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Chapter 2
Review of Literature and Research Methodology
2.1 Review of Literature
This chapter reviews relevant studies which make a base for the present study. Then it
outlines the research methodology which is followed in the thesis.
The financial markets in India have gone through various stages of liberalization that
has increased its degree of integration with the world markets. Some instances of new
policy reforms introduced in the Indian stock markets include introduction of trading
in index futures in June 2000, trading in index options in June 200 I, trading in options
on individual securities in July 2001, introduction of VAR (value at risk)-based
margin, and introduction of the T+2 settlement system from April, 2003. After
implementation of such reforms, the Indian securities market has now become
comparable with securities markets of developed and other emerging economies. In
fact, India has a turnover ratio that is comparable with that of other developed markets
and also one of the highest in the emerging markets. These developments in the
Indian securities market have drawn attention of researchers from across the globe to
look at the price behaviour of the Indian securities market. The daily gross activity
(purchase and sales) of the Foreign Institutional Investors (FlI) in the Indian stock
market has increased almost three-fold in three and half years, from Indian Rupees
(Rs.) 6 billion in October 2000 to Rs. 17 billion by the end of January 2004, to Rs. 1.8
trillion in January 2008.
The increasing interests of foreign investors in the Indian market call for greater
research on various properties and mainly the increased volatility of this market. The
present thesis examines the evidence of stylized facts with focus on volatility in the
Indian stock market.
It is hoped that the findings of this study would greatly help fund managers have a
better understanding of the Indian stock market volatility.
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The magnitude offluc/llations in the return of an asset is called its volatility. As a
concept, volatility is simple and intuitive. It measures variability or dispersion about a
central tendency. To be more meaningful, it is a measure of how far the current price
of an asset deviates from its average past prices. Greater this deviation, greater is the
volatility. At a more fundamental level, volatility can indicate the strength or
conviction behind a price move. Despite the clear mental image of it, and the quasi
standardised status it holds in the field of finance, there are some subtleties that make
volatility challenging to analyse. Since volatility is a standard measure of financial
vulnerability, it plays a key role in assessing the risk/return tradeoffs and forms an
important input in asset allocation decisions].
The relationship between feedback trading and volatility persistence is well
documented in financial literature with evidence about their significant joint presence.
This is the "open sesame" for manipulation. It suggests that feedback traders are
capable of bearing a destabilizing influence over securities prices, an issue of key
importance especially in the context of emerging markets due to the vulnerable
structure of those markets.
Numerous products are available on NSE following the introduction of derivative
trading. There are futures and options of variable periods. There is a blanket closure at
the expiry on the last Thursday of every month. They have a role in maintaining the
market stability.
We have reviewed studies encompassing many features and factors that would affect
volatility, such as - contribution of derivatives, the presence of rational and irrational
traders, cyclicity of returns and stock prices, review of popular models to estimate
volatility, studies that focus on psychology as behaviour biases etc. Considering the
global influence as well as scarcity of topic related work in India, we reviewed the
vast literature nationally and globally. The various studies try to arrive at a
mathematical model and are discLlssed briefly below:
• Sah and Omkarnath (2003)2 did not tind any contribution of derivatives
trading by comparing the period before and after its introduction. The study
was undertaken by them in year 2003 when the derivative market in India was
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at a very nascent stage. The volumes in derivates have greatly picked up since
2005. Thus the contribution of volatility to intraday and day to day volatility is
now seen in the stock markets.
• Barberis and Thaler 20023 emphasised that there are rational and irrational
traders in the markets. Therefore, prediction based on behaviour finance must
take into account the effects that irrational market players will have on the
markets. Hence, the rational market players are also influenced. This
observation thus obliterates the predictability of markets based on behaviour
finance. Their study leads to an interesting observation that volatility cannot
exist if only the rational investors were investing! They have thus highlighted
market efficiency being dependent upon investor psychology and investor
behaviour in the comparative recent discipline (evolving since 1985), viz.
Behavioural Finance. They argue that some financial phenomena can plausibly
be understood using models in which some agents are not fully rational. The
field has two building blocks: till/its to arbitrage, which argues that it can be
difficult for rational traders to undo the dislocations caused by less rational
traders: and psychology, which catalogues the kinds of deviations from full
rationality we might expect to see. They discuss these two topics, and then
present a number of behavioural finance applications: to the aggregate stock
market, to the cross-section of average returns, to individual trading
behaviour, and to corporate finance.
• In segmented capital markets, a country's volatility is a critical input in the
cost of capital (Bekaert and Harvey 1995)4.
• Peters (1994)5 noted that stock prices and returns are cyclical, imperfectly
predictable in the short run, and unpredictable in the long run and that they
exhibit nonlinear, and possibly chaotic, behaviour related to time-varying
positive feedback. Asset-return variability can be summarised by statistical
distributions. Typically, the normal distribution is used to characterise a series
of returns. The distribution is centred at the mean and its width is determined
by the standard deviation (volatility). Return series may not be normally
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distributed and often tend to exhibit excess Kurtosis" so that extreme values
are more likely than the normal distribution would suggest. Such fat-tailed
distributions are common with financial parameters. Skewnessii is also
common, especially with equity returns, where big down moves are typically
more likely than comparable, big up-moves.
• Time-variation in market volatility can often be explained by macroeconomic
and micro-structural factors (Schwert I 989a,b)6/. Volatility in national
markets is determined by world factors and part determined by local market
ejfects. assuming that the nationolmarkets are glubally linked
• It is also consistent that world factors could have an increased influence on
volatility with increased market integration. Bekaert and Harvey (1995)8
showed this using time-varying market integration parameter.
The prediction of volatility in financial markets has been of immense interest among
financial econometricians.
• This interest is further rekindled by Bollerslev et al. (1994)9 when they
established that financial asset return volatilities are highly predictable.
It is true that unlike prices, volatilities are not directly observable in the market, and it
can only be estimated in the context of a model.
• However, Andersen et al. (2001)10 concluded that by sampling intra-day
returns sufficiently frequently, the reali~ed volatility (measured by simply
summing intra-day squared returns) can be treated as the observed volatility.
J In probability theory and statistics. kurtosis (hom the Greek word KUproS, kyrtos or kurtos, meaning bulging) is a measure of the "peakedness" of the probability distribution of a real-valued random variable. lligher kurtosis means morc of the variance is the result of infrequent extreme deviations, as opposed to frequent modestly sized deviations. 11 In probability theory and statistics. skewness is a measure of the asymmetry of the probability distribution of a real-valued random \'ariable.
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• This observation has profound implication for financial markets (Brooks
1998) 11 in that
a) The realized volatility provides a better measure of total risk (value at
risk) of financial assets, and
b) It can lead to bctter pricing of various traded options,
• It has been observed in early sixties of the last century (Mandelbrot, 1963)12
that stock market volatility exhibits clustering, where periods of large returns
are followed by periods of small returns,
• Latcr popular
(1982)13 and
models of volatility clustering were developed by Engle
Bollerslev (1986) 1", The autoregressive conditional
heteroskedastic (ARCH) models (Engle, 1982) and generalized ARCH
(GARCH) models (Bollerslev, 1986) have been extensively used in capturing
volatility clusters in financial time series (Bollerslev et aI., 1992)15,
• Using data on developed markets. several empirical studies (Akgiray, 1989;
West et aI., 1993)16 havc confirmed the superiority of GARCH-type models in
volatility predictions over models such as the na'ive historical average, moving
average and exponentially weighted moving average (EWMA),
GARCH models can replicate the fat tails observed in many high frequency financial
asset return series, where large changes occur more often than a normal distribution
would imply,
• Financial markets also demonstrate that volatility is higher in a/ailing market
than it is in a rising market, This asymmetry or leverage effect was first
documented by Black (1976)17 and Christie (1982)18,
• Three popular GARCH formulations for describing this asymmetry are Power
GARCII model (Ding et aI., 1993), Threshold GARCH model (Glosten et aI.,
1993)19 and Exponential GARCI I model (Nelson, 1991 )20,
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• Empirical results also show that augmenting GARCH models with
information like market volume or number of trades may lead to modest
improvement in forecasting volatility (Brooks, 1998; Jones et ai, 1994l.
• The association between stock return volatility and trading volume was
analyzed by many researchers (Karpoff, 1987)22.
• The initial research on price-volume relation can be attributed to Osborne
(1959)23 who attempted to model stock price change as a diffusion process
with the variance dependent on the number of transactions.
• Later research on the empirical relationship between daily price volatility and
daily trading volume was based on Clarks (1973)24 mixture of distribution
hypothesis (MOH). The essence of MOH is that if the stock return follow a
random walk and if the number of steps depends positively on the number of
information events, then stock return volatility over a given period should
increase with the number of information events (e.g., trading volume) in that
period.
• [n a recent study on individual stocks in the Chinese stock market, Wang et al.
(2005)25 showed that inclusion of trading volume in the GARCH specification
reduces the persistence of the conditional variance dramatically, and the
volume effect is positive and statistically significant in all the cases for
individual stocks.
