Managing Biases - Key To Unlocking Alpha

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Alternative Research 27th August 2012

Ayan Doss

[email protected]

+91 33 40099979

ForewordForewordForewordForeword

Stock picking is similar to life, both need decision making. Decision making is connected with psychology, skill, research method,

etc. Even the most skilled stock pickers know that there is a lot more to performance than simply picking stocks. Numerous

researches have shown that while humans are good at stock picking, they fail to achieve consistent return over and above the

benchmark index. Study by Andrew Clark, Chief Index Strategist, Thompson Reuters Indices, suggest that while most portfolio

managers do know how to pick stocks, their underperformance versus their benchmark can be attributed to portfolio

construction skills. The reason for poor portfolio construction skill is because of what Freud agreed: ‘we are ignorant of

ourselves’. In this report, we have tried to explain some of these human shortcomings regarding decision making and how to

overcome them.

Princeton professor and Nobel Laureate, Daniel Kahneman, in his book ‘Thinking, Fast and Slow’ has given an excellent overview

of the way we make decisions. Daniel put forth a very interesting and disturbing fact that most of the time our brain makes

decision that is easy and not necessarily what is right. To prove his point, he highlights experiments which shows that human

being suffers from many anomalies and the most prominent of those being ‘Cognitive biases’ and ‘the planning fallacy’.

Cognitive biases suggest that we're astonishingly susceptible to being influenced by features of our surroundings in ways we

don't suspect. Similarly planning fallacy highlights than when forecasting the outcome of risky projects, we overestimate positive

outcomes while overlooking potential for miscalculations. He further adds that another reason for our underperformance is

because human beings are ‘Risk Averse’ i.e., they do not maximize utility when confronted with choosing between uncertain but

profitable and certain but less profitable option.

Another reason for our inability to outperform on a consistent basis is because we are ‘incorrigibly inconsistent’ in making

summary judgment of complex information. When predictability is poor, which is the case always, inconsistency is destructive of

any predictive validity.

So, how do we outperform the market on a consistent basis? The answer is, through ‘Optimization’. Many psychological studies

show that under ‘low-validity’ environment (like evaluation of credit risk by banks, predicting the movement of stocks, longevity

of cancer patients, outcome of football game, etc), predictions based on some form of formula/algorithm proved to be more

accurate than intuitive predictions by experts.

Optimization helps us outperform as it does not suffers from any biases. However, there are researches that show that equal

weightage portfolio tend to outperform optimized portfolio which suggests that optimization adds no value in the absence of

informed inputs. However, Mark Kritzman, CFA, Sébastien Page, CFA, and David Turkington, CFA, in their paper ‘In Defense of

Optimization: The Fallacy of 1/N’ argues that with naive inputs (like expected return), optimized portfolios usually outperform

equally weighted portfolios. The ostensible superiority of the 1/N approach arises not from limitations in optimization but,

rather, from reliance on rolling short-term samples for estimating expected returns. This approach often yields implausible

expectations. By relying on longer-term samples for estimating expected returns or even naively contrived yet plausible

assumptions, optimized portfolios outperform equally weighted portfolios.

Hence, to overcome this shortcomings, while estimating stock return we have relied on Bloomberg estimates to avoid any kind of

biases and also to get an ‘Outside View’ and then used simple optimization technique to maximize utility and avoid inconsistency.

The final result was not disappointing. Our portfolio has outperformed the broader indices (Sensex and BSE 200) and generated

an average annual Alpha over BSE 200 of ~6% in last six years. Optimized portfolio tend to outperform the non-optimized

portfolio as it is i) more concentrated; ii) gives more weightage to non-correlated stocks and iii) at a given level of risk, it

maximizes the return of portfolio and not individual stock.

So if you’ve had extensive training in a predictable, rapid-feedback environment like chess, firefighting, anesthesiology — then

blink. In all other cases, think.

