ارزیابی تجربی از فرضیه اطمینان بیش از حد
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|10805||2006||27 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Banking & Finance, Volume 30, Issue 9, September 2006, Pages 2489–2515
Recently, several behavioral finance models based on the overconfidence hypothesis have been proposed to explain anomalous findings, including a short-term continuation (momentum) and a long-term reversal in stock returns. We characterize the overconfidence hypothesis by the following four testable implications: First, if investors are overconfident, they overreact to private information and underreact to public information. Second, market gains make overconfident investors trade more aggressively in subsequent periods. Third, excessive trading of overconfident investors in securities markets contributes to the observed excessive volatility. Fourth, overconfident investors underestimate risk and trade more in riskier securities. To document the presence of overconfidence in financial markets, we empirically evaluate these four hypotheses using aggregate data. Overall, we find empirical evidence in support of the four hypotheses.
It has been a challenge for financial economists to explain some stylized facts observed in securities markets, among them, a short-term continuation (momentum) and a long-term reversal in stock returns, high levels of trading volume, excessive volatility, and a disproportionate amount of risk borne by investors. Recently, a growing number of researchers have made an effort to develop theoretical models built on the assumption of investor overconfidence to account for these observed anomalies. The momentum effect, the continuation of short-term returns, remains one of the strongest and most puzzling asset-pricing anomalies (e.g., Jegadeesh and Titman, 1993 and Jagadeesh and Titman, 2001).2 By contrast, De Bondt and Thaler, 1985 and De Bondt and Thaler, 1987 document return reversals over longer horizons. Several possible explanations for these types of return patterns have been investigated in the literature, including data mining and behavioral patterns. However, data mining and risk seem to have difficulty in explaining these two coexistent phenomena. Daniel et al. (1998) (hereafter, DHS) show that if investors are overconfident, they overweight their own private information at the expense of ignoring publicly available information. As a result, investors overreact to private information and underreact to public information, and this asymmetric response of overconfident investors induces short-horizon momentum and long-horizon reversal in stock returns (see also Odean, 1998). It has been argued that trading volume in speculative markets is too large to be justified on rational grounds. Trading motivated from hedging and liquidity purposes seems to explain only a small fraction of the observed trading activity and fails to support a substantial amount of trade in the real world. Overconfidence has been advanced as an explanation for the observed excessive trading volume. For example, Gervais and Odean (2001) develop a model predicting that overconfident investors mistakenly attribute market gains to their ability to pick winning stocks, and the process of wealth accumulation makes them trade more aggressively following market gains. A similar argument that overconfidence leads to greater trading is made in De Long et al., 1991, Kyle and Wang, 1997, Benos, 1998, Odean, 1998, Wang, 1998, Wang, 2001, Daniel et al., 2001, Hirshleifer and Luo, 2001 and Scheinkman and Xiong, 2003. De Bondt and Thaler (1995, p. 393) state, “… the key behavioral factor needed to understand the trading puzzle is overconfidence”. A large volume of empirical work has documented that stock prices are more volatile than an efficient market hypothesis can explain (e.g., Shiller, 1981). One rational solution to this volatility puzzle is Campbell and Cochrane’s (1999) habit formation model in which changes in consumption relative to habit lead to changes in risk aversion and hence variation in price-to-dividend ratios. This variation helps to reduce the gap between the volatility of dividend growth and the volatility of returns. By circumventing the relation between price movement and firms’ fundamentals, overconfidence is proposed as an important reason for excessive volatility. Benos (1998), for example, proposes a model in which overconfident traders’ aggressive exploitation of their profitable information, together with rational traders’ conservative trading strategy, leads prices to move too much in one or the other direction. The prediction that volatility increases with overconfidence is also drawn from the studies of Daniel et al., 1998, Odean, 1998, Wang, 1998, Gervais and Odean, 2001 and Scheinkman and Xiong, 2003. Financial economists have modeled overconfidence as an overestimation of the precision of private information signals (e.g., De Long et al., 1991, Kyle and Wang, 1997, Benos, 1998, Odean, 1998, Wang, 1998, Wang, 2001, Gervais and Odean, 2001, Daniel et al., 2001, Hirshleifer and Luo, 2001 and Scheinkman and Xiong, 2003). As a result of underestimating risk, overconfident investors hold more risky assets. In Hirshleifer and Luo’s (2001) model, the survival of overconfident investors in a competitive market is primarily due to their willingness to take on more risk in order to exploit the mispricing generated by noise and liquidity traders. Therefore, overconfident investors have a proclivity toward trading in relatively risky securities. In sum, the overconfidence hypothesis contains various implications and, among other things, offers the following empirically testable hypotheses. First, overconfident investors overreact to private information and underreact to public information (H1). Second, market gains (losses) make overconfident investors trade more (less) aggressively in subsequent periods (H2). Third, excessive trading of overconfident investors in securities markets contributes to the observed excessive volatility (H3). Fourth, overconfident investors underestimate risk and trade more in riskier securities (H4). In empirically evaluating implications of the overconfidence hypothesis, previous empirical studies tend to focus on the investigation of trading behavior of individual investors and on some specific predictions of the hypothesis. For example, using a sample of discount brokerage accounts, Barber and Odean, 2000, Barber and Odean, 2001, Barber and Odean, 2002 and Odean, 1999 find that individual investors appear overconfident about their perceived information and ability to trade in that they trade too much. The main goal of this paper is to provide comprehensive empirical evidence on various implications of the overconfidence hypothesis by