edelweiss style analysis esa -feb-20-edel

Edelweiss Style Analysis

1 Edelweiss Securities Limited

Feb 2020



CONTENTS

What’s Hot ......................................................................................................................... 2

ESA (Edelweiss style analysis)

Introduction ........................................................................................................................ 3

Factor model

Style Analysis

Various factor

Why Multi Factor Model………………… ......................................................................................... 4

Edelweiss Style Analysis ........................................................................................................ 5

Various Factor Groups........................................................................................................... 6

Momentum factor

Growth factor

Value factor

Quality factor

Conclusion......................................................................................................................... 10

Market Snapshot ................................................................................................................ 11

Edelweiss Multi Factor Models .............................................................................................. 12

Back-Test Results ............................................................................................................... 13

Edelweiss style analysis factor model

Appendix: ......................................................................................................................... 16

Assumptions ...................................................................................................................... 18



What’s Hot Edelweiss style analysis portfolio- ESA

Under prevailing market conditions, ESA factor model indicates the following three street

favorite factors for stock picking in February 2020. EPS Growth EBITDA Growth Sales Growth

We recommended an equal weighted value neutral portfolio.

Current month portfolio

Top 10 Long-portfolio stock Bottom 10 Short-portfolio stocks

Top 10 Long-Portfolio stocks Bottom 10 Short-Portfolio stocks

NSE Symbol Bloom Ticker NSE Symbol Bloom Ticker

ULTRACEMCO UTCEM ASHOKLEY AL

APOLLOHOSP APHS JSWSTEEL JSTL

NMDC NMDC HINDALCO HNDL

AUROPHARMA ARBP MOTHERSUMI MSS

DRREDDY DRRD MARUTI MSIL

LUPIN LPC BOSCHLTD BOS

SUNPHARMA SUNP INFRATEL BHIN

BRITANNIA BRIT TATAPOWER TPWR

TITAN TTAN HAVELLS HAVL

HCLTECH HCLT EICHERMOT EIM

Portfolio for current month (Feb-20):

Source: Edelweiss research

Previous month portfolio

Portfolio update for Jan 2020: ESA (Long-Short stocks) = 1.1%; NIFTY = (0.8)%



NSE Symbol Bloom Ticker Return NSE Symbol Bloom Ticker Return

BHARTIARTL BHARTI 8.3 AUROPHARMA ARBP -3.3

RELIANCE RIL -5.6 GAIL GAIL -3.6

NMDC NMDC -4.7 VEDL VEDL 7.5

NESTLEIND NEST 5.5 MARICO MRCO 1.5

BAJAJ-AUTO BJAUT -3.3 M&M MM -6.1

TATAGLOBAL TGBL 21.2 HEROMOTOCO HMCL -1.4

ASIANPAINT APNT -1.1 ACC ACC -5.2

SIEMENS SIEM -1.5 IOC IOCL 5.4

BPCL BPCL -2.8 EXIDEIND EXID -11.0

TECHM TECHM 2.5 ONGC ONGC 8.7

Total average return 1.9 Total average return -0.8

Portfolio for previous month (Jan-20):




Introduction

Factor model

Over the past few years, Indian capital markets have taken giant strides to enter the league

of global profitable investment avenues. This growth has attracted investors with diverse

investment strategies in quest for better returns. Along with this bustling growth, investment

strategies too are undergoing a paradigm shift. The conventional long only strategies are

gradually being sidelined by quantitative models with emphasis on long-short portfolios. Such

a drift is justified on the back of volatile nature of equity markets globally.

The Edelweiss Style Analysis (ESA) gives you cutting-edge research and an in-depth analysis

on ‘what’s hot’ in the current scenario. The analysis revolves round various factors driving the

market in different scenarios and tries to capture factors driving the current momentum. We

believe that markets follow a typical investment style or pattern at different intervals, which

is mirrored by certain factors. The analysis provides investors with an understanding of the

factors that are currently working in prevailing market conditions to enhance portfolio

performance. The analysis outlines a host of long–short portfolios drawn on the basis of these

factors.