Deepak Tewary

[email protected]

+91 33 40099977


Managing Biases: Key To Unlocking Alpha

IndexIndexIndexIndex

ContentsContentsContentsContents PagePagePagePage

I. Introduction 1

Cognitive Biases 1Decision making in a risky outcome 1Optimistic Bias and the illusion of knowing 2The Planning Fallacy 2How to overcome our Biases 3Improving return through Optimization 3

II. Optimization Methodology 4

III. Back-testing 5

IV. Alpha 6

V. Omega 7

VI. Optimization – A Critique 9

VII.Current Portfolio 10Portfolio Fundamentals 11

VIII. Portfolio Stress Testing 12

IX. Portfolio VaR Analysis 13


““““As much money and life as you could As much money and life as you could As much money and life as you could As much money and life as you could want!want!want!want!

The two things most human beings would The two things most human beings would The two things most human beings would The two things most human beings would choose above all choose above all choose above all choose above all ––––

The trouble is, humans do have a knack of The trouble is, humans do have a knack of The trouble is, humans do have a knack of The trouble is, humans do have a knack of choosing precisely those things that are choosing precisely those things that are choosing precisely those things that are choosing precisely those things that are

worst for them.worst for them.worst for them.worst for them.””””

– J.K. Rowling, Harry Potter and the Sorcerer’s Stone

There are only two ways to beat the stock market in the long-

term, net of expenses: one, trade on superior information;

two, be lucky. In an efficient market, getting lucky has a much

higher probability of working than finding superior

information.

It’s widely known that most money managers underperform

the market; even with the deep and talented pool of analysts

they have access too. Research from Thompson Reuters Lipper

examining how successful actively managed mutual funds in

Europe have been in outperforming the indices over 20 years

period throws up some interesting facts which only reaffirm

market belief. This research concludes that typically 40% of

actively managed equity funds outperform their benchmark

indices in last 20 years. The proportion of funds that

outperformed varied from 26.7% in 2011, 40% over 3 years

and 34.9% over the past 10 years.

AVERAGE PROPORTION OF FUNDS' ROLLING

RETURNS OUTPERFORMING BENCHMARKS,

31/12/1991 TO 31/12/2011

1 yr

period

3 yr

period

10 yr

period

All Equity Funds 42.8% 41.4% 39.7%

Equity Asia Pacific Ex

Japan48.4% 48.9% 54.4%

Equity EmgMkt Global 38.5% 31.1% 24.6%

Equity Europe 37.7% 35.9% 27.0%

Equity Global 42.2% 38.4% 32.5%

Equity North America 36.2% 30.3% 20.8%

Equity UK 46.4% 47.6% 47.4%

Source: Lipper;Beating The Benchmark, March 2012

SoSoSoSo whywhywhywhy itititit isisisis sosososo hardhardhardhard totototo consistentlyconsistentlyconsistentlyconsistently beatbeatbeatbeatthethethethe market?market?market?market?

A logical answer to this question would be that top performing

fund attracts more capital and thus becomes less agile. In

addition, successful managers are disinclined to take new risky

bets that might tarnish their track records. Instead they often

start following their benchmark.

The above answer, though convincing, would be too simple to

explain the magnitude of underperformance. Princeton

professor and Nobel Laureate Daniel Kahneman helps explain

these underperformance in his new book, ‘Thinking, Fast and

Slow’.

CognitiveCognitiveCognitiveCognitive BiasesBiasesBiasesBiases

According to Kahneman, human mind is designed to find an

easy answer rather than right answer because of its ‘cognitive

biases’ - unconscious errors of reasoning that distort our

judgment of the world. Typical of these is the ‘anchoring

effect’: our tendency to be influenced by irrelevant numbers

that we happen to be exposed to. The human brain is

incapable of creating new information and it simply doesn’t

know what it doesn’t know. To compensate for the unknown,

our brains attempt to piece together the best possible story

based on what we do know. Sometimes this story is accurate

and sometimes not. When we’re right, we think it’s because

we’re smart, and when we’re wrong, we think it’s because we

didn’t have enough information and there was nothing we

could do about it.

