Overconfidence And Trading Volume From Karachi Stock Exchange
The research study is proposed to test the proposition using KSE data that high observed market returns make investors overconfident about their abilities of trading and valuation and as a consequence, they subsequently trade more. Relationship between trading volume and market return will studied using vector autoregressive (VAR) model and impulse response function. A positive correlation between market return and trading volume is expected which is argued as presence of overconfidence in prior studies.
Modern Portfolio Theory (MPT) and the Efficient Market Hypothesis (EMH) are the two pillars of the standard finance. In 1952, while a doctoral candidate at the University of Chicago Harry Markowitz created modern portfolio theory (MPT) and associated the yield or return on an investment with the expected value or probability-weighted mean value of its possible outcomes and its risk with the variance or squared deviation of those outcomes around the mean (expected value). Stock or portfolio’s expected return, standard deviation, and its correlation with the other stocks or mutual funds held within the portfolio constitute the MPT. An efficient portfolio can be created using these three concepts for any group of stocks or bonds. An efficient portfolio is a group of stocks that has the highest expected return for given level of risk assumed, or, contrarily, contains the minimum possible risk for a given level of expected return. The Efficient Market Hypothesis (EMH), the other pillar, has contributed much to shape the development of the field of standard finance since last thirty years. The EMH holds the premise that a security price or market value reflects all the available information, and that the current price the stock or bond is trading for today is its fair value. Since stocks are considered to be at their fair value and only new information can change the price, proponents argue that active traders or portfolio managers cannot outperform the market in term of returns over time, see Ricciardi and Simon 2000. In other words simple rule based on already published and available information cannot generate abnormal returns, see Miller 1999. The EMH argues that competition among investors to capitalize the abnormal profits in the market drives prices to their “correct” value. The EMH avoid assuming that all investors are rational but it does assume that markets are rational. Further the EMH does not assume that markets can foresee the future, but it does assume that markets make unbiased forecasts of the future, see Ritter 2003. Implication of EMH is that all the participants in the market seeking profits make unbiased decisions about their investment and buy-and-hold strategy is the best strategy in the market. Also technical and fundamental analysis will unable to help investors earn abnormal profits because asset prices are already reflecting their fundamental values that are based on future cash flows. Any deviation from this value must trigger an immediate reaction from rational traders which will lead to rapid disappearance of the mispricing, see Konte 2010. Moreover, if some participants succeed to earn excess returns that would be purely a chance game which may also have excess risk. However, contrary to EMH there are some empirical evidences that contradict the implications of efficient market hypothesis. De Bondt and Thaler (1985, 1987) identified overreaction of investors and reversal effects whereas Jegadeesh and Titman (1993) show the momentum effects. On the other side, deviation can occur in asset prices from no arbitrage values. Some stocks are over-valued or under-valued in the past. Internet stocks are often cited as an example of overpricing, see Ofek and Richardson 2003. Such anomalies (abnormal behavior) sometimes continue to persist in financial markets before disappearing. A fundamental question arises here is that how to explain these anomalies. Attempts to answer this question give behavioral finance room to flourish after the identification of overreaction in financial markets by Thaler 1985 and subsequently Black Monday crash in October 1987.
Ricciardi and Simon  define Behavioral finance in the following way, “Behavioral finance is a science that strives to give explanation and improve insight into the overall judgment process of investors. This includes the cognitive biases and the affective (emotional) aspects of the decision-making process of novice and expert investors.” Shefrin (2000) makes the distinction between cognitive and affective (emotional) factors, “cognitive aspects concern the way people organize their information, while the emotional aspects deal with the way people feel as they register information”. The foundation of behavioral finance is an area based on an interdisciplinary approach including social sciences and business studies. It includes the fields of psychology, sociology, anthropology, economics and behavioral economics from the social perspective while it wraps up management, marketing, finance, accounting and technology from the business administration side. Specifically saying, behavioral finance has two building blocks: Cognitive psychology and limits to arbitrage. Cognitive psychology refers to how people think and behave 1) when they process information and 2) when they take financial decisions. Psychological literature shows that people make systematic errors when they think: they weigh recent experience too much, they are overconfident, they are small sample bias, etc. Their preferences may also distort the financial decisions. Limits to arbitrage refers to guessing in what situations arbitrage forces will be effective, and when they won't be. There are certain forces that may limit the arbitrage process such as fundamental risk and implementation costs.
Numerous patterns regarding how people behave have been documented by cognitive psychologists. Some of these patterns include: Heuristics, Overconfidence, Framing, Mental Accounting, Disposition effect, conservatism and Representativeness. The pattern going to be studied in this particular research project is overconfidence. Mahajan (1992) defines the overconfidence in the following way “an overestimation of the probabilities for a set of events. Operationally, it is reflected by comparing whether the specific probability assigned is greater than the portion that is correct for all assessments assigned that given probability.” The subject of overconfidence continues to have considerable presence in both the areas of psychology and behavioral finance. As investors, people have a natural ability to overlook or fail to learn from their past errors (known as financial cognitive dissonance), such as a bad investment or financial decision. Overconfidence dilemma compounds when people fail to learn from their past investment decisions.
Can overconfidence hypothesis (Investors are overconfident about their abilities of valuation and trading) explain the high trading volume in emergent Karachi Stock Exchange (KSE)?