• However, another study on the Austrian stock market (Mestel et aI., 2003)26
found that the knowledge of trading volume did not improve short-run return
forecasts. Most of the studies on the relationship between return volatility and
trading volume have used volume levels.
There have been a few attempts to model and forecast stock return volatilities in
emerging markets.
[41
• For example, Gokcan (2000)27 finds that for emerging stock markets the
GARCH (I, I) model performs better in predicting volatility of time series
data. In another market specific study, Yu (2002)28 observes that the stochastic
volatility model provides better volatility measure than ARCH-type models.
• A few studies were conducted (e.g., Vanna, 199929 and 20023°; Kiran Kumar
and Mukhopadhyay", 2002; Raju and Ghosh, 200432; Pandey, 200533;
Karmakar, 200534) on modelling stock return volatility in the worlds largest
democracy, India.
• Vanna (1999)35 showed, using daily data from 1990-1998 of an Indian stock
index (Nifty), that GARCH (I, I) with generalized error distribution performs
better than the EWMA model of volatility.
• [n a later study, Pandey (2005) showed that extreme value estimators perform
better than the conditional volatility models.
• [n another recent study, Karmakar (2005) used conditional volatility models to
estimate volatility of fifty individual stocks and observed that the GARCH (1,
I) model provides reasonably good forecast.
Most recent studies on financial market volatility are placed in the context of
transmission of volatility across economies and the contagion effects of a financial
crisis.
• These include studies by Forbes and Rigobon (2002)36, Bekaert, Harvey and
Lumsdaine (2002a.b )37, Edwards (2000)38 and others.
• Rigobon (2003)39 has focussed on alternative measures of volatility in the
equity and bond markets in the period surrounding the financial crises.
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• Bekaert and Harvey (2000)4<1 analyzed equity returns in a group of emerging
markets before and after financial reforms. The empirical studies investigating
the volatility of returns have yielded mixed conclusions.
• Aggarwal, Inclan and Leal (1999)41 analyze volatility in emerging stock
markets during 1985-95. Using an less algorithm to identify the points of
sudden changes in the variance of returns they examine the nature of events
that cause large shifts in stock return volatility in these economies. Aggarwal
et al find that mostly local events cause jumps in the stock market volatility of
the emerging markets.
• Kim and Singal (1997)42 and De Santis and Imorohoroglu (1994) study the
behaviour of stock prices following the opening of a stock market to
foreigners or large foreign inflolVs. They tind that there is no systematic effect
of liberalization on stock market volatility.
• Richards (1996)43 used three different methodologies and two sets of data to
estimate volatility of emerging markets. A common claim of all these studies
is that. the proposition that liberalization increases volatility is not supported
by empirical evidence.
• However, Levine and Zervos (1995)44 suggest that volatility increases after
liberalization.
• Hamao and Mei (200 I )45 examined the impact of foreign and domestic trading
on market volatility for Japan and tind no systematic evidence that foreign
trading tends to increase market volatility more than trading by domestic
groups. The study however relates to the time period during which the foreign
portfolio investment in Japan was rather small.
Studies analyzing the behaviour of stock prices over financial cycles have been
undertaken in the recent years. They show that stock markets when liberalized tend to
become more stable.
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• Kaminsky and Schumkler (2001,2002)46,47 examine the time varying patterns
of financial cycles before and after financial liberalization in 28 countries.
Their results indicate that while liberalization may trigger financial excesses in
the short-run it also triggers changes in institutions supporting a better
functioning of financial markets. They observe a temporary volatility increase
in the years immediately following liberalization in these countries.
Studies that focussed on psychology as behavioural biases: representativeness
heuristic, conservatism, overconfidence are as under:
• Barberis and Thaler 200i8 have focussed on the role of rational and irrational
market player's interaction.
• Bikhochandani and Sharma (2001)49 discuss psychological aspects such as:
Herding imitation; Behavioural similarities following interactive
observations for a period, like
Temporary information blockage
Slower information aggregation, and
Cascading
Studies on Mechanism of market manipulation
It was first high-lighted by:
• Allen and Gale (1992)50 have contributed a seminal article, referred and cited
in several studies on manipulation of the markets. It is generally agreed that
speculators can make profits from insider trading or from the release of false
infonnation though both forms of stock-price manipulation have now been
made illegal. They argue that it is not impossible. An uninformed speculator
simply buys lind sells shares. They show that in a rational expectations
framework, where all agents maximize expected utility, it is possible for an
uninformed manipulator to make a profit, provided investors attach a positive
probability to the manipulator being an informed trader.
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• Van Bommel51 (2003) elucidates the feedback trading as conduct based on
historical data. They noted the role of overconfidence and observed temporary
information blockage when such deals are carried out.
• Koutmos and Saidi52 (200 I) observe that positive feedback trading can induce
autocorrelation in stock returns and increase volatility. They emphasize on
rational response as portfolio insurance, stop loss order, margin accounts
liquidation.
• Andergassen53 (2003) also observed that margin trading and trading in the
derivatives are key factors in margin stability: however they do trigger
volatility. They note that herd behaviour may turn out to be rational
speculation since it involves starting / riding the trend.
• Gelos and Wei5., (2002) arguc that Indian markets are prone to trend chasing
behaviour. Interestingly enough, although India constitutes one of the fastest
grOlr ing emerging llIarkets, the issue of feedback trading and its relationship
to volatility has largely been overlooked in its context. This lVas one of the
mainllIotivatingfactorsfor the present study. They also studied fund manager
behaviour internationally and compared it with transparency. Less the
transparency, more herding behaviour was their conclusion.
• John Graham, Harvey Campbell and Hai Huang (2006)55 on studying
behavioral finance, they observed that people are more willing to bet on their
own judgments when they feel skillful or knowledgeable. They investigate
whether this competence effect influences trading frequency and home bias.
They find that investors who feci competent trade more often and have more
internationally diversified portfolios. They also fll1d that male investors, and
investors with larger portfolios or more education, are more likely to perceive
themselves more competent than the female investors, and investors with
smaller portfolios or less education. The paper also contributes to
understanding the thcoretical link between overconfidence and trading
frequency. Existing thcories on trading frequency have focused on one aspect
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of overconfidence, i,e" miscalibration, The paper offers a potential mechanism
for the better-than-average aspect of overconfidence to influence trading
frequency, In the context of their papers, overconfident investors tend to
perceive themselves to be more competent, and thus are more willing to act on
their beliefs, leading to higher trading frequency,
• David Hirshleifer, Kewei Hou, Siew Hong Teoh and Yinglei Zhang56, (2004)
emphasized the importance of profitable cash flows thus: When cumulative
net operating income (accounting value-added) outstrips cumulative free
cash flow (cash value-added), subsequent earnings growth is weak, If
investors with limited attention focus on accounting profitability, and neglect
information about cash profitability, than net operating assets, the cumulative
difference between operating income and free cash flow, measures the extent
to which reporting outcomes provoke over-optimism during the 1964-2002
sample period,
• De long and James Bredford (1990)57 showed that the issue of the relationship
between feedback trading and volatility bears an interesting connotation in
terms of financial regulation, as the dominance of feedback traders in the
market can well lead to destabilizing phenomena with prices deviating wildly
from their fundamental values when there are incomplete regulatory
environments such as corporate disclosure and information quality, De long
and other showed58 that the presence of sentiment investors in IPOs reduces
the "winners curse" problem, and further that the expected excess return to
sentiment investors may be positive or negative, depending on parameter
values, The possibility of a positive expected return suggests a rational basis
for the presence of sentiment investors in IPOs, They coined the phrase,
"winners curse" when it is based entirely on the sentiments investors herd
together and run to invest without caring for the fundamentals,
This is exactly what happened to Reliance Power IPO, Those who got the allotment
were cursed while those who did not can buy the stock in the open market at a high
discount
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• Aggarwal and W U59 explain it thus: what happens when a manipulator can trade
in the presence of other traders who seek out information about the stocks true
value. In a market without manipulators, these information seekers
unambiguously improve market efficiency by pushing prices up to the level
indicated by the informed party's information. In a market with manipulators,
the information seekers playa different role. More information seekers imply
greater competition for shares, making it easier for a manipulator to enter the
market and potentially worsening market efficiency. This suggests a strong role
for government regulation to discourage manipUlation while encouraging
greater competition for information. Their research of US markets provided
them with concluding evidence that potentially informed parties such as
corporate insiders, brokers, underwriters, large shareholders and market makers
are likely to be manipulators. More illiquid stocks are more likely to be
manipulated and manipUlation increases stock volatility. They showed that
stock prices rise throughout the manipUlation period and then fall in the post
manipUlation period. Prices and liquidity are higher when the manipulator sells
than when the manipulator buys. In addition, at the time the manipulator sells,
prices are higher when liquidity is greater and when volatility is greater. These
results suggest that stock market manipulation may have important impacts on
k ffi ' 60 mar et e lClency .