ToToToTo furtherfurtherfurtherfurther understandunderstandunderstandunderstand thethethethe humanhumanhumanhumanunderperformance,underperformance,underperformance,underperformance, wewewewe needneedneedneed totototounderstandunderstandunderstandunderstand howhowhowhow decisiondecisiondecisiondecision isisisis mademademademade inininin aaaariskyriskyriskyrisky outcome?outcome?outcome?outcome?

Kahneman and his partner, Tversky, had showed that people

making decisions under uncertain conditions do not behave in

the way that economic models have traditionally assumed;

they do not ‘maximize utility’.

Decision under uncertain (read risky) circumstances, is

governed by the expected utility hypothesis, which states that

the decision maker (DM) chooses between risky or uncertain

prospects by comparing their expected utility values, i.e., the

weighted sums obtained by adding the utility (desirability of

money) values of outcomes multiplied by their respective

probabilities.

1


INTRODUCTION


ForForForFor exampleexampleexampleexample::::

A. 80% chance to win INR 3 mn

B. 50% chance to win INR 5mn.

Given the above choice, most people will choose option B as it

gives maximum utility. Now consider this:

A. 50% chance to win INR 5 mn

B. 100% chance to win INR 2 mn.

In this case also, most people will choose option B though it

has lower utility. Swiss scientist Daniel Bernoulli observed that

most people dislike risk (the chance of receiving the lowest

possible outcome), and if they are offered a choice between a

gamble and an outcome equal to its expected value, they will

pick the sure thing. In fact a ‘risk-averse’ person will choose a

sure thing that is less than expected value, in effect paying a

premium to avoid the uncertainty. The psychological value of

a gamble is therefore not the weighted average of its

possible financial outcome but the average of the utilities of

these outcomes, each weighted by its probability.

OptimisticOptimisticOptimisticOptimistic BiasBiasBiasBias andandandand TheTheTheThe illusionillusionillusionillusion ofofofofknowingknowingknowingknowing

According to Kahneman, the planning fallacy is one of the

manifestations of a pervasive ‘optimistic bias’ which may well

be the most significant of the cognitive biases. However, a bias

toward optimism is obviously bad, since it generates false

beliefs — like the belief that we are in control, and not the

playthings of luck. Kahneman explains that the very belief

that we know something that the market does not, is not a

belief but illusion.

ReasonReasonReasonReason forforforfor thisthisthisthis illusionillusionillusionillusion

Kahneman explains that the reason for this illusion is that the

person who acquires more knowledge becomes unrealistically

overconfident. Philip Tetlock, a psychologist at the University

of Pennsylvania in his book ‘Expert Political Judgment: How

Good It Is? How Can We Know?’ writes, experts are just

human in the end. They are dazzled by their own brilliance and

hate to be wrong. Experts are led astray not by what they

believe, but by how they think.

However, after all we are Darwinian survivors and Kahneman

adds that but without this ‘illusion of control’, would we even

be able to get out of bed in the morning?

Optimists are more psychologically resilient, have stronger

immune systems, and live longer on average than their more

reality-based counterparts. Moreover, as Kahneman notes,

exaggerated optimism serves to protect both individuals and

organizations from the paralyzing effects of another bias, ‘loss

aversion’: our tendency to fear losses more than we value

gains. It was exaggerated optimism that John Maynard Keynes

had in mind when he talked of the ‘animal spirits’ that drive

capitalism.

Decisions and Errors:The Planning Fallacy

When forecasting the outcome of risky

projects, we too easily fall victim of planning

fallacy. In its grip, we overestimate positive

outcomes while overlooking potential for

miscalculations.

