MBA Student Investment Management Fund

Semi-Annual Presentation

December 2, 2016

2

Introductions

3

• Objective• Strategy• Implementation• Portfolio

• Construction• Snapshot• Composition

• Next Steps

Overview

4

Objective

• Maximize the learning experience.

• Practical application of academic theories.

• Comprehension of tradeoffs in implementing a quantitative strategy.

5

StrategyInvestment

Strategy

Quantitative

Profitability MomentumIdiosyncratic

RiskEvent-Based

Insider Trading

Dividend Yield

Fundamental

6

Strategy - Research

“Decoding Inside Information” – Journal of Finance 2010

Cohen, Malloy & Pomorski

• Types of inside trades• Routine: buying and selling in the same calendar month for past 3 years • Opportunistic: buying and selling in no discernable pattern

• Predicative power of opportunistic trades• Average monthly returns from a portfolio of opportunistic buys:

• Value-weighted: 72bp • Equal-weighted: 158bp

7

Strategy – Insider Trading

December 2013 December 2014 December 2015

X = Routine TraderY = Opportunistic Trader

Su M T W Th F Sa

X

Y

Su M T W Th F Sa

X

Su M T W Th F Sa

Y

X

Y

8

Implementation - Process

Phase 1: Establish

Routine/Opportunistic Trading Data Set

Phase 2: Weekly Dataset

Historical Data SQL

Weekly Data SQL

Opportunistic Trades

1114 Companies

UniverseScrape for

3 years

SEC Form 4

Data

1114 Companies

UniverseScrape for

1 week

SEC Form 4

Data

9

Implementation - Universe

Screening Criteria

Active U.S. Equities

Common Stock, No ETF’s, & No ADR’s

Remove Utilities & S&P 500

Market cap ≥ $1.2 Billion

30 Day Average Volume > 50,000

Number of Securities

16,931

1,114

10

Implementation - Technical

Scraper Code

EDGAR

Form 4

Output

Excel

11

Implementation - Technical

CSV Output

Python Code

12

Implementation - Technical

Database tools Challenges

• Lack of coding experience

• Internet interruptions

• Data quality• Footnotes• Compensation

based transactions

SQL Code

13

Implementation - Seeding

t-1 t-2 t-3 t-4 t-5

Avg 3 6 9 11 14

Stdev 2 3 5 6 7

Max 9 16 21 27 32

Min 1 2 3 4 5

Data Collection Time Period

#Opportunistic Trades by #Days Preceding Designated Trade Day

Day of Week Effect

Day Average

Monday 19%

Tuesday 13%

Wednesday 22%

Thursday 16%

Friday 27%

Saturday 2%

Sunday 1%

Portfolio Construction

5 Tranches with approximately 2.5% per position

14

Tranche Nov 21 Nov 28 Dec 5 Dec 12 Dec 19 Dec 26 Jan 2 Jan 9 Jan 16 Jan 23 Jan 30

1 7

2 4

3

4

5

1

2

Portfolio Snapshot

15

Company TickerInitial

Weight

Applied Industrial Technologies AIT 2.5%

Community Bank System, Inc. CBU 2.5%

Donaldson Company, Inc. DCI 2.5%

Innospec, Inc. IOSP 2.5%

Mercury General Corp. MCY 2.5%

Renasant Corp. RNST 2.5%

Square Inc. SQ 2.5%

Tranche 1 – November 21

Company TickerInitial

Weight

First Hawaiian Inc. FHB 2.5%

Avon Products Inc. AVP 2.5%

Canadian Pacific Railway

Limited

CP 2.5%

Healthcare Services Group,

Inc.

HCSG 2.5%

Tranche 2 – November 28

Portfolio Composition - Sector

16

*As of 28nd November

13%

3%

3%

19%

13%15%

17%

5%

7%

1%

4%

IWM1%

71%

2%

10%

10%

3% 3%

SIM Fund*

13%

8%

7%

15%

14%

11%

20%

3%4%

2% 3%

IWV

Cash IWV Consumer Discretionary Consumer StaplesEnergy Financials Health Care IndustrialsInformation Technology Materials Real Estate TelecommunicationsUtilities

Next Steps

1. Continue to seed portfolio

2. Rebalancing the portfolio

3. Holding & attribution analysis

4. Refine and improve trade signal

17

18

Appendix A

*Source: Decoding Inside Information

Table IV: Portfolio Returns to Routine and Opportunistic Trades

Appendix B: Portfolio Composition - Weight

19

*As of 28nd November

2%

47%51%

IWM1%

71%

5%

18%

5%

SIM Fund*

71%Cash IWV Large Mid Cap Small Cap

14%

76%

6%4%

IWV