The efficacy of the Factor Model is gauged by the performance of portfolios from various

dimensions:

Long Portfolio

Short Portfolio

Long–Short Portfolio

Long–Short Nifty

Nifty

Style analysis

Style analysis is basically a framework for measuring the efficacy of a select set of

fundamental and technical factors blended with certain quantitative disciplines. It tries to

encapsulate traditional investment styles of value and growth buying. Style analysis aims to

capture the factor momentum under prevailing market conditions to maximize the magnitude

and stability of expected incremental performance. However, due to changing market

dynamics, factors are bound to change from time to time. A specific factor riding the

momentum may change over a period of time.

Various factors

Style analysis makes use of a host of factors that aid momentum in a specific stock. P/E, EPS,

revenue, book value, EBITDA, enterprise value, P/BV, ROE, are few factors used for this

analysis. The above given factors often serve as an efficient evaluation tool. For simplicity

and better understanding, the factors are placed into different baskets as follows:

Momentum Factors

Growth Factors

Value Factors

Quality Factors



Why Multi Factor Model

Edelweiss multi factor model aims to diagnose right factor momentum to outperform the

benchmark by earning the alpha gains. Active investors like hedge funds, institutions, and

portfolio managers have been known to effectively profit from similar strategies. Considering

the volatility in equity markets, such an alternative investment can be effective in diversifying

the allocations and maximizing returns. Investment styles may be long portfolio, long

portfolio–short Nifty, or long–short portfolio.

Market in a bull run may augur well for a long portfolio style of investment, while in an

unstable market a long portfolio and short Nifty would be the preferred investment strategy.

In case of an uncertain market with negative bias, the long–short portfolio style of

investment is preferable. These strategies seem to be generating good returns on a

consistent basis and thus can be a preferred one during volatility where negative cues clearly

seem to outplay positive ones.



Edelweiss Style Analysis (ESA)

Introduction

Under dynamic market conditions, generation of Alpha returns is often the greatest challenge

confronting fund managers, which has underscored the increased importance of quantitative

stock picking. At the same time, for an active portfolio manager, a detailed understanding

of factor styles under changing market regimes is becoming increasingly important. Through

this research, we analyze the efficacy of fundamental and technical factors for stock selection

to complement the skill set of a portfolio manager.

Model description

The ability to predict the relative performance of various styles and successfully

implementing a strategy based on these predictions should have a positive effect on overall

investment returns. Indeed, as we have seen in our monitoring of investment performance

based on these styles over the years, being in appropriate groups (e.g., value, growth, and

market cap) can make an enormous difference to investment success. This study examines

the efficacy of over 20 superior factors and back-test the each factor portfolio returns since

January 2003.

Single factor portfolio construction methodology: To construct long-short factor portfolios,

we rank stocks within the coverage universe by each factor every month, and group them

into five quintiles (quintile 1 contains the highest ranked stocks). For each factor, we then

calculate the one month subsequent performance of these five equally weighted quintile

portfolios, and compute the performance difference between the highest and lowest quintiles

(Q1–Q5), to arrive at a factor return.

Factor groups: Over 20 factors derived from fundamental and technical data base were

grouped into four categories—growth, momentum, value, and quality factors. (Explained in

detail later).

Back-test results for each static factor portfolio: We report the cumulative return of

portfolios based on single factor since January 2003.



Various Factor Groups

Momentum factor

Momentum investing has taken the Indian stock market by a storm over the past couple of

years. The essence of this stock strategy is to buy winners and sell losers. Within the

momentum factor, it is worth noting that the duration of past performance (12-month/6-

months/3months) will influence the type of strategy that should be employed.

Longer look back and optimal holding period produce more reliable returns that are

sustainable over the long term. In this study, a 12-month and 6-month look back and a one-

month holding period appear to be optimal. This is consistent with our intuition that investors

under react to information over the medium term (3 months), thus justifying the 12-month

looks back as optimal. Momentum factors have shown greater dependence on market regime

change on time to time basis.

