higher commodity expectations

Changes directionally aligned with 20 Year Return Expectations. Exceptions would be Hedge Funds with the strength of 10 Year Commodities expectations and Infrastructure with higher inflation expectations.

Due to lower growth expectations and higher valuations, our expectations for global equities have decreased.

Due to higher inflation expectations, our forecasts for commodities have increased.

Forecasts for natural resources increased with higher earnings expectations.

Expectations for investment grade and short-term bonds were adjusted upward due to impacts from higher yields/rates.


Page 17 of 110




Asset Allocation Projections- MCERA

MCERA Asset Allocation as of

March 31, 2018 (%)

U.S. Equity 28.6

International Developed Markets Equity 17.3

Emerging Markets Equity 7.9

Private Equity 4.3

Investment Grade Bonds 16.7

Short Investment Grade Bonds 4.5

Cash 1.5

Hedge Funds 5.6

Real Estate 7.3

Infrastructure 1.6

Natural Resources (Private) 0.8

Natural Resources (Public)1 1.3

Commodities1 0.8

REITs1 0.8

TIPS1 1.0

2018 Expected Return (20-year)/(10-year) 7.1 / 6.0

2018 Standard Deviation (20-year)/(10-year) 13.1 / 13.1

2018 Sharpe Ratio (20-year)/(10-year) 0.32 / 0.32


Page 18 of 110

Traditional MVO Risk and Scenario Analysis

Page 19 of 110




Types of Risk Analysis Addressed

MVO-based risk analytics

Includes worst-case return expectations and Value at Risk (VAR)1

Relies on assumptions underlying MVO

Scenario analysis

Stress tests policy portfolios using actual historical examples

Stress tests policy portfolios under specific hypothetical scenarios

1 VaR is a risk measure that estimates the maximum loss on a portfolio over a given time horizon and a given confidence level (usually 95% or 99%).

Page 20 of 110




MVO-Based Risk Analysis

Scenario: Current Policy

(%)

“Worst Case” Returns

One Year -19.2

Five Years -5.6

Ten Years -2.0

Twenty Years 0.6

Probability of Experiencing Negative Returns

One Year 28.6

Five Years 10.3

Ten Years 3.7

Twenty Years 0.6

Probability of Achieving 7.25% or More

One Year 49.5

Five Years 48.8

Ten Years 48.4

Twenty Years 47.7

Achieving the assumed rate of return remains challenging, with less than a 50% chance of achieving 7.25% over any of the time periods shown.

The likelihood of negative returns decreases significantly over longer time periods.

Page 21 of 110




Historical Scenario Analysis1 (Cumulative Return)

Scenario: Current Policy

(%)

Calendar Year 2008 -26.6

Global Financial Crisis (4Q07 thru 1Q09) -28.3

Interest Rate Spike (1994) 2.2

Crash of 1987 (September thru November 1987) -13.7

Popping of the dot.com Bubble (2Q00 thru 3Q02) -14.2

Strong U.S. Dollar (1Q81 through 3Q82) 3.1

Weak U.S. Dollar (January 1986 thru August 1987) 63.6

Stagflation (January thru March 1980) -4.7

Stagflation (1Q73 thru 3Q74) -21.8

A weak U.S. Dollar should be beneficial for the Fund’s current portfolio.

A scenario similar to the Global Financial Crisis would have a significant detrimental effect; note that it is “worse” than the “worst case” return on the prior slide, showing the limitations of MVO analysis.

1 See the Appendix for our scenario inputs. In periods where the ideal benchmark was not yet available we used the next closest benchmark(s) as a proxy.

Page 22 of 110




Stress Testing: Impact of Market Movements (Expected Return under Stressed Conditions)1

What happens if (over a 12-month period): Current Policy

(%)

10-Year T-Bond rates rise 100 bp 5.8



BBB Spreads widen by 50 bp, HY by 200 bp -1.1

BBB Spreads widen by 300 bp, HY by 1000 bp -21.5

Trade-weighted U.S.$ gains 10% -0.2

Trade-weighted U.S.$ gains 20% -2.4

Equities decline 10% -5.7



An extreme widening of high yield spreads would have a materially negative impact on the fund.

The other main risk factor would continue to be an equity market decline.

1 Assumes that assets not directly exposed to the factor are affected nonetheless. See the Appendix for further details.

Page 23 of 110

Scenario Inputs

Page 24 of 110




Scenario Return Inputs

Asset Class Benchmark Used

Investment Grade Bonds Barclays Aggregate

TIPS Barclays U.S. TIPS

Intermediate-term Government Bonds Ibbotson U.S. Intermediate Government

Long-term Government Bonds Barclays Long Term Treasury

EM Bonds JPM GBI-EM Global Diversified

Bank Loans CSFB Leveraged Loan

High Yield Bonds Barclays High Yield

Core Real Estate NCREIF Property

Value-Added RE NCREIF Townsend Value Added

Opportunistic RE NCREIF Townsend Opportunistic

REITs NAREIT Equity

Infrastructure (private) S&P Global Infrastructure

Natural Resources (private) S&P Global Natural Resources

Timber NCREIF Timberland

Commodities Summer Haven Commodity

U.S. Equity Russell 3000

Public Foreign Equity (Developed) MSCI EAFE

Public Foreign Equity (Emerging) MSCI Emerging Markets

Private Equity Venture Economics Private Equity Composite

Long-short Equity HFRI Equity Hedge

Global Macro HFRI Macro

Hedge Funds HFRI Fund of Funds Composite

Page 25 of 110




Scenario Return Inputs

GFC (%)

2008 (%)

Rate spike (1994)

(%)

LTCM (July-Aug 1998)

(%)

Crash of '87 (Sept-Nov 1987)

(%)

Popping of the TMT Bubble (2q00-3q02)

(%)

Strong dollar (1q81-3q82)

(%)

Plummeting Dollar (Jan 1986-Aug 87)

(%)

Stagflation (1q80)

(%)

Stagflation (1q73-3q74)

(%)

Investment Grade Bonds 8.5 5.2 -2.9 1.8 2.2 28.6 16.1 8.4 -8.7 2.8

Short-term Bonds 7.9 5.0 0.5 1.6 2.3 21.9 29.9 13.2 -2.6 8.1

TIPS 8.2 -2.4 0.2 0.7 3.3 37.4 20.5 17.0 -2.7 14.6

LT Govt Bonds 24.2 24.0 -7.6 4.1 2.6 35.5 28.4 15.4 -13.6 -1.8

EM Bonds (local) -7.9 -5.2 -10.3 -29.9 -9.1 6.3 -1.8 41.7 -7.3 -31.3

EM Bonds (major) -5.0 -9.7 -18.9 -28.2 -9.1 6.3 2.6 22.9 -7.3 -31.3

Foreign Bonds 2.0 4.4 5.3 3.5 2.3 8.5 27.3 24.5 -2.8 8.1

Bank Loans -23.7 -28.8 10.3 0.7 -3.6 6.3 7.1 14.3 -7.5 -19.9

High Yield Bonds -22.8 -26.2 -1.0 -5.0 -3.6 -6.3 7.1 14.3 -7.5 -19.9

Core Real Estate -23.9 -14.7 6.4 0.0 2.1 23.5 13.0 6.8 5.5 -16.1

Value-Added RE -52.5 -19.4 7.5 0.0 1.6 28.1 15.6 8.2 6.6 -19.3

Opportunistic RE -53.9 -36.4 9.4 0.0 1.7 31.3 17.2 9.0 7.3 -21.2

REITs -63.0 -37.7 3.2 -15.3 -14.0 45.4 5.6 16.2 -4.4 -31.6

Infrastructure -28.1 -23.4 -4.8 -2.0 -7.3 -3.1 17.2 23.2 -6.1 -24.5

Natural Resources -22.9 -23.0 3.9 -11.5 -11.5 -10.0 -10.0 48.8 2.9 -13.1

Timberland -1.2 5.6 15.4 0.0 11.9 -1.5 1.3 24.3 3.4 -1.6

Farmland 26.7 16.0 9.4 0.0 11.9 11.4 1.3 24.3 3.4 -1.6

Commodities -32.6 -33.7 11.6 -10.1 5.6 2.0 -24.4 7.3 -10.4 132.3

U.S. Equity -45.9 -37.3 0.2 -16.9 -29.8 -43.1 -1.9 31.5 -6.3 -42.6

Public EAFE Equity -52.1 -43.4 7.8 -11.5 -14.5 -46.7 -10.7 69.1 -7.0 -42.6

Public EM Equity -51.2 -53.3 7.8 -26.7 -14.5 -43.9 -10.7 69.1 -7.0 -42.6

Long-Short Equity -26.4 -26.6 2.6 -8.3 -17.9 -8.8 -1.2 18.9 -3.8 -25.6

Private Equity -22.1 -19.9 14.6 0.0 2.7 -15.1 -1.4 15.0 -4.4 -29.8

Global Macro/GTAA 7.4 4.8 -4.3 -3.5 -5.4 12.8 9.8 20.9 -4.8 11.1

Hedge Funds (FoF) -19.5 -21.4 -3.5 -7.7 -16.8 -0.4 -0.3 18.3 -5.4 -23.2

Hedge Funds -17.8 -19.0 4.1 -9.4 -15.8 -2.1 0.7 19.3 -4.4 -22.2

Cash 2.6 1.6 3.9 0.8 1.4 4.7 13.3 5.9 2.9 13.5

Gold (spot) 23.6 5.8 -1.9 -7.2 8.6 15.9 -32.7 38.7 -3.4 137.5

Page 26 of 110




Stress Test Return Assumptions1

Rates Rise 100 bp

(%)