Feedback trading, as it has been now come to be called describes how the traders - and
the daily traders in particular trade. There are three main types of operations that are
applied:
• Contrarian tradillg
It simply applies that one acts contrary to the logics of the market at a given time.
This is so constant a happening that some fund managers"l manage their portfolios
going against the logical parameters. They argue on the basis of market data that
contrary to the standard belief the highest risk stocks can be expected to produce the
lowest returns and vice a versa - and label themselves as Contrarians!
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• Werner, De Bondt and Thaler62•63
, economists themselves, applied
psychological principles64 governing behaviour psychology to the market.
They were struck by the similarity between two sets of empirical findings
pertaining to the market and individual decision making. Both of them are
characterised by overreaction. This was further confirmed by Hong and
Stein65
• This is common knowledge to the market players. What is left out by
the acadcmicians is the practical aspect of application in the market at the
appropriate time- moment. George Soros"', who has made his mark as an
enormously successful speculator, was wise enough to largely withdraw when
still way ahead of the game, cmphasized this as "". prevailing view of how
financial markets operate tends to leave the participating function out of
account66
,67. Thus, the importance of participation at the right time by the right
person is the key; there are several stock market rules that can be well argued
out: they seldom make success68 Is it not said that success goes to those that
dare and act?
Academics based on sound principlcs of behaviour psychology help those who
understand them and apply appropriately at the correct time. What does this mean
actually when one is facing the screen? Jack Welsh, a successful manager and player
of the market, called it acting Straight from the Gut69•
• ~lomentum trading
Jegadeesh and Titman 70 suggests that there is substantial evidence that indicates that
that the performance of stocks over a three- to 12-month period tend to continue in
similar direction over the subsequent three to 12 months. This phenomenon is
ascribed to momentum trading; acceleration and deceleration both take time to
unwind the steam. The strategies that exploit this phenomenon have been consistently
protitable in the United States and in most developed markets. Similarly, stocks with
high earnings momentum outperform. This is the basis of classifying certain high
III Soros made millions that he has donated in philanthropic causes too; as v.:ell as in unsuccessful attempts to defeat George Bush in his second term campaign in 2004. Participating function is well illustrated by the fact that Soros is said to have lost 80% of his earnings in 2008 crash, hoping to make some good by writing a book about it.
148
dividends yielding stock as beta stocks; they continue to maintain/improve over their
dividend records: thereby their prices too do not fluctuate widely.
• TecftlliclII1I1I1Ilysis 71
Technical analysts seek to identity price patterns and trends in financial markets and
attempt to exploit those patterns. While technicians use various methods and tools, the
study of price charts is primary.
Technicians especially search for archetypal patterns, such as the well-known head
and shoulders or double top reversal patterns, study indicators such as moving
averages, and look for forms such as lines of support, resistance, channels, and more
obscure formations such as tlags, pennants or balance days.
Technical analysts also extensively use indicators that are typically mathematical
transformations of price or volume. These indicators are used to help determine
whether an asset is trending, and if it is, its price direction. Technicians also look for
relationships betwecn price, volume and, in the case of futures, open interest.
Examples include the Relative Strength Index (RSI), and Moving Averages
Convergence Divergence (MACD). Other avenues of study include correlations
between changes in options (implied volatility) and put/call ratios with price. Other
technicians include sentiment indicators, such as Put/Call ratios and Implied Volatility
in their analysis.
Technicians seek to forecast price movements such that large gains from successful
trades exceed more numerous but smaller losing trades, producing positive returns in
the long run through proper risk control and money management.
There are several schools of technical analysis. Adherents of different schools (for
example. candlestick charting, Dow Theory, and Elliott wave theory) may ignore the
other approaches, yet many traders combine elements from more than one schoo!.
Some technical analysts use subjective judgment to decide which pattern a particular
instrument reflects at a given time, and what the interpretation of that pattern should
149
be. Some technical analysts also employ a strictly mechanical or systematic approach
to pattern identification and interpretation.
Technical analysis is frequently contrasted with jilndamental analysis, the study of
economic factors that influence prices in financial markets. Technical analysis holds
that prices already reflect all such influences before investors are aware of them,
hence the study of price action alone. Some traders use technical or fundamental
analysis exclusively, while others use both types to make trading decisions.
Thira and Enke72 presents the use of an intelligent hybrid stock trading system that
integrates neural networks, fuzzy logic, and genetic algorithms techniques to increase
the efficiency of stock trading when using a Volume Adjusted Moving Average
(VAMA), a technical indicator developed from equivolume charting. For this
research, a Neuro-Fuzzy-based Genetic Algorithm (NF-GA) system utilizing a
VAMA membership function is introduced. The results show that the intelligent
hybrid system takes advantage of the synergy among these different techniques to
intelligently generate more optimal trading decisions for the VAMA, allowing
investors to make better stock trading decisions.
Much of econom ic theory is currently presented in terms of mathematical economic
models, a set of stylized and simplitied mathematical relationships that clarify
assumptions and implications. Formal economic modelling began in the late 19th
century with the use of differential calculus to describe and predict economic
behaviour. Economics became more mathematical as a discipline throughout the first
half of the 20th century, but it was not until the Second World War that new
techniques would allow the usc of mathematical formulations in almost all of
economics. This rapid systematizing of economics alarmed critics of the discipline as
well as some esteemed economists. John Maynard Keynes, Robert Heilbroner,
Friedrich Hayek and others have criticized the broad use of mathematical models for
human behaviour, arguing that some human choices are irreducible to arbitrary
quantities or probabilities.
Yet, Behavioural economics and behavioural finance are closely related fields that
have evolved to be a separate branch of economic and financial analysis which
150
applies scientific research on human and social, cognitive and emotional factors to
better understand economic and market decisions by consumers, borrowers, investors,
and how they affect market prices, and the returns.
The field is primarily concerned with the bounds of rationality (selfishness, self
control) of economic agents. Behavioural models typically integrate insights from
psychology with nco-classical economical theory. Psychologists have been
investigating why would people speculate - gamble - or, why would they insure,
when the chances are heavily weighted against them. We could not find any
psychology literature from India on the subject. The discipline is comparatively new
but there has been basic psychology research on the topic for long in the West. Since
the psychology of risk taking and risk aversion - insurance - is globally the same, we
have referred to a couple of basic books in psychology to understand the
fundamentals of behaviour psychology73,'4.
Jerome Bruner's'5 contribution to psychology can be compared to that of Pavlov and
Watson, classical behaviourists of earlier times. Rather than attributing behaviour to
the conditioned reflexes (herding and imitation are examples), he was a pioneer who
talked about insights guiding the bchaviour'6. Present day behaviour finance and its
further evolution to various kinds of feedback trading rely on the basic fundamentals
of both; behaviour and cognitive psychology so much so that the research protocols
are drawn basically from the work of Bruner (Appendix 3).
We found some publications of seminal importance for (i) India and (ii) global
scenario. It is pertinent to mention the Indian works here, in spite of their referencing
elsewhere too. They are:
Roy M. K. and Kannarkar M (1995).77
Based on measurement of stock market volatility for the period 1935 to 1992, they
focus on two key issues:
a) What is the average level of volatility and whether it has increased in the
subsequent period,
151
b) Whether the present trend of share price movement IS likely to impair the
development process of our economy.
Madhusudan Kannakar (2005),78
This is an extension of the paper mentioned earlier. The aim of this paper is to
estimate conditional volatility models in an effort to capture the salient features of
stock market volatility in India and evaluate the models in terms of out-of sample
forecast accuracy. The estimation of volatility is made at the macro level on two
major market indices, namely, S&P CNX Nifty and BSE Sensex. The fitted model is
then evaluated in terms of its forecasting accuracy on these two indices.
The paper relies heavily on econometric studies to arrive at a simple conclusion that
positive return stocks generate less volatility then negative return stocks, all else being
equal. The importance of the finding is great for individual investors. (This has lead to
the concept of ~ stocks". (Ex. lTC, GSK Phanna).
Harvinder Kaur (2004),79 (vide infra)
Other psychological aspects that are mentioned for the sake of completion are:
I. Gambling80
2. The Hallow EffectBl
3. Rumours as forecasts (Appendix 4)
4. Circadian Rhythm82
5. Freak factors (Appendix 5)
Behavioural finance has become the theoretical basis for technical analysis. Though
lot of mathematics is involved, Caginalp and Balenovich83, both high profile
1V B, Beta indicates the sensitivity of a stock's returns to the changes in the benchmark Index. For instance, a beta-one stock wil! change by the same percentage as the change in the benchmark. A beta lower than one, indicates a lower sensitivity to tht: benchmark. Thus, a stock with a beta 0[0.6 will fall by 6% if the index falls [0%. However, this also means that low-beta stocks are laggards during a bull run, as they are less, sensitive to market movements. So.lo\V~bcta scripts lose their charm during a bull stampede, but they provide the much-needed solace during times of sharp declines.