The graph below shows cumulative returns of single factor portfolio with the base of 100 in

January 2003.

Momentum factor (Top-bottom Quintile returns %)

0

100

200

300

400

500

600

Jan-0

3

Sep-0

3

May-0

4

Jan-0

5

Sep-0

5

May-0

6

Jan-0

7

Sep-0

7

May-0

8

Jan-0

9

Sep-0

9

May-1

0

Jan-1

1

Sep-1

1

May-1

2

Jan-1

3

Sep-1

3

May-1

4

Feb-1

5

Oct-

15

Jun-1

6

Feb-1

7

Oct-

17

Jun-1

8

Feb-1

9

Oct-

19

(Cum

ula

tive facto

r re

turn

s)

12 Month Price Momentum 6 Month Price Momentum

3 Month Price Momentum NIFTY Source: Edelweiss research



Growth factor

Growth style typically focuses on a company’s historical earnings growth to identify stocks

with the prospect of growing earnings at above-average rates versus the market. Managers

seek to invest in stocks of companies whose future earnings power has been underestimated

by markets. Growth is generally associated with greater upside potential, albeit with greater

risk on the downside.

From the growth factor chart below it is evident that none of the growth factors have given

incremental returns considering the fact that much of the weight has been given to price

momentum-driven factors all throughout the bull market period.

Since Jan 2010, EBITDA* growth has shown positive momentum, at the same time, EPS

growth has remained volatile, where as sales growth that has given negative returns till Jul-

10 started showing positive momentum post that.

Growth factor (Top-bottom Quintile returns %)

0

50

100

150

200

250

Jan/0

3

Jan/0

4

Jan/0

5

Jan/0

6

Jan/0

7

Jan/0

8

Jan/0

9

Jan/1

0

Jan/1

1

Jan/1

2

Jan/1

3

Jan/1

4

Jan/1

5

Jan/1

6

Jan/1

7

Jan/1

8

Jan/1

9

Jan/2

0

(Cum

ula

tive facto

r re

turn

s)

1 Year EPS Growth 1 Year Sales Growth

1 Year DPS Growth 1 Year BVPS Growth

1 Year EBITDA Growth 1 Year CFPS Growth Source: Edelweiss research * Please refer appendix on page no 16.



Value factor

The value factor style of investing has exhibited cyclical behavior over time, but predicting

inflection points in these cycles has been a challenge for investors.

Value factors typically focus on existing assets and valuation measures that equate a stock’s

price to the company’s intrinsic value. The premise for value managers is that the market has

incorrectly priced a stock in relation to the firm’s current assets and earnings and the

company will be revalued over time, thereby to generate value for investors. Value is

traditionally associated with more moderate upside and greater downside protection over

market cycles than growth.

Looking at the value factor chart, market cap to sales and price to cash flow are two leading

factors which have performed consistently whereas price to earnings is favored in recent

market turmoil, on the other side EV/EBITDA is the worst performer in the value factor pack

all throughout the sample period.

The value factor pack has shown varied patterns under changing market conditions. Till mid-

2005 a couple of value factors were effective and had given incremental returns, but in the

later bull market, they have lost momentum and got penalized. Although since Jan-09, one

can find good correlation of value factors with market returns.

Value factor (Top-bottom Quintile returns %)

0

100

200

300

400

500

600

700

800

Jan-0

3

Oct-

03

Jul-

04

Apr-

05

Jan-0

6

Oct-

06

Jul-

07

Apr-

08

Jan-0

9

Oct-

09

Jul-

10

Apr-

11

Jan-1

2

Oct-

12

Jul-

13

Apr-

14

Jan-1

5

Oct-

15

Jul-

16

Apr-

17

Jan-1

8

Oct-

18

Jul-

19

(Cum

ula

tive facto

r re

turn

s)

Price to Earnings Market Cap to Sales Price to Dividend

Price to Cashflow Price to Book value EV/FA

EV/EBITDA NIFTY Source: Edelweiss research



Quality factor

Besides the above conventional style factors, there are a couple of quality factors which have

shown a linear relationship with stock movement under different market conditions.