So,So,So,So, howhowhowhow dodododo wewewewe overcomeovercomeovercomeovercome ourourourour Biases?Biases?Biases?Biases?

Many psychological studies show that under ‘low-validity’

environment (like evaluation of credit risk by banks, predicting

the movement of stocks, longevity of cancer patients,

outcome of football game, etc), predictions based on some

form of formula/algorithm proved to be more accurate than

intuitive predictions by experts.

WhyWhyWhyWhy isisisis forecastingforecastingforecastingforecasting inferiorinferiorinferiorinferior totototoalgorithm?algorithm?algorithm?algorithm?

According to Paul E. Meehl, one of the most versatile

psychologists of the twentieth century, forecasters think out

of box and consider complex combinations of features in

making their predictions. Complexity may work in the odd

cases, but more often than not it reduces validity.

Another reason for the inferiority of forecasters is that

humans are ‘incorrigibly inconsistent’ in making summary

judgment of complex information. A review of 41 separate

studies of reliability of judgment made by auditors,

pathologists, psychologists, organizational managers, and

other professional suggests that level of inconsistency is

typical, even when a case is revaluated within few minutes.

Unreliable judgments cannot be valid predictors of anything.

2


INTRODUCTION


Because we have little direct knowledge of what goes in our

mind, we will never know that we might have made a different

judgment or reached a different decision under very slightly

different circumstances. Formulas do not suffer from such

problems. Given the same input, they always return the same

answer. When predictability is poor, which is the case always,

inconsistency is destructive of any predictive validity.

We are not making a case for formulas/algorithms; even they

do not add value beyond a point. We know this from various

research that the best statistical algorithms, although more

accurate that human judgments, were never very accurate.

Robyn M. Dawes, an American psychologist who specialized in

the field of human judgment, in his famous article ‘The Robust

Beauty of Improper Linear Models in Decision Making’ opined

that the complex statistical algorithm (multiple regression) add

little or no value in decision making. More recent research

suggests that formulas that assign equal weightage to all the

predictors are often superior, because they are not affected by

accidents of sampling.

ImprovingImprovingImprovingImproving returnreturnreturnreturn throughthroughthroughthroughoptimizationoptimizationoptimizationoptimization

When investor preferences are complex, optimization has

repeatedly been shown to be performing better than

traditional portfolio choice models (‘IMPROVING

QUANTITATIVE MODELS THROUGH OPTIMIZATION’ BERNARD

TEW & RICHARD BERNSTEIN). Bernard & Bernstein observe

that individual security weights can often mean the

difference between outperforming or underperforming a

benchmark.

Andrew Clark, Chief Index Strategist, Thompson Reuters

Indices, published a white paper ‘SECURITIES SELECTION &

PORTFOLIO OPTIMIZATION: IS MONEY BEING LEFT ON THE

TABLE?’ where he concludes that portfolio managers do know

how to pick stocks. However, optimization techniques

increases return once the security selection is done.

On a final note, we agree with the finding of the paper that the

observed underperformance of fund managers versus their

benchmark can be attributed to portfolio construction skills

and not to stock-picking skills as so many have claimed.

Strangely, human beings are not as Strangely, human beings are not as Strangely, human beings are not as Strangely, human beings are not as emotionally disturbed by longemotionally disturbed by longemotionally disturbed by longemotionally disturbed by long----term risk term risk term risk term risk

as they are by short term risk.” as they are by short term risk.” as they are by short term risk.” as they are by short term risk.”