Rates Rise 200 bp

(%)

Rates Rise 300 bp

(%)

BBB Spreads widen by

50 bp (%)

BBB Spreads widen by

300 bp (%)

USD Gains 10% (%)

USD Gains 20% (%)

Equities Decline

10% (%)

Equities Decline

25% (%)

Equities Decline

40% (%)

Rates Fall 100 bp

(%)

Rates Fall 200 bp

(%)

Public Domestic Equity 10.3 9.0 6.9 6.0 -42.0 3.5 7.0 -10.0 -25.0 -40.0 10.5 8.4

Public Foreign Equity (Developed) 10.3 9.0 6.9 5.5 -33.0 -7.0 -14.0 -10.5 -26.3 -42.0 10.5 8.4

Public Foreign Equity (Emerging) 10.3 9.0 6.9 5.0 -39.0 -7.0 -14.0 -11.0 -27.5 -44.0 10.5 8.4

Long-Short Hedge Funds 6.4 7.0 6.0 6.5 -21.0 2.1 4.2 -6.0 -15.0 -24.0 6.3 5.0

Private Equity 5.2 4.5 3.5 6.0 -42.0 3.5 7.0 -8.0 -20.0 -32.0 5.3 4.2

Core Real Estate 8.7 9.6 8.7 9.5 -12.0 4.0 8.0 -5.0 -12.5 -20.0 5.5 5.2

REITs 7.9 8.0 6.0 0.5 -36.0 1.0 2.0 -9.5 -23.8 -38.0 14.9 7.4

Non-Core Real Estate 7.1 10.4 9.3 11.5 -24.0 4.0 8.0 -7.0 -17.5 -28.0 3.6 7.6

Infrastructure (private) 4.3 2.6 2.9 3.5 -24.0 3.0 6.0 -5.0 -12.5 -20.0 5.3 5.5

Natural Resources (private) 8.6 12.2 13.5 2.0 -16.5 -3.1 -6.2 -5.0 -12.5 -20.0 2.5 2.0

Natural Resources (public) 11.4 16.2 18.0 4.0 -33.0 -6.2 -12.3 -9.5 -23.8 -38.0 5.0 4.0

Commodities 8.7 4.6 -0.6 -0.5 -21.0 -15.0 -30.0 -7.0 -17.5 -28.0 1.8 -4.8

Short-Term Bonds -6.4 -12.2 -17.9 8.0 6.0 7.0 14.0 1.0 2.5 4.0 5.1 10.9

Long-Term Government Bonds -15.3 -33.6 -52.0 12.0 15.0 10.0 20.0 5.0 12.5 20.0 21.6 40.0

TIPS -7.0 -15.8 -24.6 8.5 12.0 8.0 16.0 1.0 2.5 4.0 10.6 19.4

Investment Grade Bonds -3.4 -8.6 -13.9 -0.4 -4.6 8.0 16.0 2.0 5.0 8.0 7.2 12.5

Investment Grade Corporate Bonds -4.3 -11.4 -18.5 -1.4 -18.5 8.0 16.0 -1.5 -3.8 -6.0 9.9 17.0

Foreign Developed Bonds -5.1 -11.8 -18.5 0.0 -3.5 -6.3 -12.6 -2.0 -5.0 -8.0 8.4 15.2

Emerging Market Bonds (external) -2.0 -7.9 -13.9 -2.7 -25.9 5.0 10.0 -2.0 -5.0 -8.0 10.0 16.0

Emerging Market Bonds (local) -0.8 -6.6 -12.3 1.4 -8.0 -6.3 -12.6 -3.0 -7.5 -12.0 10.7 16.4

High Yield Bonds 1.5 -2.6 -6.7 -4.9 -35.9 4.5 9.0 -6.0 -15.0 -24.0 9.7 13.8

Bank Loans 5.0 6.0 7.5 2.5 -30.0 4.5 9.0 -6.0 -15.0 -24.0 3.0 2.0

Hedge Funds 5.8 6.2 3.6 3.5 -18.0 5.0 10.0 -5.0 -12.5 -20.0 8.1 4.4

TAA 7.8 5.7 3.1 6.5 -22.2 3.2 6.4 -7.0 -17.5 -28.0 10.8 11.8

Risk Parity 6.1 2.1 -2.5 5.6 -12.0 1.6 3.3 -2.0 -5.0 -8.0 10.2 12.3

1 Assumptions are based on performance for each asset class during historical periods that resembled these situations.

Page 27 of 110




Notes and Disclaimers

1 The returns shown in the Policy Options and Risk Analysis sections rely on estimates of expected return, standard deviation, and correlation developed by Meketa Investment Group. To the extent that actual return patterns to the asset classes differ from our expectations, the results in the table will be incorrect. However, our inputs represent our best unbiased estimates of these simple parameters.

2 The returns shown in the Policy Options and Risk Analysis sections use a lognormal distribution, which may or may not be an accurate representation of each asset classes’ future return distribution. To the extent that it is not accurate in whole or in part, the probabilities listed in the table will be incorrect. As an example, if some asset classes’ actual distributions are even more right-skewed than the lognormal distribution (i.e., more frequent low returns and less frequent high returns), then the probability of the portfolio hitting a given annual return will be lower than that stated in the table.

3 The standard deviation bars in the chart in the Risk Analysis section do not indicate the likelihood of a 1, 2, or 3 standard deviation event—they simply indicate the return we expect if such an event occurs. Since the likelihood of such an event is the same across allocations regardless of the underlying distribution, a relative comparison across policy choices remains valid.

Page 28 of 110

Factor Based Portfolio Analysis

Page 29 of 110




1. Introduction

2. Metrics and Tools

3. Manager Risk Analysis

4. Potential Solutions

Page 30 of 110

Introduction

Page 31 of 110




Manager Contribution To Risk – Especially in the Tail

∙ MCERA has requested a manager level analysis of contribution to risk, focused on left-tail(significant negative) events.

∙ Risk can come in many different forms and we highlight the following:

– Standard Deviation – “Normal Risk”

– Total Factor Risk – “Risk From Factors”

– Systemic Risk Exposure – “Tail-Risk”

– Diversification – “Concentration of Risk”

∙ Manager risk is broken out among 33 global economic and financial market risk factors.

– Other risks/factors/metrics/etc. can be added upon request, either qualitative or quantitative.

– By far, the U.S. Market factor is the most common/powerful risk factor among MCERA’smanager roster.

Page 32 of 110




Process, Metrics, and Tools

∙ Our process for this task is broken into three parts:

– Measure Manager Risks

– Examine current manager and broader universe risks in the context of the current portfoliorisks and preferences

– Begin evaluating solutions that better meet the desired objectives and preferences of thefund

This will be an iterative process and we are do not attempt to find the final portfoliosolution in this analysis.

More importantly, we establish a framework for future work.

∙ Our Metrics and tools

– A deep review of our metrics and tools are beyond the scope of this presentation

– We highlight high level intuition, but more details are available (two of the below were sentto the Board prior to the meeting):

High Dimension Optimization – A Meketa Whitepaper

Understanding Tail-Risk – A Meketa Whitepaper

Towards Maximum Diversification – By Tves Choueifaty and Yves Coignard

Page 33 of 110




Important Caveats and Suggested Next Steps

∙ We hope this presentation will lay a foundation for future work

– More important than the current potential solutions is that we highlight a process andframework to approach the problem

– As more data and information about preferences are attained, the process can evolve

∙ More and better data

– Most important in this process is the collection of more and better data

In this study we use benchmarks only for several asset classes (e.g. Hedge Funds,Private Markets)

The more detail about your specific preferences we can get the better we are able tocraft results

∙ Suggested next steps for June meeting

– Collect more detailed data from the Hedge Fund and Private Market programs (track recordbeyond hire date for individual managers)

– Add in discussions about liabilities and cash flows

– Discuss fund specific preferences in more detail

Page 34 of 110

Metrics and Tools

Page 35 of 110




A Brief Introduction To Our Metrics and Tools

∙ These metrics and tools serve two key purposes:

– Understanding a portfolio’s vulnerability to extreme losses and permanent capitalimpairment

– Finding the best possible trade-offs available to the fund

∙ Systemic Risk – A measure of ‘System-wide’ risk (lower is better!)

– Extreme losses come during periods when downside risk is not contained to one companyor sector but rather the entire system/market is at risk

This measure helps us understand when losses are more likely to change from ‘normal’to extreme (e.g. 2008 Financial Crisis)

∙ Total Factor Sensitivity (lower is better!)