152
professors of mathematics initially, did not find any sound basis to principles of
mathematics. They described such analysis as philosophical! Paradoxically, finding
that there is some relevance in the patterns using a dynamical microeconomie model
which generalizes the classical theory of adjustment to include finite asset base and
trend-based investment preference, they themselves evolved a charting pattern. The
mathematically complete system of (deterministic) ordinary differential equations that
has provided a quantitative explanation of the laboratory bubbles experiments
generate a broad spectrwll of patterns that are useful as they found in their studl4.
The origins of many of these charting patterns are classified as 0) those that can be
generated by the activities of a singlc group. and (iil those that can be generated by
the presence of two or more groups with asymmetric information. Examples of (il
include the head and shoulders. double tops. rising wedge while of (ii) includes
pennants, symmetric triangles and Fibonacci charts predictions.
The system of dilTerential equations is easily generalized to Stochastics85• Application
is also made to Japanese candlestick analysis86• Chart I Page No 185
As is evident from the survey of literature discussed above, the issue of changes in
volatility of stock returns on account of stock market liberalization in emerging
markets has received considerable attention in recent years.
• However almost all the studies undertaken thus far analyze the changes in
volatility across selected emerging markets in Latin America and East Asia. In
most studies India is not included in the sample of countries for which
liberalization and volatility is analyzed.
• In addition, in most studies a narrowly defined concept of financial
liberalization is adopted.
• Research has also shown that capital market liberalisation policies too, are
likely to affect volatility. It would be of interest to policy makers that the
153
correlation between the two has been found to be positive in the c"se of some
countries.
The review of literature has provided us the findings and contribution of eminent
researchers at national and international level on volatility and its estimation. Though
prominent research with significant contribution have surfaced, it is found that the
review of most of the studies do not throw light on the causative factors of intraday
volatility. Trading days where there are huge intraday swings in the frontline index
are not addressed by any of these studies reviewed.
The increasing interests of foreign investors in the Indian market call for greater
research on various properties of this market and to examine the evidence of stylized
facts in the Indian stock market. They are:
o Stock markets are characterised by bursts of price volatility.
o Price changes are less volatile in bull markets and more volatile in bear
markets.
o Price change correlations are stronger with higher volatility, and their auto-
correlations die out quickly.
o Almost all real data have more extreme events than suspected.
o Volatility correlations decay slowly.
o Trading volumes have memory the same way that volatilities do.
o Past price changes are negatively correlated with future volatilities.
The survey of literature mentioned above raises many concerns, which are expressed
as follows:
>- Has the world's financial system become more volatile in recent times?
>- Has financial deregulation and innovation lead to an increase in financial
volatility or has it successfully permitted its redistribution away from risk
averse operators to more risk neutral market participants?
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;;.. Is the current wave of financial innovation leading to a complete set of
financial markets, which will efficiently distribute risk?
;;.. Has global financial integration led to faster transmission of volatility and risk
across national frontiers?
;;.. What are the reasons for the volatility prevailing in the Indian markets?
;;.. Can financial managers most efficiently manage risk under current
circumstances?
:;. What role the regulators ought to play in the process?
Addressing such concerns the present study seeks
• To throw an insight into the existence of a possible relationship between such
variables which capture financial and economic integration as market
capitalisation to GDP, country credit risk ratings.
• This study also tries to show that the change in volume of trade in the market
directly affects the volatility of asset returns.
This is important to investigate because
• Finally, at the level of the investor, frequent and wide stock market variations
cause uncertainty about the value of an asset and affect the confidence of the
investor. Risk averse and risk neutral investors may shy away from the market
with frequent and sharp price movements.
• An understanding of the market volatility is thus important from the regulatory
policy perspective.
With the help of theoretical background developed in first chapter and reviewing
available literature, the study will make a humble attempt to study volatility and
155
manipulation during the period 2006 - 2008 to capture the salient features of
stock market volatility in India.
The stock market was reviewed historically from 199 I -2006. The factors that affected
it were studied live from July 2006-January 2008. Several factors emerged as the
causal agents for volatility. They were broadly classified as
I. Fundamentals
ii. Feedback trading including technical analysis
III. Miscellaneous factors. The factors found to influence stock markets were
identified and titrated against the empirical data emerging from the market
performance.
The present research is an attempt to locate and analyze the factors that may be
responsible for volatility and scope of market manipulations. Following the
formation of SEBI, there are regulatory controls that aim at curbing the market
manipulation. The market players may still be able to get around to
manipulating. The study aims to investigate whether this indeed can happen.
2.2 RESEARCH METHODOLOGY
2.2.1 Introduction
The theoretical base developed in the earlier Chapters and the above point with the help
of various books and available literature has helped the researcher to understand the
research problems in a better and systematic manner. Research Methodology is a
systematic and structured procedure to arrive at the conclusion of a defined problem.
This point provides an insight to the Research Methodology with the help of which the
present study is carried out.
2.2.2 Motivation for the study
The prime motivation was to empower the small individual investor with
understanding of the market behaviour and evolve strategies for protection and growth
ofhis/her capital. It was thought that volatility and market manipulation are likely to be
156
prime factors that corrupt the market function and dupe the individual investor. And, it
was thought that a background of work in finance and banking sector along with the
market exposure as a small individual investor, are the correct prerequisites to start the
present study. The primary motive leads to the universe of the study. It is from the
focal point of a single investor to the global perspective of global financial players. The
motivation further propels to study rapidly changing scenario of financial state of
global economy that is influencing bourses and to use it to the advantage of the
individual investor.
Gut wrenching volatility ranging from 3 percent to 9 percent or indices hitting circuit
filters, literally makes the retail investor throwaway stocks sometimes at great losses.
The volatility affects the retail investor the most. Several instances are cited in the
media of investors sometimes taking drastic steps because of huge losses suffered due
to the roller coaster ride of the stock market on a day to day basis even though when we
take a year to year analysis of the bell weather indices they have delivered returns
between 30 to 40 percent. Many studies do reflect that volatility may have reduced in
the past few years after the introduction of derivative products. But to put a case to
elaborate the volatility of the Indian stock market for an investor who may have bought
1000 shares of Steel Authority of India on the 16th September 2007. That would be an
investment of Rs. 154,000/00. On the opening bell of l7'h September 2007 of the stock
market his investment would be down by 10 percent. The value would be Rs.
140,000/00. The indices had hit a circuit filter as the Finance Minister had banned
investment in Stock Market via P notes for Fils. After, clarifications were made by
both the Finance Minister and the SEBI chief the markets recovered and went to hit
new highs. But investors who would be holding on to shares are unnerved on seeing the
market value of their shares depreciating in matter of seconds and they simply book out
by incurring losses or taking minimal profits.
So we have decided to focalise and, chose a small segment to study: intraday and
day to day volatility. We have tried to align them with likely causes so as to
explain the occnrrence. That is almost uncharted in the last decade following drastic
reforms.
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Middle class invests hard earned money in the stock market that is beneficial to the
country as well as to the industry. Profitability and appreciation are natural dues for the
individual and the system owes to safeguard them. Consequences of market crashes
result in payment of heavy price socially. We have imported a model of stock market
from the West and implemented it ipso/aclo. There is a tremendous lot of transparency
and yet India cannot match the West in terms of investigative journalism. A way has to
be found that mal-practices do not occur. It is not sufficient that the miscreants can now
be brought to books. The damage is done before that.
Though the stock market indices are a measure of the Nation's wealth, as well as
individual stock prices of a particular stock determine the worth of promoter or
majority stake holder, it cannot be logical that the worth of a venture can pulsate in a
wide percentile range in a single day. Obviously the elementary principle of supply and
demand drives the price. The number of a particular stock floating in the market is
always limited: it has to be titrated against the amount of money that is targeting it. The
money supply though not limitless, can be enormous; particularly if a few players
combine to take a conce.ied action by forming a syndicate. This is the fundamental
basis of volatility.
Thus the universe of the study extends globally in geography: in terms of disciplines, it
encompasses economics, finance, all aspects of commercial dealings in the market
including numerous products on the exchanges, behaviour psychology, mathematics
and econometrics including stochastics. Obviously this is just not only formidable; it is
impossible. Thus we have to limit our study to the Indian stock market and National
Stock Exchange.
2.2.3 OBJECTIVES OF THE STUDY
The prime aim of the present study is to locate and analyse the factors that may be
possible for volatility and the scope of market manipulations with respect to the --formation of SEBI. Based on tbe above prime aim, some specific objectives are also set
for better understanding of the research problem. It is intended to study:
158
o whether the stock market behaviour has also been changed following the
liberalisation of the Economy since 1991,
o the fundamental factors driving and affecting the Indian Stock Market,
o the extent of volatility in Indian Stock Market and whether it is possible
to predict the market volatility and movement day to day,
o how liberalisation has brought about this [volatility] phenomenon In
Indian Capital Markets,
o the role of market manipulators who bring about the intraday volatility
and the extent of market manipulators,
o whether volatility and manipulations of the secondary market can be
curbed and whether it is possible to generate more effective mechanism
and policies to curb volatility and manipulations,
o the volatility in the secondary market and to identify various strategies
for the regulators and,
o Whether it is possible for an individual investor to invest as the "Smart
Money" and to propose an investment strategy for the small investors.