-William Bernstein

3

Managing Biases: Key To unlocking Alpha


INTRODUCTION


OPTIMIZATIONMETHODOLOGY

BSE-200 Stocks

BEst. Consensus Target

and Covariance Matrix

Mean Variance Optimization

with Constraints

Stocks and Weights

We begin our stock selection process by defining a universe consisting of stocks

included under the BSE-200 stocks. We believe that this set of stocks provide the

perfect balance of liquidity and outperformance potential (as it covers ~85% of

listed market cap). There on we calculated the expected return for each stocks on

the basis of Bloomberg Consensus Target Price. The covariance matrix among the

stocks was derived on the basis of the weekly returns of the last five years. We

then filtered our data by removing the stocks that did not have either the

Bloomberg Consensus Target Price or 5 years of price data. There after we fed the

data to a Mean Variance Optimization model in order to get the list of stocks to

invest and their corresponding weights in the portfolio. To summarize, the key

tenants of our model is given in the table below –

** Markowitz, H., 1952. Portfolio Selection. Journal of Finance, 7(1), p.77-91.

Sl.

No.Particulars Details

1 Expected ReturnOn the basis of Bloomberg Consensus Target

Price.

2. Covariance Matrix Five years of weekly return data.

3. Optimization Engine Markowitz Mean Variance Optimization.

4. Risk Free Rate 10 Yr Govt. Bond Yields.

5Optimization

Constraints

A. No Short Selling.

B. No Leverage.

C. Maximum allocation to a particular

scrip limited to 5%.

Mean Variance Optimization is a quantitative technique for allocating

investments within a portfolio. MVO does this by evaluating the trade-off

between risk and return and then choosing those combination of assets that

helps maximize the return while minimizing the risk. This concept was

developed by economist Harry M. Markowitz and is part of Modern Portfolio

Theory (MPT).

The MVO requires three data sets: expected return, standard deviation of

each stocks, and the correlation among them. These three data sets generate

a feasible set of asset combinations that Markowitz called the ‘efficient

frontier’. The efficient frontier consists of those combination of assets that as a

whole is expected to produce better returns given a level of risk, or, produce

lower risk given a level of return.

Combining the efficient frontier with the risk free rate of return generates the

Capital Market Line (CML) on the basis of which investors can choose the

assets to invest in and their corresponding weights.

A primer on Mean Variance Optimization (MVO)

4



BACKTESTING

In order to verify how our quantitative portfolio strategy performed we created a portfolio on the basis of the

aforementioned methodology and rebalanced it yearly. The chart below shows how our portfolio performed vis-à-

vis BSE 200 –

Particulars Portfolio Sensex BSE 200

Time Horizon July 3, 2006 to July 3, 2012

Return (Annualized) 18.1% 12.06 13.3%

Risk (Annualized) 29.6% 26.9% 32.4%

Beta 1.17 (vs. BSE-200) 0.97 (vs. BSE-200) 1

Source: VCK Research; Bloomberg

The summary statistic of the portfolio , BSE 200 and Sensex is given in the table below –

Source: VCK Research

5



BACKTESTING

ALPHA

Risk Adjusted Return Analysis

Particulars Portfolio Benchmark BSE-200

Sharpe Ratio 0.57 0.36

Jensen Alpha 6.69 0.00

Information Ration 0.36 0.00

Treynor Ratio 12.9 6.3



6



BACKTESTING

Introduction to Omega

The above mentioned performance matrices do not capture all the

characteristics of the portfolio performance. Most of the matrices

discussed above are not robust enough for measuring

performance, since they take into account only the two moments

of returns distribution namely, the mean and variance while

ignoring the other moments like skewness and kurtosis.

A measure, known as Omega, which employs all the information

contained within the returns series was introduced by Keating &

Shadwick[1] in 2002. It can be used to rank and evaluate portfolios

unequivocally and takes into consideration all the characteristics of

the return distribution. Mathematically it can be defined as –

Omega Ratio Ω( ) =

where (a,b) is the interval of returns, r is the required rate of return

and F is the cumulative distribution of returns.

[1] Keating, C., & Shadwick, W. F. (2002). A Universal Performance Measure. Journal of Performance Measurement, 6(3), 59-84. Retrieved from http://edge-

fund.com/KeSh02a.pdf

According to Keating & Shadwick, in the simplest of terms, Omega

ratio involves partitioning returns into gain and loss above and

below a return threshold and then considering the probability

weighted ratio of returns above and below the partitioning.