– Uncertainty driven by all included factors adjusted for the correlation between factors (i.e.factor specific risk)

Page 36 of 110




A Brief Introduction To Our Metrics and Tools (continued)

∙ Diversification Ratio (higher is better!):

– This measure captures the gains from diversification

– For example, if a portfolio had a single investment then the diversification ratio would be 1

Any increase in the ratio represents a diversification gain

1.25 equals a 25% improvement over the single asset investment

– This measure can be applied to standard deviation or factor risk

∙ High Dimension Optimization (HDO)

– ‘To optimize’ simply means to find the best available trade-off

Optimization has been standard best practice for many years (1950s)

– In practice, traditional methods could not simultaneously include the complexities of the realworld

– HDO incorporates nearly unlimited trade-offs and is therefore a much morerealistic/practical tool

Page 37 of 110




Visualizing the Tail

∙ The assumptions made by most tools lead investors to think that returns fit in a normaldistribution.

– As a result, tail-risk events come as unexpected shocks.

– While these shocks can be both positive or negative, the negative shocks can have anoutsized effect on investment outcomes.

Financial Turbulence

Distribution of Daily U.S. Equity Returns Standard Deviation By Turbulence Regime

Page 38 of 110




Systemic Risk: Predicting Tail-Risk

∙ In our toolbox, systemic risk is used to predict how much turbulence is on the horizon andtherefore helps us understand how vulnerable a fund is to tail-risks.

– Reducing exposure to this type of risk reduces the probability of permanent capitalimpairment

Using Systemic Risk To Predict Turbulence

Page 39 of 110




Total Factor Sensitivity – Factor Driven Risk

∙ Factors of all types drive risk in different forms, but many are highly related (correlated).

– Total factor sensitivity measure this risk after adjusting for correlations between factors.

Page 40 of 110




Diversification Ratio – Risk Concentration

∙ When constructing an asset allocation it is best practice to spread risk across investments

– This ensures that if one investment goes bad for idiosyncratic reasons, the portfoliosurvives

∙ Below we have equal weighted portfolios with different numbers of assets

– In this simple example, we can see how spreading risk across more investments increasesdiversification

– In a real world example adding more assets may not provide any benefit

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

Div

ersi

fica

tio

n R

atio

Diversification Ratio Of Equal Weighted Portfolios

Page 41 of 110




A Very Brief History of Portfolio Optimization

∙ MVO is the foundation of all Modern Portfolio Theory (MPT).

∙ Since MVO’s creation many have added value on top of this foundational model.

∙ HDO does the same, but is designed to incorporate any and all.

Framework Return Goals Risk Goals Intuition

Additional information it attempts to incorporate

relative to MVO

MVO - 1952Expected

ReturnExpected Variance

Foundation of all below. Investors like return but dislike risking capital

NA

Mean-cVaR - 1999Expected

ReturnExpected Tail Risk

Focuses on extreme losses as these are of more

importance to investors

Asset behavior in extreme scenarios

MVTE - 1992Expected

ReturnExpected Variance +

Expected Tracking Error

Taking a position that Is different from the market

is a riskTrade-off of relative risk

BL - 1992Market Base Case

(CAPM) + confidence weighted investor views

Market Base Case (CAPM) + confidence

weighted investor views

Expressing a view that is different from the market

is a risk and should be systematically weighted

by confidence

Confidence weighted investor views

Foundation of Risk/Return Frameworks

Models that add/modify

informational value on top of the

foundation of MVO

Page 42 of 110




Where Does High Dimension Optimization Fit In?

∙ Modern Portfolio Theory in Formula(s):

∙ Looking at the formula for HDO we can see how it builds on existing foundations.

∙ HDO also incorporates insights from the other models of MPT.

– Tail-Risk:

This was the focus of Mean-cVaR and is controlled for within the Total FactorSensitivity portion of HDO by trading off or targeting Systemic Risk.

– Black-Litterman (BL) and confidence weighted expectations:

The insight of BL was about how to formulate forward looking returns.

This is addressed in the machine learning measurement process of HDO and the sameinsights of BL can be incorporated into HDO.

HDO

MVO

Return Variance Tracking Error

Variance

Total Factor

Sensitivity

Other Objectives

Can Be Added

�� = � − �� ∗ � − �� ∗ �� − �� ∗ ��…

MVTE

Page 43 of 110




What Does “High Dimension” Look Like?

∙ Risks and opportunities can come in many different forms and factors.

– To find the best trade-off in this complex landscape, each much be evaluatedsimultaneously.

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MCERA Manager Risk Analysis

Page 45 of 110




Manager Analysis Introduction- MCERA

Page 46 of 110




∙ We compare factor exposures on an ‘apples-to-apples’ by examining 1 SD events.

– In contrast, traditional approaches use beta which is less intuitive for cross comparison.

∙ The risk within this fund is driven nearly exclusively by the U.S. Market factor.

– The U.S. Market factor is the main risk factor in the majority of the manager roster.

Page 47 of 110




∙ Again, the risk within this fund is driven nearly exclusively by the U.S. Market factor.

– Although this is appropriate and expected for a Large Cap U.S. Equity Index fund.

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∙ In this DFA strategy, we can see that the Fama-French style market factors drive performance.

– This is in-line with how the fund constructs its portfolios and is expected.

∙ In particular, the size factor does have a strong effect on performance.

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∙ Again, we see the Fama-French factors driving most risk/performance.

– While not as strong as in the DFA example, again the size factor plays an important role.

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∙ In the Copper Rock strategy, we see some economic factors playing an important role.

– This is especially true for Growth and Currency.

∙ The U.S. Market factor remains strong but the size factor is basically zero.

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∙ In this international strategy, the strength of the U.S. dollar is a major headwind as expected.

∙ The U.S. Market factor is still the main risk factor, meaning even international index funds havesignificant correlation to the U.S. Equity market.

Page 52 of 110




∙ In this emerging market strategy we see that the Chinese Leading indicators and Commodities(especially metals) become important factors.

– This makes sense and is in line with expectations for this asset class.

∙ U.S. market factors remain an important driver as well and the U.S. dollar presents a headwindto the strategy.

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∙ Moving on to the fixed income strategies, it is no surprise that interest rates are now thedominant factor

– We can also see that the short-term duration of this index is helped by curve steepening

∙ Interest rates across the globe have been highly related over the performance history, which iswhy European and Japanese rates have been influential

– This is not likely a permanent part of the strategy and is more related to temporary demandfluctuations across global government bonds

– This highlights why correlations among factors are important considerations

Page 54 of 110




∙ Again, interest rates are the strongest risk factors.

– In this example, inflation also plays an important role as does the U.S. dollar.

Page 55 of 110




Systemic Risk Exposure By Manager/Program

∙ Above we see how major asset classes, and each manager/program is exposed to SystemicRisk, our main indicator for performance in ‘fat-tailed’, or significant negative marketenvironments. For programs where manager data was not available, we used policybenchmarks. Mellon’s dynamic strategy stands out in a positive way in this analysis.

∙ Holding all else equal, expected performance during a 2008 type scenario is approximately 10xthe Systemic Risk level. (e.g, if System Risk exposure above is -2.5%, then likely return is -25%)

– More details are on the next slide

Page 56 of 110




Alpha Expectations and Risk By Manager

∙ Using machine learning, we find the above expectations for alpha and active risk for eachmanager.

Page 57 of 110




Conclusions from Manager Risk Analysis

∙ The U.S. Market Factor is the major factor among the managers in the MCERA portfolio

∙ There is some redundant exposure in the manager roster.

∙ Systemic Risk is an important factor for tail risk, and very few current strategies would beexpected to “protect” against this type of risk.

∙ Factor based analysis is only as good as the data we have access to, and should be consideredalongside other ways to evaluate portfolios.

Page 58 of 110

Potential Portfolio “Solutions”

Page 59 of 110




Potential Solutions

∙ As previously mentioned, this analysis does not suggest ‘final’ solutions.

– The solutions that come out of the HDO process represent our current understanding ofyour preferences.

– This understanding will evolve and we hope our HDO framework aids this conversation andgives us more insight into the June presentation.

∙ Initial goals.

– Provide a foundation for further analysis and common vocabulary/framework.

– Illustrate our current understanding of your preferences and provide space for furtherrefinement.

∙ Current analysis goals and preferences.

– Reduce ‘tail-risk’ vulnerability.

– Improve efficiency of current manager investments.

– Opportunistically seek other improvements.

∙ Tackling the trade-offs.

– The HDO approach is focused on finding the best available trade-offs.

– Other than the stated preferences/goals above we trade-off risks/opportunities on a 1-to-1 basis.

– Further refinement of trade-offs will get us closer to a final recommendation.

Page 60 of 110




Potential Solutions Process

∙ For our initial process we perform three steps:

– Examine the current portfolio.

– Re-allocate among the current investments.

– Expand to other asset class categories

∙ Each portfolio reflects the same preferences/goals.

– Our initial review of the portfolio highlighted some implicit preferences.

Fees

Diversification

∙ Our HDO solutions reflect this same understanding.

∙ The “HDO Solution” in the following slides is just using the current portfolio to better optimizeoutcomes and reduce left tail risk.

∙ The “HDO Expanded Solution” takes into consideration adding some additional asset classexposures to further enhance outcomes.