2.2.4 Significance of the study
This study is probably one of the few that has not only,
(i) employed a relatively simple method of volatility measurement that IS
consonant to intra-day and day to day trading
(ii) But it has also tried to link the causative factors to it so as to evolve a rational
and empirical basis. Causative factors have been identified: the study shall
guide as to what to expect when these causes are seen to be operative.
(iii) This shall provide a further platform to do such studies on intraday movements
of the stocks that may include the entire range of tick prices since such data
may soon become available because of advances in technology.
The parameters of determining volatility based on returns are impractical in
such trading: there the key lies in the hand of operator and he has to develop a
159
"gut sense" to press the right key when he sees the appropriate situation/s
emergIng.
(iv) It will be most useful to the small time traders/ investors but shall also provide
useful insights to hedge and arbitrage fund managers.
2.2.5 Scope of the study
The scope of the study is limited to the intraday fluctuations of the Nifty, the values of
which have been collected from the National Stock Exchange.
2.2.6 Time Span
Secondary data as obtained from the market performance between 1 ,t July 2006 to
30th June 2008, is tabulated with reference to volatile behaviour and attempt is made
to define likely factors that cause feedback trading.
The determination of volatility was worked out as; percentage variation and
coefticient of variation (vide infra). The market was reviewed and searched for the
causative factors from CNBC TV 18, NDTV Profit, UTV, ET Now and other business
channel telecasts, news papers (data was derived from The Economic Times, Business
Standard, Gujarat Samachar and such other dailies), bulletins from broker houses such
as Finnapolis, market analysts and chartists. This was juxtaposed with the market
performance for analysis. Collation so accrued was noted.
2.2.7 Data Collection and Methodology
The present study is descriptive type in nature. The major purpose of descriptive
research is description of the State of affairs as it exists at present87. If we take a
broader outlook, the research is described as Descriptive and non-experimental.
To measure intraday volatility we have used a lonnula which is given below:
160
d =x-x
Standard Deviation
Coefficient of variation _0 __ x 100
[Percentage variation from mean 1
d = Deviations from Mean
L = Sigma = Summation
(J = Standard Deviation
The study relies heavily on secondary data. The secondary data is collected from:
a. RBI, report on Currency and Finance.
b. Economic Surveys
c. CMIE Reports
d. RBI Annual report
e. SEBI
f. Economic Surveys of GOI
g. Various journals, report and magazines both international and national.
h. Database ofNSE
I. Database of BSE
Secondary data was gathered as it generated from day to day stock market behaviour.
It was studied as the ongoing process every day as markets behaved from day to day
from I st July 2006 to 30 June 2008. Most studies reviewed by us are retrospective;
they are conducted academ ically after some time: at times after number of years.
Probably this may be a unique instance where the market behaviour was studied and
analyzed in terms of volatility. not only from day to day but, as it occurred. It was
reviewed vis-a-vis the emerging fundamentals. Technical analysis involved the study
of several models: Japanese candle sticks, Elliot waves, Relative Strength Index,
Gann Calculator, Stochastics, Moving Average Convergence and Divergence, Money
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flow, Relative strength. Mr.rket products, such as Metastock, Advance Get and
Japanese candle sticks were utilized to collate the technical charts.
Secondary data for market manipulations was obtained from the published reports of
the litigations against two well established market manipulators, Harshad Mehta and
Ketan Parekh. It is discussed already.
2.2,8 Plan of Data Collection
A population is the set of all the elements in a studl8.
At the onset variOus products available on the stock market and their
interconnectedness in regulating the market were studied. For sound and concise
empirical data, Nifty was chosen as a parameter.
Nifty a product of the NSE is a composite index of 50 scripts and is traded as a stock.
The included scripts are varied from time to time. The prices of the included stocks
are appropriately weighted as per their capital. This is fine tuned arrangements and
though the movements of anyone of the constituent does reflect in Nifty at that
moment, its reflection is as much as the value apportioned to the stock as index
weightage. The fluctuations in the price of the Nifty included stocks electronically get
reflected in Nifty and, therefore, Nifty reflects market price variability/volatility
appropriately. Alternatively, Nifty trading is subject to manoeuvrings by bulk buying
and selling the heavy weight stock; thus empowering the manipulators to drive the
market.
The intra-day fluctuations are registered electronically from second to second. They
show a fresh calculation of the market index every time a trade takes place for an
index component. Most of the time, more than one trade takes place in a given
second, so multiple records are found for the same second. Hence, we often see days
where there are more than 100,000 observations for Nifty. The records are registered
on the screen in correct time-sorted order, even though it appears that they all have the
same timestamp.
162
Thus electronic trading has not only made the data available almost instantaneously
but transactions are totally transparent. Anyone can have access to them and, it is also
possible to know which broking house is trading what to what extent. This can give a
lot of insight into market manipulations. Legally they stay as movements bui can well
be seen as a syndicated well orchestrated manipulation.
Constitution ofNSE and the product Nifty has given the researchers a novel and well
documented tool for study. NSE archives store and supply enormous data. Nifty is a
consolidated value stock of 50 stocks that is being traded as a stock. Thereby it is a
reliable tool for studying the market movements.
We have therefore taken movements of Nifty as the parameter for the study. BSE
figures are also mentioned concurrently.
2.2.9 Sampling method and Sample Size:
Sample is a subset of the population drawn to collect data, whereas sampling is the
process of drawing a sample from population. Sampling can be broadly divided into:
Probability, Non-Probability and Mixed Sampling. Some of the commonly used non
probability sampling methods are: Purposive sampling, judgmental sampling and
convenience sampling. In this study we have utilised convenience sampling and a
sample of 498 trading sessions has been selected to understand the volatility and their
causes on that particular day.
2.2.10 Presentation of the data
The day to day figures ofNSE and BSE were obtained.
2.2.10.1 Sam pie Design
The samples were obtained by sitting face to face with the NSE screen on the Internet
on all the working days of the study. Two parameters were applied:
163
I. Figures for the close of the prevIous day were compared with the close of
present day to determine the percentage variation in prices from day to day.
2. Figures for the opening. high of the day, low of the day and close of the day
were obtained and co-efficient of variation was obtained for them.
• Whenever the change of I % or more of the two consecutive days closing prices
was found occurring, the day when the change was noted was considered
volatile trom day to day; and,
• Whenever the coefficient of variation was found to be more than I for the
intraday trading. that day was considered as having intra-day volatility;
• The figures were collated with the factors that cause feedback trading.
2.2.10.2 Sample Size
No of trading days studied: 498 trading days between 1st July 2006 to 30th June 2008
2.2.11 Geographical Area
The Nifty is the bench mark index of the NSE and quotes of the Nifty taken pertaining
to India.
164
2.2.12 TABLE: SUMMARY OF RESEARCH DESIGN
RESEARCH TOPICITITLE Stock Market Behavior following Liberalization of
Economy (1991-2006) with focalization on Volatility,
Market Manipulation and Cyclic Nature: Causes,
effects and remedies
RESEARCH APPROACH Descriptive
RESEARCH METHOD Convenience Sampling
SAMPLE DESIGN Convenience Sampling techniques
SIZE OF THE SAMPLE 498 trading days of the National Stock Exchange
SOURCE OF DATA Collected from NSE
COLLECTION
GEOGRAPHICAL AREA India
TYPE OF DATA Secondary
TIME SPAN l;t July 2006 to 30to June 2008
SOURCES OF Research Journals, Magazines, Websites, Research
SECONDARY DATA Reports etc.
STATISTICAL Co-efticient of Variation
MEASURES
DATA DISPLAY Narrative, Text, Graphical Displays, Tables
165
2.3 Defining Volatility
2.3.1 Introduction to Volatility
The study was inspired by the huge volatility and established manipulation of the
market from 1991-2006. During this period. volatility up to 20% on a single day in
some stocks was observed on several occasions.
The volatility is well paraphrased. Volatility is defined as changeability or randomness
- fluctuations - of asset prices over a given time frame. It is measured either in terms of
percentage variations in the prices during the study period or, variations in the rates of
returns. Most oftbe published studies rely on the differences in tbe opening and closing
prices over the time frames adopted and study variations in the rates of returns to
measure volatility. For the short period like intraday or day to day, it is absurd to
study rates of return. Percentage variations in the prices have therefore been
taken as the rational criteria for voillfility.
When tbe study began. there was no bench mark for volatility on Nifty. (It is pertinent
to note that SEBI also found volatility studies of importance and introduced VIX -
volatility index from April 2008). In US markets. VIX was introduced in 1993 and is
a traded product. The VlX, introduced by the Chicago Board Options Exchange in
1993, was a weighted measure of the implied volatility of eight S&P 100 at-the
money put and call options. Ten years later. it expanded to use options based on a
broader index, the S&P 500, which allows for a more accurate view of investor
expectations on future market volatility. VIX values greater than 30 are generally
associated with a large amount of volatility as a result of investor fear or uncertainty,
while values below 20 generally correspond to less stressful, even complacent, times
in the markets.