Diagram 1, shows the cumulative distribution F for Asset A, which

has a mean return of 5%. The return threshold is at r=7%. I2 is the

area above the graph of F and to the right of 7%. I1 is the area

under the graph of F and to the left of 7%. Omega for Asset A at

r=7% is the ratio of probability weighted gains, I2 , to probability

weighted losses, I1[1]

By considering this Omega ratio at all values of the returns

threshold, we obtain a function which is characteristic of the

particular asset or portfolio. We illustrate this in Diagram 2. Omega

for Asset A as a function of returns from r=-2 to r=15. Omega is

strictly decreasing as a function of r and takes the value 1 at Asset

A’s mean return of 5. [1]

Thus an omega score of more than 1.0 indicates the probability of

achieving the threshold return is more than the probability of not

achieving it. The more the omega score, the better the investment

at the given level of threshold return. The omega function as

discussed above can be used to evaluate different investment

opportunities.

7



BACKTESTING

OMEGA RATIO

The Omega Ratio chart shows clearly that our

portfolio outperformed the benchmark at the

15% threshold level. An omega score of 1.33

indicates that there is a 57% chance that the

portfolio will generate a return of 15% or

more. Compared to this the benchmark BSE-

200 had an omega score of 0.93 meaning that

there is just 48% change of the benchmark

returning 15% or more.

Another important observation is that given the

threshold return of 15%, the portfolio

outperformed the BSE-200 over most of the

distribution range, with the outperformance

increasing above the 90% annual return range.

The omega ratio chart shown above confirmed

that our portfolio outperformed the BSE-200 at

the given threshold level of 15%. However, in

order to truly evaluate the portfolio

performance vis-à-vis BSE-200, we should

compare the omega score across whole

spectrum of required return range. The Omega

Function chart shows just that. It plots the

omega score of the portfolio at various

threshold levels. From the chart, it is clear that

our portfolio outperforms the BSE-200 at all

points of required return spectrum.



8



The above back testing result clearly shows that our quantitative portfolio

optimization outperformed its benchmark on all the risk adjusted

parameters. However the efficacy of Mean Variance Optimization have been

under constant debate in the last few decades. There are many proponents

both in favour and against it.

Duchin and Levy (2009) argued that M-V optimization does not beat equally

weighted (1/N) portfolios. According to them Employing the 1/N rule for

investment purposes has the disadvantage of not utilizing information on the

various parameters, but by the same token, it has the advantage of not being

biased in relying on historical parameters that may be much different from

future relevant parameters. They found that 1/N optimization does beat M-

V albeit only for small portfolios.

Do Optimization Really Work?

Optimization – A Critique

Optimization 1500 Years Ago

““““A man should always place his A man should always place his A man should always place his A man should always place his money, one third in land, a third money, one third in land, a third money, one third in land, a third money, one third in land, a third into merchandise, and keep a into merchandise, and keep a into merchandise, and keep a into merchandise, and keep a

third in hand.third in hand.third in hand.third in hand.” ” ” ” - Babylonian Talmud

The most plausible argument against optimization came from the study by DeMiguel, Garlappi, and Uppal (DGU 2009). They

demonstrated then none of the different optimization techniques are consistently better than the 1/N rule in terms of Sharpe

ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more

than offset by estimation error. They attributed the poor performance of the quantitative optimization to the length of the

estimation period and parameter instability.

Arguing in favour of optimization are Kritzman, Page, and Turkington (2010). In their paper titled “In Defense of Optimization :

The Fallacy of 1 / N” they argued that the ostensible superiority of the 1/N approach arises not from limitations in optimization

but, rather, from reliance on rolling short-term samples (60-120 months) for estimating expected returns. Even Markowitz

(1952) did not propose the use of past return for predicting future return. According to Markowitz, optimization is about the

choice of a portfolio given a set of beliefs and is not about how to form those beliefs.