∙ Again, these are informational at this stage in the process.

Page 61 of 110




Metrics By Portfolio – Initial Trade-offs

∙ These initial metrics that highlight the trade-offs approach.

– The Sharpe ratio and the Total Factor Sensitivity Ratio indicate how much the portfolio‘gets’ from each unit of risk.

∙ In short, HDO can get more from each factor.

Return/Total Factor Sensitivity

Return/Standard Deviation

Page 62 of 110




y = -2.0451x - 0.0331R² = 0.3017

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

Per

form

ance

Ove

r 200

8

Standard Deviation

Standard Deviation And Performance Over 2008

y = -2.82x + 0.0686R² = 0.5439

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

0% 5% 10% 15% 20% 25% 30% 35% 40%

Per

form

ance

Ove

r 200

8

Total Factor Sensitivity

Total Factor Sensitivity And Performance Over 2008

Components of a Fat-Tail

y = 9.5888x - 0.1706R² = 0.2943

-120.0%

-100.0%

-80.0%

-60.0%

-40.0%

-20.0%

0.0%

20.0%

40.0%

60.0%

80.0%

-10.0% -8.0% -6.0% -4.0% -2.0% 0.0% 2.0% 4.0%

Per

form

ance

Ove

r 200

8

Systemic RIsk Exposure

Systemic Risk And Performance Over 2008

∙ In these charts we see how each manager, as wellas major asset classes outside your Fundperformed during 2008.

∙ This performance is compared to three metricsthat help us understand expected performanceduring a crisis.

– Standard Deviation

– Total Factor Sensitivity

– Systemic Risk

Page 63 of 110




Metrics By Portfolio

∙ The key behind HDO’s insights is the ability to consider all metrics simultaneously.

– This is just a sample of the metrics we consider, these were chosen because of theirparticular interest to MCERA.

∙ Notice the three highlighted metrics, which all directly effect performance in a ‘2008-like’scenario.

Page 64 of 110




Weights By Portfolio/Manager

∙ In both HDO solutions Cooper Rock would be eliminated and Mellon Dynamic would increase.

– However, the weights are not widely different as a preference for not moving to far from theportfolio is expressed.

∙ In our expanded example, duration and tail-risk protection is added due to our understanding ofthis goal.

Page 65 of 110




Decomposition of Standard Deviation By Portfolio

∙ Most importantly the HDO solutions considerably reduce standard deviation.

∙ While the current portfolio is very well diversified it does not take into account what thecosts/benefits are for achieving this goal.

Page 66 of 110




Decomposition of Function Risk By Portfolio

∙ Both HDO solutions diversify away from the Function Growth bucket driving risk.

∙ Because the HDO expanded solution seeks to reduce tail-risk both the diversifying strategiesand interest rate sensitive strategies are increased.

Page 67 of 110




Factor Risk Type Decomposition

∙ The HDO solutions reduce total factor risk and among each sub-category.

∙ In particular the Systemic Risk (tail-risk) measure is greatly reduced.

∙ HDO is focused on getting the most from each type of risk exposure.

Page 68 of 110




Multiple Objective: Portfolio Comparison

∙ To make the most efficient use of tracking error, a portfolio should focus on using activemanagement in those areas where it can add the most value.

– The HDO approach is opportunistic in this area.

– The current manager roster has managers seeking alpha in much of the same area (Marketfactor).

By diversifying where managers seek to attain their alpha more potential exists at thetotal portfolio level.

∙ More broadly: fees, performance in future crisis, and net-value added as well as all otherincluded factors.

MetricCurrent Portfolio HDO Solution

HDO Solution Expanded

Expected Performance in a future crisis -34.5% -30.1% -22.7%

Tracking Error to U.S. 60/40 4.2% 3.3% 3.4%

Portfolio Level -- Manager Active Risk 0.8% 0.7% 1.5%

Alpha Expectation (gross) @ 25th percentile 70 bps 62 bps 113 bps

Total Fund Expense 64 bps 54 bps 58 bps

Expected Net Value-Add from Active Management @ 25th percentile +6 bps +8 bps +55 bps

Page 69 of 110




Conclusion

∙ Based on the analysis in this document, you can see that using HDO allows the Board toevaluate the portfolio with a focus toward downside risk protection.

∙ The previous slide indicates that there is room for improvement in terms of the portfolio’smanager roster and factor exposures.

∙ This review should be considered in concert with the traditional MVO analysis seen earlier in thepresentation, as well as a discussion of each manager on the MCERA roster.

∙ We would like to receive feedback from the board and further refine this analysis, in addition toevaluating some portfolio “alternatives” for the June meeting.

Page 70 of 110

Active Manager Summaries and Expectations

Page 71 of 110


Manager Summaries and Expectations


1. Dimensional Fund Advisors (DFA)

2. PanAgora

3. BNY Mellon

4. Copper Rock

5. Wells Capital

6. Barrow, Hanley, Mewhinney & Strauss

7. Active Manager Expectations

Page 72 of 110

Dimensional Fund Advisors (DFA)

Page 73 of 110




Dimensional Fund Advisors U.S. Small Cap Trust

Organization:

Dimensional Fund Advisors (DFA) is a very large, independent investment firm with $586 billion under management as of March 31, 2018. $25.2B is managed in the small cap strategy. DFA employees and their family members hold approximately 70% of the firm. The remaining 30% is held by a group of original individual investors who provided seed capital to start the firm.

DFA uses quantitative techniques in an enhanced indexing framework to manage portfolios. The firm offers a variety of products spanning the market cap spectrum. The majority of DFA’s assets are in the firm’s equity strategies, but DFA also manages fixed income, alternatives and multi-asset strategies.

Investment Team:

Three individuals at DFA manage the US Small Cap Core strategy. These individuals are Joseph Chi, Jed Fogdall, and Joel Schneider. Mr. Chi and Mr. Fogdall have been managing the strategy since 2012. Mr. Schneider was added in 2015.

Mr. Chi joined DFA in 2005 as a portfolio manager on the International Equity team. Prior to DFA, he worked as a securities and finance attorney specializing in venture capital, public offerings, and mergers and acquisitions. Mr. Chi earned a BS in electrical engineering and an MBA from UCLA, and a J.D. from the University of Southern California.

Mr. Fogdall joined DFA in 2004 as a portfolio manager on the International Equity team. He earned a BS in electrical engineering from Purdue University and an MBA from UCLA.

Page 74 of 110




Dimensional Fund Advisors U.S. Small Cap Trust (continued)

Investment Team (continued):

Mr. Schneider joined DFA as an Investment Associate in 2011 and was promoted to portfolio manager in 2013. Before joining DFA, he served as a management consultant at ZS Associates. He also worked in engineering and business development at Lockheed Martin. Mr. Schneider earned a BS in computer engineering from Iowa State University, an MS in industrial engineering from the University of Minnesota, and an MBA from the University of Chicago.

These individuals are all on the Investment Committee at DFA. This committee creates the policy and procedures implemented by the portfolios, and effectively drives the investment process.

Investment Philosophy:

DFA seeks to provide broad and diversified exposure to the equity markets in a more efficient way than the market capitalization-weighted approach typically favored by index providers.

DFA believes that markets are largely efficient. However, they think modest excess returns can be generated by building a portfolio based on equity return premiums. Assuming a sufficiently long time horizon, investors should be compensated for holding “riskier” stocks by realizing higher returns.

Their investment philosophy attempts to capture well-vetted sources of return (Fama-French factors) that have worked over long periods. Portfolio construction is based on systematic expected return premiums for market, company size, value, and profitability.

The portfolio is relatively sector neutral, with the exception of REITs that DFA will not own. This rule is based on the firm’s belief that REITs act more like real estate than stocks.

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Investment Process:

The initial investment universe is all U.S stocks. From this broad universe, DFA excludes REITs, recent IPOs, companies in extreme distress or bankruptcy, and companies in the midst of a merger or the target of an acquisition. DFA then sorts this eligible universe by market cap and targets companies in the smallest 10% of the market cap universe.

The next step in the process is to remove the least profitable and most expensive stocks from the investment universe. DFA ranks the eligible stocks by price to book ratio and profitability, and eliminates the most expensive and the least profitable. Profitability is defined as operating income before depreciation and amortization minus interest expense divided by book value. The stocks that are eliminated generally fall within the intersection of the bottom quartile of stocks by profitability and the highest quartile by valuation.

DFA utilizes a market cap weighting scheme to determine target weights for this list of eligible securities. Differences between the current portfolio and this target portfolio drive the suggested trade list.

To help ensure that the portfolios remain broadly diversified and fully invested, there are additional constraints on portfolio construction:

To reduce sector-specific risk, holdings in a single industry are generally limited to a maximum of 25%.

Individual security weights are based on free-float adjusted market capitalization and eligibility criteria. The current maximum security weight is 4% at time of purchase

Cash is kept at nominal levels with the goal of remaining fully invested.

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Investment Process (continued):

The PMs are responsible for the portfolio feedback phase. This stage is the qualitative component of the process where PMs monitor daily news and events, balance the benefits of continuous factor exposure with the associated costs, and take into account market frictions such as momentum and turnover. All buy and sell decisions consider expected daily premiums versus transaction costs.