focusing on aggregate investor behavior. Our focus on aggregate investor behavior is motivated in part by the argument of Odean, 1998, Daniel et al., 2001 and Gervais and Odean, 2001 that investor behavior should be observable in market level data, and in part by that of Kyle and Wang, 1997, Benos, 1998, Daniel et al., 1998, Hirshleifer and Luo, 2001 and Wang, 2001 that overconfident investors can survive and dominate the markets in the long run. In addition, Fama (1998) asserts that a valid finance theory should explain the market as a whole rather than a specific type or group of investors. As such, it is an important empirical issue to examine whether a cognitive bias such as overconfidence is observed in market level data. To empirically evaluate the aforementioned four hypotheses of the overconfidence hypothesis, we provide various empirical frameworks taking into account Fama’s criticism.3 We employ the longest sample period available from database to account for the potential problem of data mining. We construct and use weekly observations to mitigate the non-synchronous trading problem. We make use of different methods to detrend the trading volume series and use both value- and equal-weighted variables in most tests to take into account the size effect. Overall, we find empirical evidence in support of the four hypotheses. Statman et al. (2003) examine the empirical validity of the overconfidence model of Gervais and Odean (2001) that market gains make overconfident investors trade more frequently in subsequent periods. They find that high market-wide returns are followed by high market-wide trading volume. They are aware that the disposition effect (Shefrin and Statman, 1985) also implies this positive relation between lagged returns and current volume and provide further evidence that their finding is not the spurious result of aggregated disposition effects on individual securities.4 Their analysis mostly focuses on our second testable hypothesis (H2), whereas we attempt to empirically evaluate broad implications of the overconfidence hypothesis in this paper. The remainder of the paper is organized as follows. Section 2 discusses the testable empirical hypotheses of the overconfidence hypothesis and presents our empirical frameworks devised to evaluate the overconfidence hypothesis. Section 3 describes data and discusses the methods we employ to detrend the turnover series. Section 4 presents the empirical results of the evaluation of those hypotheses. Section 5 concludes the paper.
نتیجه گیری انگلیسی
Overconfidence has been proposed as a viable explanation for several observed anomalies in securities markets. In this paper, we devise various empirical frameworks in an attempt to provide a comprehensive evaluation of the empirical validity of the overconfidence hypothesis by focusing on the aggregated behavior of overconfident investors. To study the response of stock prices to private and public information, we employ a just-identified bivariate moving average representation model, the restriction of which is based on theoretical considerations. Our identification of the dynamic effects of private and public information shocks on stock prices shows that, with an initial underreaction, stock prices overreact to private information followed by a correction process, and underreact to public information reaching an equilibrium response without a significant long-run reversal. This price response provides evidence in support of our first hypothesis that overconfident investors overreact to private information and underreact to public information. By performing Granger-causality tests of stock returns and trading volume, we find that high stock returns Granger-cause high trading volume, and that this positive dynamic relation is quite robust for sub-sample periods. We also have investigated the asymmetric trading behavior of investors and find that investors do trade more aggressively in a bull market than in a bear market. This is in accordance with the argument of Daniel et al., 2001 and Gervais and Odean, 2001 that overconfidence is posited to be easily fostered in a bull market. To study investors’ reactions to market gains when they make right and wrong forecasts and to the precision of their forecast errors, we obtain investors’ forecasts of future stock returns and forecast errors by adopting two GARCH-type specifications. We find that investors become overconfident and trade more actively following market gains as they make right forecasts of future stock returns than as they make wrong forecasts. In addition, we find weak evidence that investors trade more actively when their forecast errors are smaller. These are consistent with the theoretical prediction of self-attribution bias in the Daniel et al., 1998 and Gervais and Odean, 2001 models. Overall, these findings provide empirical evidence in support of our second hypothesis that if investors are overconfident, market gains make them trade more aggressively in subsequent time periods. To see whether the relation between excessive trading in securities markets and the observed excessive volatility is due to investor overconfidence, we decompose trading volume into two components: one related to past stock returns and the other unrelated to past stock returns. Then we examine the relation between the volatility of stock returns and these two components of trading volume. We find that conditional volatility is positively accounted for by trading volume caused by past stock returns. This finding provides some empirical evidence to support our third hypothesis that if investors are overconfident, their excessive trading in securities markets contributes to the observed excessive volatility. To examine whether investors underestimate risks in making their investment decisions and trade more in riskier securities as a result of their overconfidence, we construct portfolios with varying risk levels employing two measures of risk: firm-specific risk and return volatility. Overall, we find that investors trade more in riskier stocks regardless of the measure of risk used in constructing the portfolios. Overall, our study provides extensive evidence of the presence of overconfidence in financial markets. There is a growing literature that explores how asset return predictability could arise from rational agents (e.g., Jones and Slezak, 1998, Grundy and Kim, 2002 and Holden and Subrahmanyam, 2002). Typically, these theoretical models rely on some combination of risk aversion and information asymmetry. However, given comprehensive evidence of various implications of the overconfidence hypothesis presented in this paper, we believe the overconfidence hypothesis remains a viable explanation of many stylized anomalous observations in securities markets.