Post on 26-Oct-2021
Edelweiss Style Analysis
1 Edelweiss Securities Limited
Feb 2020
Edelweiss Style Analysis
2 Edelweiss Securities Limited
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
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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)%
Top 10 Long-Portfolio stocks Bottom 10 Short-Portfolio stocks
Top 10 Long-Portfolio stocks Bottom 10 Short-Portfolio stocks
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):
Source: Edelweiss research
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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
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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.
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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.
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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
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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.
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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
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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
`
Source: Edelweiss research
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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.
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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
6 Month Price Momentum
12 Month Price Momentum / Beta
1 Year BVPS Growth
1 Year EPS Growth
1 Year EBITDA Growth
3 Month Price Momentum
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)
Source: Edelweiss research
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
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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.
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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.
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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
Source: Edelweiss research
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
Source: Edelweiss research
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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 (%)
Source: Edelweiss research
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 (%)
Source: Edelweiss research
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 (%)
Source: Edelweiss research
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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.
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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)
Edelweiss Style Analysis
19 Edelweiss Securities Limited
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.).
Edelweiss Style Analysis
20 Edelweiss Securities Limited
NOTES
Edelweiss Style Analysis
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