Trading is a source of added value. DFA often provides liquidity in the market at a beneficial premium. Due to their unique process, DFA provides their clients with factor exposures (value, size, profitability) as opposed to targeting any specific stock. The firm is relatively indifferent as to which stocks they buy/sell, so long as their intended factor exposures are achieved. DFA’s traders also consider momentum when trading in and out of stocks. Portfolio managers will delay purchasing stocks if they are exhibiting negative momentum, and delay sales of securities that are exhibiting positive momentum.

To minimize single stock risk, the portfolios are extremely diversified. The small cap portfolio typically holds close to 2,000 stocks (1,950 currently). Annual portfolio turnover is relatively low, 14% during the last 12 months.

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Performance:

DFA’s Small Cap Core strategy is an enhanced index strategy that is broadly diversified and largely sector neutral. The portfolio has historically outperformed the benchmark by 50 to 150 bps, gross of fees, over the trailing multi-year periods with a tracking error of 2.0 to 2.7. This combination has resulted in attractive risk-adjusted returns.

1Q18 (%)

1 YR (%)

3 YR (%)

5 YR (%)

SI1 (%)

DFA Small Cap Core -1.4 9.4 8.6 12.2 10.4

Russell 2000 -0.1 11.8 8.4 11.5 9.8

Fees: MCERA Account

Investment Vehicle Commingled Fund

Investment Vehicle Name DFA Small Cap Core

Liquidity Daily

Annual Fee Schedule 0.35% on all assets

Effective Annual Fee $70,068

This fee is extremely low for the small cap asset class.

Research Team Recommendation: DFA Small Cap is a reasonable, low cost core strategy and should be retained. The portfolio offers the opportunity for modest excess returns with low tracking error resulting in attractive risk-adjusted returns. The portfolio should be expected to lag the index in growth- oriented markets.

1 Inception: June 1986.

Page 78 of 110

PanAgora

Page 79 of 110




PanAgora U.S. Small Cap Core Stock Selector

Organization:

PanAgora is a reasonably large firm with $54 billion under management as of December 31, 2017. Employees own 20% of the company. During the first quarter of 2018, the firm announced that Power Financial was buying Nippon Life's 15% stake, increasing Power Financial’s ownership to 80%.

The firm offers a wide variety of products spanning the market cap spectrum, including global products.

Investment Team:

George Mussalli is the Chief Investment Officer and Head of Research. He is responsible for oversight of several of the firm’s strategies including the U.S. Small Cap Core Stock Selector strategy. Prior to joining PanAgora in 2004, Mr. Mussalli was a portfolio manager at Putnam Investments on the Structured Equity Team. He started his career as a Senior Investment Analyst at John Hancock in 1995. He has a B.S. from Tufts University and an M.B.A. from the Sloan School of Management at MIT.

The U.S. Small Cap Core Stock Selector strategy investment team is composed of seven PMs/analysts who support Mr. Mussalli. In addition, the firm leverages the investment teams of the broader organization.


PanAgora believes that stock prices are driven by an underlying company’s business fundamentals, and that traditional quantitative managers do not dig deep enough to fully assess a company’s business. The firm believes that the best way to capitalize on investment opportunities that arise from mispriced fundamentals is by systematically evaluating a business using quantitative techniques.

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PanAgora U.S. Small Cap Core Stock Selector (continued)

Investment Process:

The Small Cap Core Stock Selector strategy is built upon a well-reasoned quantitative model that has produced attractive risk-adjusted returns over time.

Pan Agora utilizes “contextual alpha modeling” to create a unique model for every security. “Context” is defined as where the company is in its life cycle. They evaluate each company based on management capability, external forces, market intelligence, industry specific indicators, and event- driven signals that are uncorrelated with fundamental factors. The individual signals for each company are multiplied by the factor weights for those corresponding characteristics to arrive at a final alpha score.

The alpha scores for each stock and risk parameters are run through a proprietary optimization model that maximizes the model’s expected excess return for a given level of risk (defined by tracking error). The portfolio will typically hold the highest ranked companies subject to risk control and other liquidity constraints. Stocks that are overweighted tend to be highly ranked by the alpha model and/or exhibit diversifying characteristics relative to other stocks in the portfolio. These positions generally reflect high quality, well-run businesses that are focused on creating shareholder value at a reasonable price.

The final portfolio is very diversified with approximately 340 holdings.

Page 81 of 110




PanAgora U.S. Small Cap Core Stock Selector (continued)

Performance:

The Small Cap Core Stock Selector strategy is benchmarked to the Russell 2000 index. The table compares the strategy’s performance to that of the Russell 2000 index over several trailing periods. The strategy’s tracking error has historically been between 2.5% and 4.0% with gross excess returns of 200 to 300 bps, resulting in very attractive risk-adjusted returns.

1Q18 (%)

1 YR (%)

3 YR (%)

5 YR (%)

10 YR (%)

SI1 (%)

PanAgora Small Cap Core Stock Selector 3.9 13.6 9.2 13.9 13.2 11.7

Russell 2000 -0.1 11.8 8.4 11.5 9.8 8.6

Fees: MCERA Account


Investment Vehicle Name U.S. Small Cap Core Stock Selector

Liquidity Bi-monthly (1 & 15)

Annual Fee Schedule 0.80% on first $100M 0.75% on next $100M

0.70% thereafter


PanAgora offers a separately managed account with the same fee schedule

Research Team Recommendation: PanAgora U.S. Small Cap Core Stock Selector is a reasonable quantitative strategy and should be retained. The portfolio offers the opportunity for attractive risk-adjusted returns. The product could potentially underperform in low quality rallies and at market inflection points. 1 Inception: April 1996.

Page 82 of 110

BNY Mellon

Page 83 of 110




BNY Mellon MCM Dynamic U.S. Equity Fund (DUSE)

Organization:

BNY Mellon is the parent company of BNY Mellon Asset Management North America (Mellon AMNA). MELLON AMNA was formed in 2018 to house three of the major boutiques at BNY Mellon. These include Mellon Capital, Standish and The Boston Company. BNY Mellon has fourteen independently operated investment boutiques. As of March 31, 2018 the firm manage $558.6 billion. The largest boutique is Mellon Capital with approximately $363.0 billion. DUSE is less than 0.5% of the total assets at Mellon Capital equaling $1.4 billion.

The new business is headquartered in Boston and led by Desmond Mac Intyre, formerly the CEO of U.S. Asset Management at BNY Mellon. Chief Investment Officers have been appointed from each investment boutique and include Dave Daglio from TBC for Active Equity; Jeff Zhang from MCM for Multi-Asset, Index, and Smart Beta; and Dave Leduc from Standish for Active Fixed Income.

Investment Team:

The team managing DUSE trace their roots back to the firm’s roots as a low cost passive index provider. The investment team for multi-asset investment strategies is led by CIO Jeff Zhang. Mr. Zhang oversees a team of 45 team members broken out across three sectors; Portfolio Management, Multi-Asset Research and Global Trading. The investment staff is supported by a team of seven Global Investment Strategists.

James Stavena is the senior portfolio manager responsible for DUSE. Mr. Stavena is responsible for the implementation of several strategies at Mellon AMNA. These include global alpha, domestic, active currency, active commodity, and custom rules-based strategies. Mr. Stavena has 27 years of experience, 20 of those with Mellon AMNA, and predecessors. Mr. Stavena served as the portfolio manager for DUSE for 20 years.

Page 84 of 110




BNY Mellon MCM Dynamic U.S. Equity Fund (DUSE) (continued)

Investment Team (continued):

Key members of the team reside in the Multi-Asset Research team led by Anjun Zhou. Dr. Zhou is responsible for leadership across the research team at MELLON AMNA. Dr. Zhou joined the firm in 2010. She is supported by a team of 23. The team is responsible for the assumptions of expected return, volatility and correlation used in the investment process.


The philosophy, while consistent, has modulated as the dynamic in capital markets shifted. In the early life of the product, the firm and team understood that they did not want to compete with typical active managers and to facilitate an advantage they added the most diversifying asset to U.S. equites, long duration U.S. Treasuries. Through classic and well tested academic research, external and internal, to capture the benefits of diversification to achieve a higher long term outcome. As is typically the case in academic research the portfolio is leveraged to achieve the additional return over the benchmark.

A hallmark of the strategy is the goal of a beta of 1 to the S&P 500, key to the long term outlook of the higher expected returning asset and the downside qualities of the U.S. Treasury market.

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Investment Process:

The DUSE investment opportunity set is small with three instruments (basically) at their disposal: an S&P 500 index, Bloomberg Barclays U.S. Long Treasury Index, and cash. Each instrument is evaluated on stand along basis first and then combined based on the relative attractiveness of each instrument.

The S&P 500 evaluation is the most complex in that it seeks to understand what the combined equity risk premium is for all 500 of the securities in the S&P 500 and rolls that into an evaluation of the potential expected return of equities going forward. The premium is based upon a the relative attractiveness, or the higher or lower the risk premium of equities relative to the other assets, the greater the weight of equities in the portfolio. The research process uses street consensus on each security. This allows the team to inform their relative attractiveness of the forward-looking equity risk premium on a quarterly basis.