Purpose of the Study
The aim of the research project is to study the impact of overconfidence phenomenon on the trading volume and its significance in the formation of the excess volume on the Karachi stock Exchange (KSE).
Odean (1998), and then Gervais and Odean (2001) developed a multi-period model where the overconfidence of noise traders (irrational traders) increases as they attribute high returns in bull markets to their trading skills. Many other studies shed light on the proposition that investor overconfidence generate the high trading volume in financial markets e.g De Bondt and Thaler, (1995), Odean (1998a, 1998b, 1999), Barberis and Thaler (2003) and Statman, Thorley and Vorkink (2006). These studies predict that overconfident investors trade more than the rational investors. De Bondt and Thaler (1995) argue that “the key behavioral factor needed to understand the trading puzzle is overconfidence”. Overconfident investors overestimate their valuation abilities and precision, they give more weight and tend to overestimate the accuracy of their private information signals (Daniel, Hiershleifer and Subrahmanyam,1998; Gervais and Odean, 2001).
As pointed out by (Zaiane and Abaoub (2009), there are two assumptions under which researchers have developed theory and testable implications: 1) that investors are overconfident about the precision and accuracy of their private information 2) that the degree of overconfidence vary with realized market outcomes due to biased self attribution. Deaves, Luders and Luo (2008) conclude that “Overconfidence appears to be a multi-faceted dynamic phenomenon, and it is not clear how to best measure it.” Glaser and Weber (2007) indicated that overconfidence can manifest itself in four facets: 1) miscalibration (Lichtenstein and et al., 1982; Yate, 1990; Keren, 1991; Mcclelland and Bolger, 1994), 2) better than average (Svenson, 1981; Taylor and Brown, 1988), 3) illusion of control (Langer, 1975; Presson and Benassi, 1998) and, 4) unrealistic optimism (Weinstein, 1980). Deaves, Luders and Luo (2008) show that the calibration technique most closely conforms to the new overconfidence models. There is little difference in the implications of trading patterns between the better than average version of overconfidence and miscalibration one (Statman, Thorley and Vorkink, 2006). I will not differentiate between them in my study and will stick to prior studies that model overconfidence as the idea that investors overestimate their private information signals.
Statman, Thorley and Vorkink (2006) argue that as overconfidence encourages investors to trade asymmetrically between gains and losses so it works as a driver of the disposition effect (selling winners too early and carrying losers too long),. We can differentiate between overconfidence and disposition effect in the following two ways. First, the disposition effect refers to an investor’s attitude towards a specific stock in the portfolio (Odean, 1998b; Dhar and Zhu, 2002). However, overconfidence affects the stock market in general. Second, the disposition effect explains the incentive for only one side of a trade contrary to overconfidence that can explain both sides of a given transaction.
Various studies done on developed markets predict a link between current volume and lagged returns (Statman, Thorley and Vorkink, 2006; Chuang and Lee, 2006; Glaser and Weber, 2007), but, Nardi and Stulz (2007) and Zaiane and Abaoub (2009) find little evidence in emergent markets. In addition, emerging markets are significantly smaller and less liquid than developed markets. This fact can play an important role in determining the relationship between stock returns and trading volume; it can potentially alter the previous findings of the developed markets (Pisedtasalasai and Gunasekarage, 2006).
To what extent overconfidence is correlated with trading volume in KSE?
Is there any relationship between trading volume and past returns in KSE?
Ho: There is no relationship between past return and trading volume.
H1: Past returns and trading volume are positively correlated.
Database will consist of monthly observations from Karachi Stock Exchange from January 2000 to December 2009. Monthly observations for trading volume and returns will be used. Reason for using monthly data is consistent with the argument that changes in investor overconfidence occur over monthly or annual horizons (Odean, 1998; Gervais and Odean, 2001; Statman, Thorley and Vorkink, 2006).
Following Statman et al. (2006), vector autoregressive (VAR) and impulse response functions in order to study the interaction between market returns and trading proxies (volume) will be used.
Definition of Variables:
mret : the monthly stock market return (value-weighted)
mturn : the monthly volume (shares traded; detrended natural log of market volume)
msig : the monthly temporal volatility of market return based on daily market
returns within the month, correcting for realized autocorrelation, as specified in French, Schwert and Stambaugh (1987).
Disp: cross-sectional standard deviation of returns for all stocks in month t.
Vector Autoregressive Model:
where Yt is a nx1 vector of period t observations of endogenous variables, for example turnover and return, Xt is a vector of period t observations of the exogenous (i.e., control) variables, and et is a nx1 residual vector. The regression coefficients, Ak, and Bl, estimate the time-series relationships between the endogenous and exogenous variables, where K is the number of lagged endogenous observations, and L is the number of lagged exogenous observations. The VAR methodology allows for a covariance structure to exist in the residual vector, et, that captures the contemporaneous correlation between endogenous variables.
Impulse Response Functions:
Significance of the study
This study will help explain the role of overconfidence in emergent markets. The study will also provide empirical evidence in favor or against the overconfidence hypothesis from an emergent market that could be helpful in deriving theories about the subject of overconfidence in future.
Activity Time in months (Approx.)
Review of literature one and half month
Data gathering and summarizing one month
Data analysis one month
Write up one and half month .
Total five months
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