From the quality factor chart below it is evident that market cap (size) factor performance

during a market downturn was considerably higher than in other periods, indicating that at

every fall, large cap stocks are favored against small caps.

12-month price momentum/Beta is highly correlated to 12-month price momentum factor,

indicating that stocks with high return high Beta share the same characteristics as price

momentum factor in a bull market.

As witnessed globally during a bull market run, ROE does not play a significant role in

explaining the returns. Market players have ignored ROE throughout the Bull Run and as a

result it has remained flat with upward bias every time the market falls.

Quality factor (Top-bottom Quintile returns %)

060

120180240300360420480540600660720780840

Jan/0

3

Jan/0

4

Jan/0

5

Jan/0

6

Jan/0

7

Jan/0

8

Jan/0

9

Jan/1

0

Jan/1

1

Jan/1

2

Jan/1

3

Jan/1

4

Jan/1

5

Jan/1

6

Jan/1

7

Jan/1

8

Jan/1

9

Jan/2

0

(Cum

ula

tive facto

r re

turn

s)

ROE 12 Month Price Momentum / Beta Size NIFTY

`




Conclusion

In isolation, none of the factors outperform the broader index on consistent basis. Single

factor effectiveness can vary over time, depending on the prevailing market regime and no

single factor works consistently for every market condition. Fund managers can mitigate

challenges of timing style & sub-style cycles by engaging in active style management.

Discerning inflection points of style & sub-style cycles is difficult. Employing a more robust

mechanism to capture the prevailing style may help capture more returns.

Assessment of various styles & sub-style is necessary to better understand the implications of

equity allocations regardless of cycles, while increasing diversification.

Various permutations of styles can be explored to optimize the diversification and return

objectives of fund managers. Given the unpredictable nature and recent magnitude of style

cycles, fund managers may be better served by choosing multiple investments within a sub-

style category where characteristics and behavior together are complementary.



Market snapshot

In Jan 2020 for BSE200 (ex financials) universe 1Y Cash Flow Growth, Sales Growth and 12M

Price momentum have worked well.

Factor performance (Top-bottom Quintile returns %)

(20) (15) (10) (5) - 5 10 15 20

1 Year CFPS Growth

1 Year Sales Growth

12 Month Price Momentum


12 Month Price Momentum / Beta

1 Year BVPS Growth

1 Year EPS Growth

1 Year EBITDA Growth


1 Year DPS Growth

PEG

ROE

Market Cap to Sales

Price to Book value

Price to Earnings

EV/EBITDA

EV/FA

Price to Dividend

Price to Cashflow

Size

Benchmark Return

Last 6 Month Last 3 Month Last Month* Benchmark = NIFTY (for illustration only)


Note: (1) All factor performance (for past six months: Aug 2019 to Jan 2020)

(2) Sorted on the descending order of previous month’s top performing factor



Edelweiss Multi Factor Model

The multi factor model aims to catch the style momentum under prevailing market conditions

to maximize the magnitude and stability of expected incremental performance. We have

presented a robust mechanism for exploiting market anomalies via quantitative multi-factor

stock selection model. The approach aims to extract independent sources of alpha under

prevailing market regimes. By performing out-of-sample back-tests, we can exploit alpha

opportunities for long only and long-short (benchmark and stocks) portfolios. Our research

continues to make a strong case for this type of style-driven approach, complementing

valuation-based active stock picking strategies and empowering a more holistic approach to

the investment process.

To catch the factor momentum we have applied the information coefficient (IC) to decide the

relative importance of the factors in a given month. IC entails to depict how well a factor is

correlated with (subsequent) returns. It is the correlation coefficient between the factor rank

and the return rank for all companies in the universe for a specific period.