The Bloomberg Barclays Long Treasury index attempts to understand the steepness, or rate premium, one receives relative to equity securities. The effort evaluates the premium through the corporate lens versus the UST lens. This is an important distinction as it does introduce credit risk into the evaluation but they take no credit risk in the fund. The team believe that the consensus for corporate treasuries provides a lens into the relative premium in the U.S. Long Treasury.

Cash plays two roles. First in support of any derivative exposure in the portfolio and as a risk mitigant in times of severe stress.

The fund will dynamically allocate to equities when the risk premium is high and reduce when it is low. The equity portfolio can have exposure ranging from 50% to 150%. The average stock exposure was 98% from December 1989 to December 2017. The Treasury index will range between -50% and 100%. The average bond exposure was 41% since inception.

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Performance:

DUSE should be evaluated on two fronts. The first is the absolute level of return and second is the composition of their return. The performance analysis completed indicates the firm does a fantastic job executing their stated academic strategy. Additionally, the firm has facilitated strong downside protection versus equities.

Below we show the trailing period returns of the strategy versus three benchmarks. The first two represent the bulk of the universe they hold (S&P 500 and the Long U.S. Treasury). The second is a leveraged index comprised of 49% 2x S&P 500 and 51% Barclays Long-Term U.S. Treasury rebalanced on a quarterly basis.

December 1989 to March 2018 1Q18 (%)

1 YR (%)

3 YR (%)

5 YR (%)

10 YR (%)

SI1 (%)

Mellon Dynamic U.S. Equity -4.5 17.5 12.8 16.3 12.8 12.6

S&P 500 -3.0 14.0 10.8 13.3 9.5 10.5

Barclays Long Term U.S. Treasury Index -12.5 3.5 0.3 3.3 5.8 8.2

Custom Benchmark2 -9.5 15.9 11.1 15.1 13.8 15.0

Fees:

MCERA Account


Liquidity Daily

Annual Fee Schedule 0.30% on all assets


1 Inception: December 1989 2 Custom benchmark is for informational purposes only. The benchmark is composed of a leveraged S&P 500 return stream weighted at 49% and the Barclays Long Term U.S. Treasury (unleveraged) at 51%. Correlation and R-Squared of the

index versus the DUSE are statistically significant.

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Research Team Recommendation: The underlying financial theory of the investment strategy is a core of the fundamental underpinnings of finance. The execution is efficient and straightforward. Mellon has resisted adding in additional tools, a positive from our perspective. The low cost exposure and efficient implementation leads us to favor retaining this asset manager. We do have additional work to facilitate on the strategy, specifically an on-site to the San Francisco offices to gather additional information.

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Copper Rock

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Copper Rock International Small Cap

Organization:

Copper Rock Capital Partners is a Boston-based investment management firm. Old Mutual owns 65% of the firm and Copper Rock employees own the balance of the stock. When it was founded in 2005, the firm’s focus was on small cap equity investments across global, international, and emerging markets. Copper Rock has since expanded its product base by adding a global all cap equity strategy. Copper Rock manages approximately $6.1 billion across small cap equity strategies and one global all cap equity strategy as of March 2018. The International Small Cap strategy has $2.4 billion in assets.

Investment Team:

Copper Rock’s investment team consists of five portfolio managers, four analysts, and three traders. Stephen Dexter, the International Small Cap team’s lead portfolio manager, has managed international small cap equities since 2002, when this team was employed at Putnam Investments. Mr. Dexter has 35 years of investment experience and is supported by four additional portfolio managers: Denise Selden, David Shea, David Dineen, and Tim Codrington.

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Copper Rock International Small Cap (continued)


Copper Rock believes that small cap markets are inefficient and that a fundamental growth approach with a strong sell discipline provides the best opportunity to outperform in most market conditions. The team seeks to identify companies that are emerging leaders across various industries. Copper Rock attempts to buy these companies at an early point in their growth cycle, before widespread investor interest ultimately drives valuations higher.

Investment Process:

Copper Rock’s investment process begins with a quantitative, multi-factor model that focuses on three broad factor categories: earnings revision, valuation within sectors, and quality of earnings. The team also dedicates a significant amount of time to evaluating stocks that do not screen well, but may be candidates for inclusion in the portfolio based on idiosyncratic factors. Next, they perform a detailed analysis of each investment candidate to determine if it is truly a growth company (e.g., strong revenue/earnings growth, healthy industry), if the growth is sustainable (e.g., quality of management, competition) and if the growth is already priced into the market (i.e., valuation). The result is a broadly diversified portfolio of 80 to 120 companies with a considerable focus on portfolio construction and risk management. Portfolio turnover averages 75-100%.

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Performance:

As of March 2018, Copper Rock has underperformed the index over all trailing periods, with the exception of the since inception period, net of fees. Copper Rock’s objective is to achieve 2-3% of excess returns over a full market cycle, gross of fees, with tracking error of 3-6%. On average, Copper Rock has outperformed in 88% and 100% of rolling five- and ten-year periods by an average of 3.6% and 3.4%, gross of fees, respectively. However, weak absolute and benchmark-relative returns in calendar year 2016, when the strategy underperformed the index with returns of -7.2% versus 2.2%, has negatively impacted Copper Rock’s trailing period performance. In general, Copper Rock tends to outperform when growth and quality factors come into favor and underperform when deep value factors (such as the price/book value ratio) perform well. In 2016, cheap, low quality stocks drove market returns and Copper Rock’s relative underperformance.

1Q18 (%)

YTD (%)

1 YR (%)

3 YR (%)

5 YR (%)

10 YR (%)

SI1 (%)

Copper Rock International Small Cap -1.4 -1.4 21.9 6.9 9.2 5.9 11.5

MSCI EAFE Small Cap 0.2 0.2 23.5 12.3 11.1 6.5 10.5

1 Inception date is April 2002.

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Fees: Copper Rock


Investment Vehicle Name International Small Cap Fund

Liquidity Daily

Annual Fee Schedule1 0.85% on all assets


Copper Rock’s commingled fund fees rank at the 23rd percentile of the peer group for commingled funds and a mandate size of $32 million.

Recommendation: Copper Rock is an above average international small cap manager. The firm’s seasoned team of investment professionals, led by senior portfolio manager Steve Dexter, has managed the international small cap strategy for over 15 years at Copper Rock and previously Putnam Investments. Mr. Dexter and the team utilize a well-defined investment process that incorporates quantitative screening tools and bottom-up fundamental research to identify high conviction investment ideas. However, the portfolio’s returns have been disappointing recently and should be monitored closely. Despite recent performance, we believe that Copper Rock remains an attractive international small cap option and this manager should be retained.

1 Represents the management fee for the fund and does not include operating expenses.

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Wells Capital

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Wells Capital Berkeley Street Emerging Markets

Organization:

Wells Capital Management is a wholly-owned subsidiary of Wells Fargo & Company, a large publicly-traded bank (NYSE: WFC). The Wells Capital Berkeley Street Emerging Markets Equity Team joined Wells Capital through Well’s acquisition of Wachovia. Prior to that, the team was formerly with Evergreen Investments, an asset management subsidiary of Wachovia. Wells Capital Management manages roughly $378 billion in assets as of March 2018. The Berkeley Street team manages approximately $10.2 billion in assets across three emerging markets equity strategies. The Berkeley Street Emerging Markets Equity product is the flagship, with roughly $9.4 billion in AUM.

Investment Team:

The investment team is located in Boston, MA and is led by Jerry Zhang. Mr. Zhang has managed the strategy since September 2006, having previously served as an analyst on the team. Mr. Zhang is supported by two co-portfolio managers, Derrick Irwin and Richard Peck. Portfolio managers also have research analyst responsibilities. The three PMs are supported by six research analysts. Analyst coverage is divided on a regional and sector basis.

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Wells Capital Berkeley Street Emerging Markets (continued)


Wells seeks to invest in quality companies whose stock prices are trading below their estimate of intrinsic value. They define “quality” companies as businesses that can generate a return on capital greater than their cost of capital for an extended period of time. They believe these businesses can create value for shareholders via the reinvestment of retained earnings and/or dividend payouts. Characteristics which they believe are evidence of “quality” include a superior competitive position, financial strength, demonstrable profitability, shareholder friendly management, and favorable growth prospects supported by long-term trends.

Investment Process:

The Berkeley Street team executes a low turnover, bottom-up, quality-at-a-reasonable price investment approach. The process has two elements applied sequentially- Quality and Valuation. The team initially screens the universe for a defined list of quality criteria. The output of the screen is a “Quality Pool” of stocks, which are then subjected to fundamental analysis and valuation. Next, the team conducts a fundamental appraisal of the business to estimate its intrinsic value. The team will use various valuation techniques depending on the type of business. Stocks trading at a discount to intrinsic value are eligible for portfolio inclusion. Portfolios typically hold 90-130 stocks. Sector and country weights are constrained versus the benchmark. For benchmark weights greater than 15%, portfolio exposures are constrained to 50% to 150% of the benchmark. For benchmark weights between 5% and 15%, portfolio exposures are constrained to 0% to 300% of the benchmark. For benchmark weights between less than 5%, portfolio exposures are constrained to an absolute weight of 0% to 15%. Max position sizes are limited to an absolute of 5%.