The table summarize the major drawback of M-V optimization vis-à-vis 1/N optimization and the step we have taken to correct

them.

1. Duchin, Ran, and Haim Levy. 2009. “Markowitz versus the Talmudic Portfolio Diversification Strategies.” Journal of Portfolio Management, vol. 35, no. 2 (Winter):71–74.

2. DeMiguel, Victor, Lorenzo Garlappi, and Raman Uppal. 2009. “Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?” Review of Financial Studies, vol.

22, no. 5 (May):1915–1953.

3. Kritzman, M., Page, S. & Turkington, D., 2010. In Defense of Optimization : The Fallacy of 1 / N. Financial Analysts Journal, 66(2), p.31-39.

Drawbacks of MVO Our Modification

Use of short term returns data to forecast future return.We have used the BEst. Consensus target Price to compute the

expected return.

Parameter instability because of the use of small data

sets (~60-120 data points).

We have used weekly returns data for the last 5 years (259 data

points) to compute the variance of each stock and the covariance

matrix among stocks.

M-V underperforms when the portfolio is small (~10-15

stocks portfolio) because small portfolios maximises

estimation error.

We have chosen moderate sized portfolios (~24-27 stocks) and

have constrained the weight to a particular stock to a maximum of

5%.

By constraining the maximum weight of a particular stock to 5% we have ensured that our portfolio will always be moderately

sized (20 stocks at minimum) which will help us in minimizing the impact of estimation error. Also the way MVO works results

in equal weights for most of the stocks. This helps us in getting the benefits of both worlds - MVO as well as 1/N. The result of

our back testing confirms our beliefs that the above modification does help in generating alpha.

9



Current Portfolio

The current portfolio as per our

optimization is given below. We

believe that our portfolio is

optimally diversified with adequate

upside potential and descent risk

reduction. Our portfolio covers

most of the prominent sectors in

India and no single sector have

more than ~20% weightage in the

portfolio which protects our

portfolio from sector specific

shocks.