The Alternative and Derivative Research team at Edelweiss has implemented the Quantitative

Stock Selection Model, and will provide monthly stock signals (long only and long-short) and

style shift analysis reports.

Methodology

Diagnosing the top three factors with high IC and significant level of IC T-stats on monthly

basis and complementing the same with factor’s top-bottom quintile performance (as

discussed above).

The multi-factor rank equal weighted portfolios are constructed applying top three factors

with high IC.



Back-Test Results

Edelweiss style analysis factor model (ESA factor model):

We back-tested the style analysis on BSE-100 Universe for last nine years (Since 2000) and

as expected results looks promising considering the hypothesis is applied on smaller universe

of approximately 80 stocks ( excluding financials ).

Further to this, sincere effort has been made to back-test hypothesis for 2000-2002 bear

market phase and results are consistent with later bull market period to ensure that results

are not excessively influenced by recent Bull Run.

Please note hypothesis has been back-tested using the same factors applied in earlier ESA

factor model except 12-months momentum to Beta whereas PEG ratio has been included in

back-test from Jan-2003. Please note we have removed PEG ratio factor from live ESA model

effective Jan-2010.Stocks with trading volume less than INR 50cr (one month average) does

not form the part of the live portfolio.

ESA factor model versus benchmark observation

Long only and long-short (stock) rupee neutral portfolios have outpaced the benchmark

consistently, whereas the short portfolio has been consistently lagging the benchmark. As a

result, long-short (stock) spread is widening on a continuous basis, signifying EESA factor

model effectiveness.

Whereas long-short (NIFTY) rupee neutral portfolio has generated the similar returns as

compared to the benchmark with lesser volatility and high risk reward ratio (information

ratio).

The equity curve shows cumulative returns of a portfolio with a base of 100 since January

2000.



ESA factor model cumulative % return

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

Dec-1

0

Dec-1

1

Dec-1

2

Dec-1

3

Dec-1

4

Dec-1

5

Dec-1

6

Dec-1

7

Dec-1

8

Dec-1

9

Long Portfolio Short PortfolioLong Portfolio - Short NIFTY NIFTYESA


ESA factor model versus benchmark results

Long only and long-short (stocks) rupee neutral portfolios have outpaced the benchmark

consistently & has given average returns of ~34% and ~28% p.a. respectively Vs. NIFTY

returns of ~15% p.a.

The long portfolio, long stocks-short NIFTY and long–short stocks portfolio has maintained

the 80%,73% & 87% hit ratio respectively on Y-o-Y basis, where as 67%, 56%, and 60%

respectively, M-o-M basis.

So, combining the observations, we can summarize that the long only portfolio stocks have

been able to beat the index fairly consistently and long-short (benchmark & stocks) rupee

neutral portfolios have consistently generated the Alpha and have been able to provide

significantly better returns with acceptable annualized information ratio of 0.6, 0.8, and 1.2

respectively Vs. NIFTY information ratio of 0.4.

ESA factor model annualized performance (Y-o-Y)

(90)

(60)

(30)

-

30

60

90

120

150

180

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

(Retu

rn p

er

year

%)

Long Portfolio NIFTY Long Portfolio - Short NIFTY Short Portfolio ESA




ESA Annual return matrix (%)

PeriodLong

portfolioNIFTY

Long - Short

NIFTY

Short

portfolio

Long - Short

portfolio (ESA)