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Performance:

Wells Capital Berkeley Street Emerging Markets Equity has outperformed the MSCI Emerging Markets index over the trailing three-, five-, and ten-year periods, by 1.7%, 0.2%, and 1.4%, respectively, net of fees. The strategy has outperformed the index since PM Jerry Zhang has taken over the fund by 1.5%. However, the product’s benchmark-relative risk (i.e. tracking error) is low, which is likely to place a relatively low ceiling (floor) on the strategy’s out-(under)performance going forward. At 3.9%, Wells’ tracking error ranks in the 71st percentile of the peer group. As a result, we do not expect this strategy to materially out-(under)perform going forward.

1Q18 (%)

YTD (%)

1 YR (%)

3 YR (%)

5 YR (%)

10 YR (%)

SI1 (%)

Wells Capital Berkeley Street Emerging Markets Equity 1.4 1.4 21.7 10.5 5.2 4.4 7.6

MSCI Emerging Markets 1.4 1.4 24.9 8.8 5.0 3.0 6.1

1 Inception date is September 2006, when PM Jerry Zhang became lead PM on the strategy.

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Fees:

Wells Capital


Investment Vehicle Name Emerging Markets Equity Fund

Liquidity Weekly

Annual Fee Schedule 0.90% on first $100 mil

0.85% thereafter


Wells’ fees rank in the second quartile of the peer group, at the 41st percentile. While relatively attractive versus the peer group, the product’s fees are high when compared to its relatively low expected value-added, given the portfolio’s low tracking error and Meketa’s performance analysis of the fund.

Recommendation: We believe that Wells Capital Berkeley Street Emerging Markets Equity is an average emerging markets equity manager. While an experienced team of investors manages the product, the portfolio’s low active risk and relatively high fees are likely to negatively affect the magnitude of outperformance going forward. We believe emerging markets equities is an inefficient asset class where high conviction active managers, who utilize greater amounts of active risk, can potentially achieve greater alpha for clients. Higher conviction emerging markets equity managers, with lower fees, are available. We believe Wells Capital is a replacement candidate on the Plan’s manager roster.

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Barrow, Hanley, Mewhinney & Strauss

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Barrow, Hanley, Mewhinney & Strauss

Organization:

Founded in 1979, Barrow, Hanley,Mewhinney & Strauss (BHMS) is a Dallas-based investment management firm with $86.7 billion in equity and fixed income assets under management as of March 31, 2018. The firm is 75% owned by Old Mutual with the remainder owned by employees. BHMS operates autonomously from its parent, however, in both business and investment operations.

John Williams, founder of the fixed income group at BHMS, retired in February 2018. He is succeeded by Mark Luchsinger and Scott McDonald as co-Heads of Fixed Income. The retirement was announced well in advance and the transition has been smooth.

Investment Team:

The fixed income team at BHMS comprises ten experienced investors who have been with the firm for many years. The team members average 14.5 years with the firm and 23 years of industry experience. The five portfolio managers and five analysts work closely on all strategies, with certain portfolio managers taking the lead for various strategies. David Hardin is the lead portfolio manager for the intermediate and short duration strategies.


The team’s investment philosophy is centered on value and bottom-up, fundamental research. The team seeks to create portfolios that offer a yield advantage versus the benchmark index. The team does not attempt to time the direction of interest rates, as they believe this creates an undue level of risk and volatility that is not consistently rewarded. All portfolios are managed in a team approach, with investment strategy decisions resulting from a consensus of all fixed income professionals.

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Barrow, Hanley, Mewhinney & Strauss (continued)

Investment Process:

The investment process begins with an analysis of the best potential return opportunities identified by the team’s relative return model. The model quantifies and ranks the total return potential of various sectors of the market over the following 12 months. Based on the model’s results, the team will focus on individual security selection decisions.

The team reviews the universe of corporate bonds, mortgage securities, and agencies for those that have yield premium versus the most recently issued Treasury of similar maturity. When bonds with a yield premium are identified the team then analyzes the factors impacting the yield premium. The team’s process seeks to translate the yield premium into a total return premium, regardless of the direction of interest rates.

Sector concentrations are a residual of the security selection process, with weightings varying over time in response to market dynamics. Portfolios are constructed to optimize yield to maturity by emphasizing those sectors and securities that are undervalued. The value philosophy prevalent on the equity side of BHMS’s business translates into fixed income security selection as well. Portfolios are diversified among bond sectors and along the yield curve with 40 to 50 securities.

Performance:

1Q18 (%)

1 YR (%)

3 YR (%)

5 YR (%)

SI1 (%)

BHMS Core Fixed Income2 -1.5 1.1 1.2 1.7 3.6

Bloomberg Barclays U.S. Aggregate -1.5 1.2 1.2 1.8 3.2

1 Inception: April 2010. 2 Performance is specific to the Merced County Employees’ Retirement Association and shown gross of fees as of March 31, 2018.

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Barrow, Hanley, Mewhinney & Strauss (continued)

Fees:

BHMS

Investment Vehicle Separately Managed Account

Investment Vehicle Name Core Fixed Income

Liquidity Daily

Annual Fee Schedule 0.30% on first $50mm 0.20% on next $100mm

0.15% thereafter


Effective Annual Fee on $135 million (%) 0.24%

Peer Group Median 0.24%

Peer Group Rank 45

Recommendation: BHMS is a reasonable core fixed income option, and we recommend retaining the strategy. The fixed income team at BHMS focuses on fundamental research and looks for value opportunities within various segments of the fixed income market. The team specializes in fundamental corporate credit research and as a result, BHMS tends to hold a strategic overweight to that sector. They utilize the equity team’s access to management and tend to be owners of both debt and equity of companies. BHMS focuses on high credit quality investments and runs relatively concentrated portfolios. The strategy is conservative and tends to hold up well in down markets, while also performing well in up markets. The recent leadership transition is not a cause for concern, given the advance notice.

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Active Manager Expectations

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Active Manager Expectations

Manager Strategy Description

Beta (High/Low/

Neutral)

Tracking Error Range

(bps) Environments Manager Underperforms

Domestic Equity

DFA U.S. Small Cap Trust Very diversified small cap portfolio that tilts toward value Neutral 2.0% to 2.7% In growth-oriented markets

PanAgora U.S. Small Cap Core Stock Selector Diversified, quantitatively managed small cap portfolio Neutral 2.5% to 4.0% In low quality rallies or at market inflection points

Mellon Capital MCM Dynamic U.S. Equity Very diversified, quantitative, large cap core portfolio Neutral (higher in more recent

periods)

2.5% to 5.0% When investors misprice forward looking return/risk characteristics; when returns are

concentrated in one sector

Developed Markets Equity (Non-U.S.)

Copper Rock International Small Cap Strategy Diversified, growth-at-a-reasonable price manager; combination of quantitative tools and fundamental

research

Low 4.0% to 5.0% At market inflection points, with abrupt leadership change. Deep value, low quality

market environments (e.g. 2009, 2016)

Emerging Markets Equity

Wells Capital Berkeley St Emerging Markets Diversified, quality-at-a-reasonable price manager Low 3.5% to 4.0% Deep value, low quality market environments

Investment Grade Bonds

Barrow Hanley Core Fixed Income Conservative, low tracking-error core strategy focused on bottom-up security selection

Neutral 0.2% to 0.7% At market Inflection points, but should track the index very closely

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Disclaimer, Glossary, and Notes

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Disclaimer


The material contained in this report is confidential and may not be reproduced, disclosed, or distributed, in whole or in part, to any person or entity other than the intended recipient. The data are provided for informational purposes only, may not be complete, and cannot be relied upon for any purpose other than for discussion.

Meketa Investment Group has prepared this report on the basis of sources believed to be reliable. The data are based on matters as they are known as of the date of preparation of the report, and not as of any future date, and will not be updated or otherwise revised to reflect information that subsequently becomes available.

In general, the valuation numbers presented in this report are prepared by the custodian bank for listed securities, and by the fund manager or appropriate General Partner in the case of unlisted securities. The data used in the market comparison sections of this report are sourced from various databases. These data are continuously updated and are subject to change.

This report does not contain all the information necessary to fully evaluate the potential risks of any of the investments described herein. Because of inherent uncertainties involved in the valuations of investments that are not publicly traded, any estimated fair values shown in this report may differ significantly from the values that would have been used had a ready market for the underlying securities existed, and the differences could be material. Note that for unlisted securities the valuations may be lagged by one or more calendar quarters, or may reflect original cost.

This document may contain certain forward-looking statements, forecasts, estimates, projections, and opinions (“Forward Statements”). No representation is made or will be made that any Forward Statements will be achieved or will prove to be correct. A number of factors, in addition to any risk factors stated in this material, could cause actual future results to vary materially from the Forward Statements. No representation is given that the assumptions disclosed in this document upon which Forward Statements may be based are reasonable. There can be no assurance that the investment strategy or objective of any fund or investment will be achieved, or that the Trust will receive a return of the amount invested.