Particulars Value

Expected Return - 24.96%

Expected Risk (σ) - 32.15%

Beta (β) - 0.89 X

Stocks Weights Stocks Weights

Andhra Bank 4.38% India Cements Ltd 4.38%

Apollo Hospitals Enterprise Ltd 4.38% Indian Bank 4.38%

Aurobindo Pharma Ltd 4.38% Indian Oil Corp Ltd 4.38%

Bank of Baroda 4.38% Jaiprakash Power Ventures Ltd 4.38%

Bharat Electronics Ltd 4.38% MAX India Ltd 4.38%

Bharti Airtel Ltd 4.38% PTC India Ltd 4.38%

Britannia Industries Ltd 4.38% Reliance Infrastructure Ltd 4.38%

Cadila Healthcare Ltd 4.38% Sintex Industries Ltd 2.15%

Dr Reddy’s Laboratories Ltd 4.38% Syndicate Bank 1.44%

GSK Pharmaceuticals Ltd 0.78% Torrent Power Ltd 4.38%

GMR Infrastructure Ltd 4.05% Union Bank of India 3.94%

Gujarat State Petronet Ltd 4.38% United Phosphorus Ltd 4.38%

Hero Motocorp Ltd 4.38%



10



Portfolio Fundamentals

Port Bench +/- Port Bench +/- Port Bench +/-

Utilities 1.91 1.93 -0.02 100.81 117.93 -17.12 7.64 10.35 -2.70

Banks 4.35 1.62 2.73 218.51 244.06 -25.55 10.82 11.82 -1.00

Capital Goods 0.75 1.28 -0.53 194.09 181.55 12.54 13.29 11.20 2.10

Pharma 0.96 0.84 0.12 70.89 37.44 33.44 12.08 13.66 -1.58

Materials 2.27 1.54 0.73 76.06 70.96 5.09 5.14 7.04 -1.91

Autos 4.14 1.85 2.28 23.58 94.13 -70.56 8.44 6.54 1.90

Energy 3.77 1.96 1.82 123.59 53.19 70.40 5.60 6.04 -0.43

FMCG 1.81 1.57 0.24 147.61 56.43 91.18 16.48 17.33 -0.85

Health Care 0.64 1.30 -0.66 29.89 45.56 -15.67 20.09 11.55 8.54

Telecom. 0.38 0.39 -0.01 136.38 125.84 10.54 6.25 6.41 -0.16

Port Bench +/- Port Bench +/- Port Bench +/-

Utilities 0.86 1.35 -0.49 7.32 13.95 -6.63 10.12 10.80 -0.68

Banks 0.64 1.72 -1.09 3.70 10.28 -6.57 17.89 16.42 1.48

Capital Goods 1.12 2.02 -0.90 21.10 14.80 6.30 4.00 13.77 -9.76

Pharma 2.59 4.09 -1.50 59.82 34.76 25.06 20.29 10.16 10.13

Materials 0.80 1.29 -0.49 8.21 9.76 -1.55 10.42 15.08 -4.67

Autos 8.23 2.68 5.55 13.92 9.17 4.75 66.18 31.85 34.33

Energy 0.93 1.41 -0.48 12.40 9.80 2.60 7.15 16.52 -9.38

FMCG 12.54 7.28 5.26 24.10 30.16 -6.06 54.47 24.18 30.29

Health Care 3.07 2.49 0.58 31.84 10.93 20.91 10.01 21.26 -11.25

Telecom. 1.80 1.24 0.55 20.02 22.30 -2.28 8.55 4.90 3.65

ROE

Sectors

Sectors

Div Yld Debt/Equity EV/EBITDA

P/B P/E


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Portfolio Stress Testing

-39% 88%

In the model portfolio that we have created, the expected Risk and Return are dependent on various factors. For example, if the

expected return changes, the Risk and Return characteristics of the portfolio will also change. In order to stress test the portfolio

and see what is the likely Risk-Return characteristics that we can expect, we have run a Monte Carlo Simulation on the portfolio.

We have done a 3,000 run Monte Carlo simulation by using the theoretical distribution of the various variables and their mean

and dispersion. Our simulation shows that we can expect a return of 24.83% with a risk of 32.57%.

Parameter Modifier Distribution

Expected Return Natural Log of Expected Return ln(x) Normal


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Portfolio Stress Testing

However, in the real world, people are more concerned about downside volatility rather than upside potential. So, we have

calculated the probability of incurring a loss and also the probability of not getting the expected return using the parameters

from our Monte Carlo simulation. The result is summarised below –

Particulars Value

Probability of Loss 22.36%

Probability of return less than 8% 30.5%

Probability of return less than 15% 38.21%

Thus, even from a safety first approach, the portfolio is quite desirable. The probability of loss in all the three scenarios is well

within respectable limits. Thus, to sum up, we can say that even after stress testing, the risk-return characteristics of the portfolio

is quite appropriate.

Value-at-Risk (VaR) Analysis

Methodology – Daily VaR 95% VaR 97.5% VaR 99% VaR

Monte Carlo Simulation VaR 6.63% 8.28% 10.69%

Historical 1 Year Simulation VaR 7.68% 9.84% 13.38%



Parametric VaR 6.94% 8.27% 9.81%



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Key to our Recommendation Structure

BUY Absolute) Stock expected to give a positive return of above 15% over a 1 year horizon

SELL (Absolute) Stock expected to give a negative return of less than (-5%) over a I year horizon

ACCUMULATE Stock expected to give a positive return between 5%-15% over a 1 year horizon

REDUCE Stock expected to give a return in the range of (-5%) to 5% over a 1 year horizon

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Managing Biases - Key To Unlocking Alpha

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