The VlX attempts to predict the volatility of tbe S&P 500 index over the next 30
trading days using options data from the index's 500 underlying stocks. Specifically,
the VIX is a weighted average of the implied volatilities from a large basket of
options. That basically means that it's a cumulative index of uncertainty.
166
The VIX is interpreted as a measure of volatility in a certain sense, but it more
accurately measures fear rather than volatility. That is why the VIX is important to the
average investor: it is a sign of uncertainty. So a fall ing VIX may portend better times
ahead.
The volatility index is an index which measures expectations of volatility, or
fluctuations in price, of the S & P 500 index in US. Higher values for the volatility
index indicate that investors expect the value of the S&P 500 to fluctuate wildly - up,
down, or both - in the next 30 days. VIX depends on investor's fear of a further
decline in stock prices. Even if the VIX continues to fall, that does not mean that high
volatility for stocks is finished. Investors should still hope for volatility, realized from
a recovering rally. But a low VlX - signalling reduced uncertainty - would likely
signal a coming rally, rather than stagnation in prices.
To illustrate the point, two charts are produced below: one before the beginning of the
study and the other at the time of submission. They speak for themselves.
VOLATILITY AT 20-YEAR LOVY'S
45.---n---------------------~------~1-------,
40
35
30
25
20
15
10
VIi: INDEX, wI1I1 • 50-day movmg average 'in black)
j.
I,
5+---~--~--_r--~--~--~----~--~--~--~ 1966 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
Volatility soared during the Crash of 1987. It jumped when Iraq invaded Kuwait a few years later. It jumped during the Asian crisis in late 1997, and after the crash of the LTCM hedge fund in 1988. It jumped up after September lIth, 2001. Volatility in the stock market soars after major uncertainty appears
167
VIX From April 8, 2004 through April g, 2009 .. so
70 .. .. '0
"" ~
10
0 .. ~ .. .. .. .. ~ ~ .. ... " .. " 'C 1:- -' .. , 8 " ~ ~ 'C 1:- 8 ~ .. ,
" ~
TIME "J,', I
Note: The period of study finishes at the end of June 2008, Yet the chart is produced
in its entirety for the sake of relevance.
As noted before it is pertinent to note that SEBI also found volatility studies of
impol1ance and introduced VIX - volatility index from April 2008. SEBI and NSE
issued the following press release:
NSE has been in the forefront of bringing the latcst products and services to the Indian capital markets
for the benefit of the investors. In another innovation in the Indian markets, NSE is pleased to
announce the launch of India VIX, a volatility index based on the Nifty 50 Options prices.
Over the last decauc or so, there has been a paradigm shift in the Indian capital markets, The Indian
markets are no longer isolated from the global economic events. We have witnessed bouts of volatility
in our markets, some of which may have their origin in global events. The recent subprime crisis and
nev,'s of probable recession emerging from the U.S., is an example of how events which are
international, can be a cause ofvolJtility in our markcts. Events both domestic and international playa
role in affecting the volatility of stocks. lntlation ratC$, global energy prices, exchange rate fluctuations
etc. are witnessing constant changes in the recent years, These arC affecting the volatility of the
markets.
A Volatility Index renects the markets expectation of volatility over the near term. The index captures
the implied volatility embedded in option prices. Volatility is often described as the "rate and
magnitUde of changes in prices" and. in. finance ollen referred to as risk. Volatility Index is a measure,
of the amount by which an underlying Index is expected to lluetuate, in the near term, (calculated as
annualised volatility, denoted in percentage c.g.,20%) based on the order book of the underlying index
168
options. Market volatility keeps changing as new information tlows into the market. It would be
imperative for market participants to have an index designed to track market volatility.
The India VIX is a simple but useful tool in determining the overall volatility of the market. Not only is
the volatility index used as an indicator of implied volatility of the market, various tradable products,
such as futures and options contracts are available on the volatility index internationally. There is no
intention to introduce tradable products based un the India VIX in the immediate future. It is important
that the market participants get used to understanding and tracking the India VIX number and what it
signifies.
Presently. India VIX vVDuld be calculated for the entire day and made available at the end of the day, on
the website of NSE (v\"\vw.nseindia.com). Subsequently, the index would move to on-line
dissemination.
Thus, the isslies of volatility and risk have become increasingly important in recent
times to financial practitioners, market participants, regulators and researchers.
Volatility estimation is important for several reasons and for different people in the
market. Pricing of securities is supposed to be dependent on the volatility of each
asset. Mature markets/developed markets continue to provide over a long period of
time a high return with low volatility. Amongst emerging markets, except India and
China all countries exhibited low returns (sometimes negative returns) with high
volatility. India with a long history and China with a short history, both provide as
high a return as the US and UK markets could provide but the volatility in both
countries is higher. Indian markets have started becoming information-efficient.
Volatility is both the boon and bane of all traders - you cannot live with it and you
cannot really trade without it. Volatility is commonly perceived as: "choppy" markets
and wide price swings. These basic concepts are accurate, but they also lack nuance.
Volati lity is simply a measllre of the degree of price movement in a stock, futures
contract or any other markets.
Volatility most frequently refers to the standard deviation of the change in value of a
financial instrument with a specific time horizon. It is often used to quantify the risk
of the instrument over that period. Volatility is typically expressed in annualized
terms, and it may be either an absolute number or a fraction of the initial value. In
169
financial terms, volatility is the degree to which the price of a security, commodity, or
market rises or falls within a short-term period. There are several points to note about
this definition. Most importantly, the definition specifically mentions price increases
and decreases. People are usually most concerned about volatility during periods
when prices decrease or go through a "correction". In addition, most people use
volatility and risk interchangeably.
2.3.2 Volatility in stock markets
2.3.2.1 Stock volatility89
It is the relative rate at which the price of a security moves up and down. Volatility
is found by calculating the annualized standard deviation of daily change in price. If
the price of a stock moves up and down rapidly over short time periods, it has high
volatility. If the price hardly changes, it has low volatility.
A variable in option-pricing formulas shows the extent to which the return of the
underlying asset will fluctuate between now and the options expiration. Volatility, as
expressed as a percentage coefficient within option-pricing formulas, arises from
daily trading activities.
How volatility is measured will affect the value of the coefficient used.
2.3.2.2 About high and low volatility
In other words, volatility of the stock refers to the amount of uncertainty or risk
about the size of changes in a security's value. A higher volatility means that a
security's value can potentially be spread out over a larger range of values. This
means that the price of the security can change dramatically over a short time period
in either direction. Whereas a lower volatility would mean that a security's value
does not fluctuate dramatically, but changes in value at a steady pace over a period
oftime.
170
One measure of the relative volatility ofa particular stock to the market is its beta. A
beta approximates the overall volatility of security's returns against the returns of a
relevant benchmark (usually the S&P is used). For example, a stock with a beta
value of 1.1 has historically moved 110% for every 100% move in the benchmark,
based on price level. Conversely, a stock with a beta of .9 has historically moved
90% for every 100% move in the underlying index.
2.3.2.3 More about volatile markets
During volatile times, many investors get spooked and begin to question their
investment strategies. This is especially true for novice investors, who can often be
tempted to pull out of the market altogether and wait on the sidelines until it seems
safe to dive back in. The thing to realize is that market volatility is inevitable. Its the
nature of the markets to move up and down over the short term.
Trying to time the market over the short term is extremely difficult. One solution is
to maintain a long-tenn horizon and ignore the short-term fluctuations. For many
investors this is a solid strategy, but even long-term investors should know about
volatile markets and the steps that can help them weather this volatility.
2.3.2.4 Dealing with volatility
One way to deal with volatility is to avoid it altogether. This means staying invested
and not paying attention to the short-term fluctuations. One common misconception
about a buy-and-hold strategy is that holding a stock for 20 years is what will make
you money, provided you find a company with a strong balance sheet and consistent
earnings, the short-term fluctuations won't affect the long-term value of the company.
In fact, periods of volatility could be a great time to buy if you believe a company is
good for the long-term.
l7l
2.3.3 Measuring volatility of a Stock: ATR90
The concept of awrage true range. cOlllmonly referred to as ATR. is a measure of a
security's volatility. The true range of a security for any given day is the greatest of
the following three distances:
*
* *
The distance Ii'om yesterday's close to today's high
The distance from yesterday's close to today's low
The distance ("rolll today's high to today's low
The average true range is a moving average of the true ranges. In order to use ATR
effectively. an investor needs to ensure that a sufficient sample is taken. For example,
obtaining a two day ATR or ATR (2) is not sut1icicnt to provide him with a
reasonable indication of that security's normal daily movement. Whereas using at
least 10 days in the average calculation. or an ATR (10) would provide him an
inuication of that security's daily movement over the last 10 trading days (2 weeks).
