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India Equity Research Reports, IPO and Stock News
Visit http://indiaer.blogspot.com/ for complete details �� ��
Portfolio for current month ( March-12):
Top 10 Long-Portfolio stocks
Company Name
J S W Steel
Tata Steel
Sesa Goa
Reliance Infrastructure
Crompton Greaves
Bharat Heavy Electricals
Mahindra & Mahindra
Bharat Forge
Bajaj Auto
Reliance Industries
Bottom 10 Short-Portfolio stocks
Company Name
Reliance Power
D L F
Adani Power
Dr. Reddy'S Laboratories
Sun Pharmaceutical Inds.
Hindustan Unilever
I T C
Infosys
Sterlite Industries (India)
Idea Cellular
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 onemonth
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.
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 substyle
category where characteristics and behavior together are complementary.
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.
India Equity Research Reports, IPO and Stock News
Visit http://indiaer.blogspot.com/ for complete details �� ��
Portfolio for current month ( March-12):
Top 10 Long-Portfolio stocks
Company Name
J S W Steel
Tata Steel
Sesa Goa
Reliance Infrastructure
Crompton Greaves
Bharat Heavy Electricals
Mahindra & Mahindra
Bharat Forge
Bajaj Auto
Reliance Industries
Bottom 10 Short-Portfolio stocks
Company Name
Reliance Power
D L F
Adani Power
Dr. Reddy'S Laboratories
Sun Pharmaceutical Inds.
Hindustan Unilever
I T C
Infosys
Sterlite Industries (India)
Idea Cellular
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 onemonth
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.
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 substyle
category where characteristics and behavior together are complementary.
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.
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