In some cases, Meketa Investment Group assists the Trustees in handling capital calls or asset transfers among investment managers. In these cases, we do not make any representations as to the managers’ use of the funds, but do confirm that the capital called or transferred is within the amounts authorized by the Trustees.

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Glossary


Credit Risk: Refers to the risk that the issuer of a fixed income security may default (i.e., the issuer will be unable to make timely principal and/or interest payments on the security.)

Duration: Measure of the sensitivity of the price of a bond to a change in its yield to maturity. Duration summarizes, in a single number, the characteristics that cause bond prices to change in response to a change in interest rates. For example, the price of a bond with a duration of three years will rise by approximately 3% for each 1% decrease in its yield to maturity. Conversely, the price will decrease 3% for each 1% increase in the bond’s yield. Price changes for two different bonds can be compared using duration. A bond with a duration of six years will exhibit twice the percentage price change of a bond with a three-year duration. The actual calculation of a bond’s duration is somewhat complicated, but the idea behind the calculation is straightforward. The first step is to measure the time interval until receipt for each cash flow (coupon and principal payments) from a bond. The second step is to compute a weighted average of these time intervals. Each time interval is measured by the present value of that cash flow. This weighted average is the duration of the bond measured in years.

Information Ratio: This statistic is a measure of the consistency of a portfolio’s performance relative to a benchmark. It is calculated by subtracting the benchmark return from the portfolio return (excess return), and dividing the resulting excess return by the standard deviation (volatility) of this excess return. A positive information ratio indicates outperformance versus the benchmark, and the higher the information ratio, the more consistent the outperformance.

Jensen’s Alpha: A measure of the average return of a portfolio or investment in excess of what is predicted by its beta or “market” risk. Portfolio Return- [Risk Free Rate+Beta*(market return-Risk Free Rate)].

Market Capitalization: For a firm, market capitalization is the total market value of outstanding common stock. For a portfolio, market capitalization is the sum of the capitalization of each company weighted by the ratio of holdings in that company to total portfolio holdings; thus it is a weighted-average capitalization. Meketa Investment Group considers the largest 65% of the broad domestic equity market as large capitalization, the next 25% of the market as medium capitalization, and the smallest 10% of stocks as small capitalization.

Market Weighted: Stocks in many indices are weighted based on the total market capitalization of the issue. Thus, the individual returns of higher market-capitalization issues will more heavily influence an index’s return than the returns of the smaller market-capitalization issues in the index.

Maturity: The date on which a loan, bond, mortgage, or other debt/security becomes due and is to be paid off.

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Glossary


Prepayment Risk: The risk that prepayments will increase (homeowners will prepay all or part of their mortgage) when mortgage interest rates decline; hence, investors’ monies will be returned to them in a lower interest rate environment. Also, the risk that prepayments will slow down when mortgage interest rates rise; hence, investors will not have as much money as previously anticipated in a higher interest rate environment. A prepayment is any payment in excess of the scheduled mortgage payment.

Price-Book Value (P/B) Ratio: The current market price of a stock divided by its book value per share. Meketa Investment Group calculates P/B as the current price divided by Compustat's quarterly common equity. Common equity includes common stock, capital surplus, retained earnings, and treasury stock adjusted for both common and nonredeemable preferred stock. Similar to high P/E stocks, stocks with high P/B’s tend to be riskier investments.

Price-Earnings (P/E) Ratio: A stock’s market price divided by its current or estimated future earnings. Lower P/E ratios often characterize stocks in low growth or mature industries, stocks in groups that have fallen out of favor, or stocks of established blue chip companies with long records of stable earnings and regular dividends. Sometimes a company that has good fundamentals may be viewed unfavorably by the market if it is an industry that is temporarily out of favor. Or a business may have experienced financial problems causing investors to be skeptical about is future. Either of these situations would result in lower relative P/E ratios. Some stocks exhibit above-average sales and earnings growth or expectations for above average growth. Consequently, investors are willing to pay more for these companies’ earnings, which results in elevated P/E ratios. In other words, investors will pay more for shares of companies whose profits, in their opinion, are expected to increase faster than average. Because future events are in no way assured, high P/E stocks tend to be riskier and more volatile investments. Meketa Investment Group calculates P/E as the current price divided by the I/B/E/S consensus of twelve-month forecast earnings per share.

Quality Rating: The rank assigned a security by such rating services as Fitch, Moody’s, and Standard & Poor’s. The rating may be determined by such factors as (1) the likelihood of fulfillment of dividend, income, and principal payment of obligations; (2) the nature and provisions of the issue; and (3) the security’s relative position in the event of liquidation of the company. Bonds assigned the top four grades (AAA, AA, A, BBB) are considered investment grade because they are eligible bank investments as determined by the controller of the currency.

Sharpe Ratio: A commonly used measure of risk-adjusted return. It is calculated by subtracting the risk free return (usually three-month Treasury bill) from the portfolio return and dividing the resulting excess return by the portfolio’s total risk level (standard deviation). The result is a measure of return per unit of total risk taken. The higher the Sharpe ratio, the better the fund’s historical risk adjusted performance.

Page 108 of 110


Glossary


Standard Deviation: A measure of the total risk of an asset or a portfolio. Standard deviation measures the dispersion of a set of numbers around a central point (e.g., the average return). If the standard deviation is small, the distribution is concentrated within a narrow range of values. For a normal distribution, about two thirds of the observations will fall within one standard deviation of the mean, and 95% of the observations will fall within two standard deviations of the mean.

STIF Account: Short-term investment fund at a custodian bank that invests in cash-equivalent instruments. It is generally used to safely invest the excess cash held by portfolio managers.

Style: The description of the type of approach and strategy utilized by an investment manager to manage funds. For example, the style for equities is determined by portfolio characteristics such as price-to-book value, price-to-earnings ratio, and dividend yield. Equity styles include growth, value, and core.

Yield to Maturity: The yield, or return, provided by a bond to its maturity date; determined by a mathematical process, usually requiring the use of a “basis book.” For example, a 5% bond pays $5 a year interest on each $100 par value. To figure its current yield, divide $5 by $95—the market price of the bond—and you get 5.26%. Assume that the same bond is due to mature in five years. On the maturity date, the issuer is pledged to pay $100 for the bond that can be bought now for $95. In other words, the bond is selling at a discount of 5% below par value. To figure yield to maturity, a simple and approximate method is to divide 5% by the five years to maturity, which equals 1% pro rata yearly. Add that 1% to the 5.26% current yield, and the yield to maturity is roughly 6.26%.

5% (discount) =

1% pro rata, plus 5.26% (current yield)

= 6.26% (yield to maturity) 5 (yrs. to maturity)

Sources: Investment Terminology, International Foundation of Employee Benefit Plans, 1999. The Handbook of Fixed Income Securities, Fabozzi, Frank J., 1991.

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Notes


The Russell Indices®, TM, SM are trademarks/service marks of the Frank Russell Company.

Throughout this report, numbers may not sum due to rounding.

Returns for periods greater than one year are annualized throughout this report.

Values shown are in millions of dollars, unless noted otherwise.

Page 110 of 110

Merced County Employees’ Retirement Association (MCERA) RETIREMENT BOARD AGENDA ITEM

Page 1

DATE: May 24, 2018

TO: MCERA Board of Retirement FROM: Kristie Santos, Plan Administrator

SUBJECT: Reclassification of MCERA classifications; Retirement Benefit Specialist and Retirement Officer to Staff Services Analyst classification.

ITEM NUMBER: 3

ITEM TYPE: Action

STAFF RECOMMENDATION: 1. Staff are asking the MCERA Board of Retirement to support by adoption the

reclassification of Retirement Benefit Specialist and Retirement Officerclassifications to Staff Services Analyst classification.

DISCUSSION:

The job classification specifications of both Retirement Benefit Specialist and the Retirement Officer are in fact, very inaccurate and out of date. MCERA enlisted the assistance of County Human Resources (County HR) since the classifications are ‘owned’ by the County and any and all changes to the classification specifications must be vetted and approved by County HR, the County’s Chief Executive Officer and ultimately the County Board of Supervisors. County HR analyzed the current work in MCERA and what is needed in the future for the organization and are recommending that both classifications be reclassified to the Staff Services Analyst classification.

For budgetary purposes, MCERA added a ‘placeholder’ in the MCERA proposed budget for Fiscal Year 2018-2019 which was approved by the MCERA Board of Retirement on May 10, 2018.

Classification Hourly Monthly Annually Retirement Benefit Specialist I/II $19.32 to $25.91 $3,692.00 to $4,491.07 $44,304.00 to $53,892.80 Staff Services Analyst I/II* $22.81 to $34.43 $3,953.73 to $5,967.87 $47,444.80 to $71,614.40

Staff Recommendations 1. Staff are asking the MCERA Board of Retirement to support by adoption the

recommendation by Merced County Human Resources to reclassify theRetirement Benefit Specialist and Retirement Officer classification(s) used byMCERA to Staff Services Analyst classification.

Item 3

mcera investment retirement board agenda date: …

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