Jan00-Dec-00 1.5 (8.6) 10.5 57.8 64.3

Jan01-Dec-01 5.1 (7.8) 17.7 (9.4) 13.9

Jan02-Dec-02 41.0 4.2 32.7 (11.2) 29.1

Jan03-Dec-03 161.4 72.7 54.6 (37.1) 75.3

Jan04-Dec-04 32.1 5.3 24.6 (14.1) 18.2

Jan05-Dec-05 31.6 31.5 0.7 (23.7) 8.3

Jan06-Dec-06 44.4 36.3 4.8 (21.4) 18.2

Jan07-Dec-07 116.5 55.3 43.9 (22.8) 73.6

Jan08-Dec-08 (49.3) (52.5) 2.7 88.2 14.2

Jan09-Dec-09 113.2 78.3 24.9 (46.9) 23.1

Jan10-Dec-10 10.6 17.3 (6.2) 4.8 18.9Jan11-Dec-11 (23.9) (23.8) (0.4) 54.0 24.4

Jan12-Dec-12 9.8 15.3 (4.9) (10.4) 3.2Jan13-Dec-13 24.7 0.0 23.9 25.8 71.1

Jan14-Dec-14 1.9 9.6 (6.6) (3.1) (0.9)Jan15-Dec-15 7.0 (12.5) 22.3 34.5 45.6

Jan16-Dec-16 22.7 10.4 11.3 (17.6) 3.1Jan17-Dec-17 23.2 28.0 (3.8) (27.1) (9.1)Jan18-Dec-18 (8.4) 9.0 (15.9) 14.0 6.9

Jan19-Dec-19 (18.1) 1.7 (19.5) 8.3 (10.9)Jan20-Dec-20 1.9 (0.8) 2.6 (0.8) 1.1

Average return 26.1 12.8 10.5 2.0 23.4

Standard deviation 49.6 30.5 19.1 33.8 27.0

Reward/Risk 0.5 0.4 0.6 0.1 0.9

Annual return matrix (%)


Note: * Returns are calculated on cumulative basis; Nifty returns calculated only for the duration of trade

ESA Monthly return matrix (%)

PeriodLong

portfolioNIFTY

Long - Short

NIFTY

Short

portfolio

Long - Short

portfolio (ESA)

Average (%) 1.8 0.9 0.9 0.1 1.9

Best (%) 48.0 24.8 34.4 43.4 39.4

Worst (%) (24.5) (34.4) (11.1) (35.8) (28.1)

Standard deviation (%) 8.7 6.4 5.3 8.1 7.5

Hit Ratio (%) 63% 61% 53% 46% 59%

Monthly return matrix (%)


12-month rolling ESA Monthly return matrix (%)

PeriodLong

portfolioNIFTY

Long - Short

NIFTY

Short

portfolio

Long - Short

portfolio (ESA)

Feb-19 0.6 1.1 (0.5) 3.9 4.5Mar-19 0.8 2.8 (2.0) (5.0) (4.2)Apr-19 1.3 0.6 0.7 0.8 2.1May-19 0.9 (0.7) 1.6 2.1 3.0Jun-19 (2.4) (0.9) (1.5) 2.4 (0.0)Jul-19 (6.5) (2.2) (4.3) 2.3 (4.2)Aug-19 (0.3) (1.5) 1.2 4.6 4.3Sep-19 (0.9) (0.0) (0.9) (3.0) (3.9)Oct-19 (0.1) 2.6 (2.8) (2.4) (2.6)Nov-19 (4.0) 0.5 (4.5) (0.0) (4.0)Dec-19 (4.3) (0.9) (3.4) 0.3 (4.0)Jan-20 1.9 (0.8) 2.6 (0.8) 1.1

Monthly return matrix (%)




APPENDIX

Factor definitions

Momentum factors

12 month / 6 months / 3 month’s price performance: Indicates acceleration / deceleration in

a stock’s price performance. High reading is preferred.

Growth factors

Earnings per share (EPS) growth: Represents trailing 12-month EPS growth over the past two

corresponding years.

Sales growth: Represents trailing 12-month sales growth over the past two corresponding

years.

Dividend per share (DPS) growth: Growth in DPS indicates high earnings growth and/or

rising payout ratio over the past two reporting periods.

Operating cash flow per share (OCFPS) growth: Indicates high depreciation and/or high net

income and/or efficient working capital management over the past two reporting periods.