The ATR is usually expressed as ATR (X) where X is the number of days used in the
calculation of the moving average. T'he number of periods selected to obtain the
average would depend on his application.
One application of ATR is that they can be uscd quite effectively for setting exits, or
stops. Using A TR for exits allows the investor to tailor the stop loss to the sccurity
you arc trading. For example. if he used a standard 10% stop. this would be a tighter
stop (i.e. closer) for some securitics than lor others. If a security moves 5% a day on
average. then a 10% stop would be tighter than for a security that only moves I y,% a
day on average. Using A TR can alleviate this situation.
To use A TR lor exits, an investor would normally use a multiple of the A TR to ensure
a sufficient gap between his exit and the security's normal price movement.
Theretore, using the ATR without any modification would have his stop too close to
the price and would not allow the security he is are trading sut1icient room to move
and behave naturally. Depending on his trading style, he would normally consider
using something in the ordcr of 2 - 3.5 multiplied by the ATR as a suitable trailing
172
exit. If he used a 2.5 II TR stop. then his trailing stop will always be 2.5 times the
ATR below the highest price the security has reached since he entered the trade.
Another application of A TR is to loosely categorise securities as blue chips, mid
capitalisation (mid-caps) or speculative companies. This concept is called Volatility
Perccntage. The calculation that is uscd is to take the ATR over the last 20 days and
divide that by the closing price of the share and then mUltiply by 100 to determine a
volatility percentage. The result will be an indication of what percentage the share
moves on average on a daily basis. As a guide, he will discover that most mid-cap and
blue chip companies have a volatility percentage of under 4%. and anything above 5%
is nonnally speculative. A value or under 1.5% indicates that it may be a propelty
trust or a security that olTers little potential lor short to medium term gains.
Investors need to be aware of the potential risks during times of stock volatility.
Choosing to stay invested can be a great option if one is confident of his strategy. An
investor or trader needs to have a strategy to trade during volatility, be aware of how
the market conditions will affect the trade. So making a study of the volatility carries
a great importance in gaining in stock market.
2.3.4 Stock Market: Volatility and Manipulation
Volatility and manipulation are intrinsic to the market. The players are in the market
to make money and not to practice morals. However, the order in a system is also
intrinsically a must so as to set the rules by which the game is played: then only the
game can go on. Finance and the stocks are the pieces on the chessboard of the stock
market and the game is played with the help of both. Both volatility and
manipulations are likely to increase when more lenient the rules and more difficult to
enforce them and, less transparency to detect the violations. Even thereafter, legal
deterrent and punishment subsequent to fraud should have a role, though it is seen in a
very few instances only. Therefore, reflections on these aspects are better made with a
watershed: before globalisation and after it.
Roy and Karmarkar91 (as cited earlier) surveyed volatility during the years 1935-92.
The retrospective study rightly focllsed on the year-wise and decade-wise volatility
173
and focussed more on the events; the derivations are also of the nature of historical
wisdom over a span of 58 years. This period witnessed many events, big and smail
directly or indirectly, related to stock market activities. Hence, an assessment based
on an analysis of this period can be truly called as an average measure of volatility of
the stock market of those times. Following the liberalisation of economy, stock
markets underwent transformation to the global scale. This period should be
considered, therefore, separately.
2.3.5 Stock Market Volatility with emphasis on News-Centric events
Stock market performance has emerged as one of the most visible and self-titrating
parameter of the economy that pulsates from moment to moment. An average investor
has little understanding of the play in the market; he is there to park his savings for a
better yield. Inflation being part of most developing economies, a middle class investor
tries to hedge the inflation by maximizing returns through investment in equity. The
investor puts in the money with an eye on the dividends and, mainly on appreciation
that should result from the synergistic application of capital, labour and productivity.
So his savings should mUltiply. But the logic goes only thus far.
The prices of the stocks are influenced by an enormous numbers of factors with which
a very large numbers of players fiddle. While every player in the stock market is there
for making money, the only common factor for their style of play is motivation. The
players range from highly professional and skilled, to utterly illiterate ones. Both the
types can be there in the market and they may be with large capital. And then, not
every professional makes it to riches and though most fools crash. lucky ones get
away with a pot full of cash. This is because, while stock market is a good parameter
of the state of economy of a country, its volatile behaviour is more a matter of
emotions, hunches, predictions and manipUlations rather than the state of economy at
a particular day, or more precisely at a particular moment. Thus, though wealth is
generated through production by fanners and workers in the industry with wise
application of financial policies to the economy and. so the stock market transactions
should follow simple rules of inductive logic; it is seen to be functioning on
anticipation and may be manipulations. At the bottom line is the basic principle of
demand and supply that titrates the price.
174
Indian bourses have witnessed high volati lity during the period 1991-2006. It may be
thought that global political events, fluctuations in the interest rates and global inflation
would be main determinants ofthis phenomenon. For instance, on September 12,2000,
in the aftermath of terrorist attacks in the U.S., equities in Indian bourses too bore the
brunt and sentiment was affected. But this is only partly true and markets are subject to
clever manipulations to the extent that intra-day fluctuations in the range of 1000
points on the Sensex have been seen several times for unaccountable reasons.
It emerged from reflections that there seems to be a logic in the market movements
that revolves around feedback trading. A major factor was evident. It was the inflows
and outflows offunds from abroad. This is substantiated as follows:
Foreign institutional investors (FII) have come to play a major role following
liberalisation of economy since they are major investors in large cap companies.
Some of them have huge finance available at their disposal - as much as annual
revenue in Indian Government budget - and they fan and fuel the market with this
money; that is hot money. By taxing short term trading gains at the rate of 10% only,
when the trade may have occurred only a day after, the Finance Minister has willingly
created this situation.
Volatility, thus, has been studied in sparse detail; there are just a few studies over a
large time span of the Indian markets92• The period ranges from decade to decade,
year to year, month to month etc. there are enough evidences of using monthly share
price index to measure volatility and the findings show that, in the long run, both
monthly and daily share price indices reveal identical trend (Schwert, 1989)93. To ollr
knowledge, post 2000 in India, there is no study as yet which has included day to day
variations and collating thelll with the causative Jactors. Day to day variations are the
basis on which a large number oj day traders operate.
Volatility is variously researched by researchers using different parameters.
2.3.6 Calculating Volatility94
I. Measure the day-to-day price changes in a market.
175
Calculate the natural log of the ratio (Rt) of a stocks price (S) from the
current day (t) to the previolls day (t-l):
2. Calculate the average day-to-day changes over a certain period.
Add together all the changes for a given period (n) and calculate an
average for them (Rm).
3. Find out how far price vary from the average calculated in Step 2.
2.3.6.1
The historical volatility (HY) is the "average variance" from the mean (the
"standard deviation"), and is estimated as:
Parkinson's model
Parkinson's (1980)95 model, which uses intra-day highs and lows, is used for the
estimation of intra-day volatility. Following is the Parkinson model. This volatility
measure is referred to as high-low volatility. The use factor of 0.601 scales down
volatility although, statistically. it is correct. Therefore, in order to provide additional
information on intra-day (high-low) volatility it was computed K=l also.
High-low volatility conveys extreme movements and dispersion during the trade time.
A very high high-low volatility is likely to scare investors and lead sometimes to
176
panic conditions in the market place. Therefore, regulators and policy makers strive to
implement policies that smoothen the information flow and they ensure certain
measures, which in turn ensure bounded extremes with the help of circuit breakers,
exposure limit, margin etc.
2.3.6.2 Historical Volatility measure
I) Open-Open
2) Close-Close
Open-to-open volatility is very important for several of the participants. High open-to
open volatility reveals informational asymmetry and overflow of infonnation. Any
positive or negative information that comes after the close of the market and before
the start of the next day's trading is expected to get reflected in the opening prices of
shares and on the index. Significant economic and socia-political developments
induce price movements and the extent of price movement depends on severity of
information.
2.3.6.3 Garman & Klass model
The Garman and Klass (1980)96 estimator, which uses four intra-day variation
statistics of open, high, low and close, is Llsed for the calculations. The following
model is used for this estimator.
---
J ""I" L (1/2) [loj('1 t /1l)Y-G IG',c' 1 - 1J~"1 ( ,cIDJ Where HI. Lt, Ct, and Ot, denote intra-day high, low, close and open respectively.
177
2.3.6.4 Open-low-high-c1ose volatility
It provides information on change of the prices during the day. Volatility is a function
of length of time: the longer the trading hour, the higher the expected volatility. This
is important mainly for India as the trading hours increased over a period. In the open
out-cry system, the market was open for about two hours. Later on the number of
trading hours was extended. With the implementation of computer screen-based
trading, the number of trading hours has been further enhanced.
2.3.6.5 Measurement by Point Changes
The perception that prices move a lot - and have been moving a lot more in recent
years - is in part merely a reflection of the historically high levels of popular
indexes97• Perception of both the public and the press about stock market volatility is
generally based on point changes. The point changes invariably overestimate and
create a false impression regarding the magnitude of volatility among the investors.