Book value per share (BVPS) growth: Book value represents the equity of the firm. Growth in

book value indicates and/or of high earnings growth and/or low payout ratio and/or lesser

conversion of convertibles or a low rights issue and/or equity issuance at greater than bps.

EBITDA growth: Represents trailing 12-month EBITDA growth over the past two

corresponding years. Growth in EBITDA indicates high top line growth flowing down to the

EBITDA line or EBITDA margin expansion, i.e., good operational performance.

Value factors

Enterprise value (EV) to EBITDA: The ratio is meant to give a proxy for value of net debt and

equity divided by EBITDA. A low reading is an indication of good value for debt and equity

holder but can also indicates low EBITDA growth prospects.

Price to book: A low ratio indicates good value, but can also be an indication of lackluster

growth and/or profitability prospects.

Price to earnings: A low ratio indicates good value but can also be an indication of lackluster

growth prospects. The ratio is widely used due to its simplicity.

Price to sales: A low ratio indicates good value but can also be an indication of lackluster

growth prospects and/or low margins currently or in the future.

Price to DPS: A low ratio indicates high earnings growth and/or rising payout ratio, but can

also indicate lackluster growth prospects.

Price to operating cash flow per share (OCFPS): A low ratio indicates efficient working capital

management and/or high depreciation and/or high net income.

Enterprise value (EV) to fixed assets (FA): The ratio is meant to give a proxy for value of net

debt and equity divided by fixed assets available to debt and equity holders. A low reading is

an indication of good value for debt and equity holding but can also indicate lower fixed

assets efficiency level.



Quality factors

Return on equity (ROE): Defined as net income divided by common equity. A high ratio

indicates good operational performance and/or financial efficiency, and a high return to

equity shareholder. It also reflects ability to utilize assets efficiently to generate earnings.

Size: Indicates market capitalization of a company. Market capitalization is calculated by

multiplying a company’s shares outstanding by the current market price.

12-month performance/Beta: A high value implies that the stock has performed better than

would have been expected given its beta level, therefore favorable reward/risk profile.

Price earnings to growth (PEG): PEG is a widely used indicator of a stock’s potential value. It

is favored by many over the price/earnings ratio because it also accounts for growth. Similar

to the P/E ratio, a lower PEG means that the stock is more undervalued.

Information coefficient

The information coefficient (IC) is a concise measure of how well a factor is correlated with

(subsequent) returns. It is the correlation coefficient between the factor rank and the return

rank for all companies in the universe for a specific period.

Information coefficient is calculated as:

In order to calculate the significance of IC we have applied

IC T-Stat = sqrt [(n-2)/ (1-r × r)] × r

Where

n = # companies in the universe

r = the correlation coefficient between the two arrays (the IC)



Assumptions

(I) Universe

ESA factor model: Strategy has been back-tested using BSE-100 universe.

Financial stocks have been excluded since many factors would have no clear meaning.

Companies with less than two year of financial history and one year trading history

have been excluded.

(II) Our database has been carefully developed to minimize look-forward bias: Many

researchers use databases which make simple or even simplistic assumptions about

when new information became available. Frequently, this means that their “out-of-

sample” results are actually contaminated by information that would not have been

available at the time. Obviously, investors should be skeptical about the results from

such work. We believe our database does not suffer from this problem.

(III) What’s hot: This approach is suitable for stock picking within a defined universe,

where stocks have sufficient dispersion both in factors and in returns. If all stocks in a

particular market tend to move up and down together, then obviously a factor-based

approach will generally be less effective, because there will be less opportunity to use

factor effectiveness to distinguish between the best and worst performers.

(IV) It is important to note that long-short portfolio returns do not account for transaction

costs including the bid-ask spread and the market impact of buying and selling.

(V) Hit ratio: signifies number of month’s portfolio has given positive returns out of total

sample period.

(VI) Portfolio neutrality: Long-short (stocks or NIFTY) portfolio has been constructed and

back-tested keeping in mind least possible sector exposure at any given point of time.).



NOTES



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