This cannot give comparative data for different periods with variable levels of index.
So the point movements are thus only psychological. They do not reflect the real
change. Percentages are therefore a better parameter.
2.3.6.6 NAV
Another tool is to go by the asset value fluctuations (NA V). It is more pertinent to the
study of mutual funds. However, even there, closed-ended funds frequently trade at a
discount to net asset value.
2.3.6.7 Rate of return vis-a- vis price
The rate of return from buying and holding a stock depends upon the price at the time
of purchase, the holding period, the total dividend payments received during this
period and the price at the end of the holding period98• This is a widely accepted
concept. It is based on rates of return. That is the logarithmic difference of prices of
two successive periods. This is fine tuned in econometrics as various GARCH
models. An autoregressive conditional heteroscedasticity (ARCH), model considers
178
the variance of the current error term V to be a function of the variances of the previous
time periods error terms. ARCH relates the error variance to the square of a previous
period's error. It is employed commonly in modelling financial time series that
exhibit time-varying volatility clustering, i.e. periods of swings followed by periods of
relative calm. Such studies can turn into spinning the fine yarn even finer. Apart from
the Vanilla Model of GARCH, there are several models and each has its limitations
and advantages.
2.3.7 Volatility: Time frames
Changeability or randomness - Ouctuations - of asset prices of necessity takes into
account a particular period of time. That defines volatility. It is also defined as a
measure of the dispersion of possible returns from tinancial asset over a period of
time99• Based on the previously declared dividend. it refers to the standard deviation
f .. ~ H' d K 101) • • o returns over vanous tllne ,rames. arvlll er aur mentIOns vanous parameters
of measurement of volatility by various workers as
• Markowitz expected variance (1952)
Risk has been defined as the standard deviation of stock returns
• Officer (1973)
Moving standard deviation of 12 months of monthly data around 12 months
arithmetic means
• French, Schwert and Stanbaugh (1987)
Daily percentage stock price changes to measure the monthly standard deviation
• Schwert (1989)
Emphasized that the percentage variation is more appropriate than point variation
(followed in present study)
v Statistical errors and residuals are t\\'Q closely related and casily confused measures of "deviation of a sample from the mean": the error of a sample is the deviation of the sample from the (unobservable) population mean or actual function. while the resid1lal of a sample is the difference between the sample and the (observed) sample mean or regressed (fitted) function.
179
• Harvinder Kaurs method
The rate of return based on change in day to price divided by the price of investment
at the beginning. The formula works out as
Rt + pt - pt-I
Pt- I
Where rate of return for the period t and pt and pt-I are the beginning and closing
prices for the successive periods t I and t.
For considering the rate of return, dividend yield of the last year is taken into account
and price earnings ratios are generated. Most studies on volatility are so modelled.
They indicate the volatile stocks in particular and, therefore likely to be manipulated
and involve high risk.
The fine tuned studies as described above turn out to be futile in presence of master
manipulators (ACC script in case of Harshad Mehta and K-IO scripts of Ketan
Parekh, such unnamed instances abound in the market). The manipulators select a
few scripts. Such a study based on volatility parameters as mentioned will not reflect
market trend as a whole; particularly before the introduction of Sensex and Nifty.
Wide fluctuations from day to day prices have been witnessed in the Indian stock
markets as well as globally therefore, the present study has focussed on this.
Obviously, the yield in terms of dividend earned becomes the least important
parameter since this can account for the day of the declaration of the dividend only.
Moreover study of one or a few scripts will not reflect holistically the entire market.
The above is mentioned as a review of the methods employed by various researchers.
The present study is different. simple and novel:
1. More than one percent fluctuation in the opening and closing price - day
to day,
2. Coeflicient of Variation of more than 1 for intraday fluctuations.
180
-_._------------------------
Even one percent change in NIFTY reflects a change in market capitalization
amounting to several thousand crores. It will cover a span of fifty large cap stocks.
Therefore it is decided that percentile change of more than 1% in Nifty is an
appropriate parameter for present study.
2.3.8 Operational Definition of the Present study
The present study was taken up since this has been sparsely studied so far to our
knowledge (Shah Ash and G. Omkarnath'U', Singh, Kapoor and Babbarvi, 102).
In her magnum opus, "The Indian Financial System", 13harati Pathak103 provides a
simpler and more appropriate definition of volatility. She defined volatility as
measurement of frequency with which changes in the market price take place over a
period of time. The present study relies on this definition since the study has main
focus on day to day volatility. Titrating intra-day fluctuations vis a vis dividend yields
and returns is apparently meaningless. Triggers for intra-day fluctuations are not the
dividend yields but should be looked elsewhere. Therefore, it was decided that
volatility should be measured in terms of percentage changes in prices. Thus, the
present study relies on percentage changes and not on point changes of prices.
The present study considers a day as volatile market day when the percentage
variation for the two consecutive closing prices was more than one percent and, when
the coefficient of variation of intraday movement was more than I. The rationale
behind the criteria (defined for the first time in the present study) was like this.
Intraday volatility is utilised by day traders - punters as they are derogatively called
sometimes; though they consider themselves the real farmers of the market. The basis
of intraday trade is that the brokers charge 0.2% commission. This added to the
service charge of 0.01 % will leave a profit on day to day trade in the hands of a day
time trader if he happens to tick on the lowest buy and highest sell prices on a
particular day.
VI This study is singular in n:spcct of study or Jaily volatility and employs the percentage price fluctuation at BSE. It is futuristic too. since it aimed at predicting the movements following the year of study. However, it is rctrospecti\'e for having collected the data from records on PROWESS provided by Centre for Monitoring Indian Economy. Our study differs since we have done study on Nifty as a stock progressing from day to day.
181
That the punters in fact actually look forward to exercise such volatile moments is
what limits and, what causes volatility. This is the basis on which the charts perform.
Thus the present study has fixed that more than one percent variation in two
consecutive closing prices for the day and, coefficient of variation of more than 1
on a single day prices should be considered volatile.
It is seen frequently during the study period that there were variations ranging from
2- 8% during the trading in a single day but the index may close flat as it opened: this
will not reflect any volatility when reviewed on a day to day basis later on. In this
study, we have also taken into consideration the percentage variations during intra
day movement of index. If the Nitty moved more than 1 % from the opening and close
of the market, we have considered that trading day as volatile day and tried to collate
the global occurrences as well as market forces at play on that particular day with
volatility. While clear indications emerged to ascribe cause and effect, it has not been
possible to do so all the times.
2.3.9 Market Manipulation
It came to be legally established that at least two mavericks, Harshad Mehta and
Ketan Parekh could manipulate the markets phenomenally. In May 2006, there were
three major falls on 15, 19 and 22nd• There were lower circuits hit on the BSE and
NSE in October 2007 and January 2008 too. This has greatly affected the investors
and had social repercussions too. The study was undertaken to analyze causation of
volatility and possibility of market manipulation. Preliminary review of the literature
showed scant data regarding market manipUlation pertaining to Indian markets. The
present study defines market manipulation as follows:
Market manipulation'04 describes a deliberate attempt to interfere with the free and
fair operation of the market and create artificial, false or misleading appearances with
respect to the price of, or market for, a security, commodity or currency. Market
manipUlation is prohibited in the United States under Section 9(a)(2)'05) of the
Securities Exchange Act of 1934, and in Australia under Section s 1041A of the
182
Corporations Act 2001. The Act defines market manipulation as transactions which
create an artificial price or maintain an artificial price for a tradable security.
Market manipulation can occur in multiple ways:
Pools
• "Agreements, olien written, among a group of traders to delegate authority to
a single manager to trade in a specific stock for a specific period of time and
then to share in the resulting profits or losses."lo6
Churning
• "When a trader places both buy and sell orders at about the same price. The
increase in activity is intended to attract additional investors, and increase the
price. 1I
Runs
• "When a group of traders create activity or rumours in order to drive the price
of a security up." An example is the Guinness share-trading fraud of the
1980s. In the US, this activity is usually referred to as painting the tape.
Ramping (the market)
• "Actions designed to artificially raise the market price of listed securities and
to give the impression of voluminous trading, in order to make a quick profit.
Wash trade
• "Selling and repurchasing the same or substantially the same security for the
purpose of generating activity and increasing the price"
Bear raid
• "Attempting to push the price of a stock down by heavy selling or short
selling."lo,
183
2.3.10 Summary
The prediction of volatility in financial markets has been of immense interest to
financials econometricians. During the past few years. the Indian Capital market has
undergone metamorphic reforms. Every segment of the market, viz primary and
secondary markets, derivatives, institutional investment and market intermediation,
has experienced the impact of these changes. Our market, today, is being recognized
as one of the most transparent, efficient and clean markets. Academicians, policy
makers, practitioners and investors, to test the extent of efficiency of the market, use
several techniques I instruments. We have outlined the operational definition of
volatility and market manipulation for this study in this point.
184
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