نوسانات بازار سهام، بازدهی اضافی، و نقش تمایلات سرمایه گذار
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19515||2002||23 صفحه PDF||سفارش دهید||9936 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Banking & Finance, Volume 26, Issue 12, 2002, Pages 2277–2299
Using the Investors' Intelligence sentiment index, we employ a generalized autoregressive conditional heteroscedasticity-in-mean specification to test the impact of noise trader risk on both the formation of conditional volatility and expected return as suggested by De Long et al. [Journal of Political Economy 98 (1990) 703]. Our empirical results show that sentiment is a systematic risk that is priced. Excess returns are contemporaneously positively correlated with shifts in sentiment. Moreover, the magnitude of bullish (bearish) changes in sentiment leads to downward (upward) revisions in volatility and higher (lower) future excess returns.
Underlying noise trader models in finance is the premise that subsets of agents trade in response to extraneous variables that convey no information about fundamentals. Early papers (Friedman, 1953; Fama, 1965) argue that noise traders are unimportant in the financial asset price formation process because trades made by rational arbitrageurs drive prices close to their fundamental values. Continuing evidence of market anomalies, for example, the underreaction and overreaction of stock prices and the closed-end mutual fund premium/discount puzzle, however, challenge efficient markets theory. The extent to which arbitrage can eliminate the divergence between prices and fundamental values has come into question in recent literature. The notable work of De Long, Shleifer, Summers, and Waldmann (DSSW (1990) hereafter) models the influence of noise trading on equilibrium prices. Noise traders acting in concert on non-fundamental signals can introduce a systematic risk that is priced. In their model, the deviations in price from fundamental value created by changes in investor sentiment are unpredictable. Arbitrageurs betting against mispricing run the risk, at least in the short run, that investor sentiment becomes more extreme and prices move even further away from fundamental values. The potential for loss and the arbitrageurs' risk aversion reduce the size of positions they are willing to take. Consequently, arbitrage fails to completely eliminate mispricing and investor sentiment affects security prices in equilibrium. The `noise trader' model of DSSW has motivated empirical attempts to substantiate the proposition that `noise trader' risks influence price formation.3 Since closed-end fund shares are primarily held by individual investors, Lee, Shleifer and Thaler (LST (1991) hereafter) infer that fluctuations in closed-end fund discounts proxy for changes in investor sentiment. They find that changes in closed-end fund discounts are highly correlated with the returns on small capitalization stocks that are predominantly held by individual investors. Neal and Wheatley (1998) also find that (larger) closed-end fund discounts predict (higher) small firm returns,4 and that net redemption captures the investor sentiment in closed-end fund discounts. Surprisingly, another popular measure of investor sentiment, the odd-lot sales to purchases, appears to have no ability to predict small or large firm returns. Similarly, Bodurtha et al. (1995) report that changes in country fund discounts reflect a previously unidentified risk factor, which they conclude, is related to the sentiment of US investors. Using household data, Kelly (1997) also shows that the likelihood an individual is a noise trader diminishes with income; that is, a high participation of low-income households (noise traders) in the equity market is associated with a low participation by high-income households (smart money or informed traders). Moreover, a high participation of low-income households (noise traders) results in negative future returns. Further, in examining the predictability of both survey based as well as indirect sentiment measures on short- and long-horizon returns, Brown and Cliff (1999) find weak evidence of short-run predictability but a strong correlation between sentiment and long-horizon (2–3 years) returns. Additionally they note not only the existence of individual sentiment but also of institutional sentiment, and reject the conventional wisdom that sentiment is primarily an individual investor driven phenomenon that should only affect small stocks. The above evidence, which suggests investor sentiment is a priced factor in market equilibrium, is a matter still in dispute.5 Using samples of non-utility and utility stocks, passive portfolios of stocks sorted into CRSP size deciles, and active portfolios of mutual funds, Elton et al. (1998) find that closed-end fund discount changes enter as a pricing factor in the return-generating process less often than industry return indices which are not considered to be priced factors.6 Moreover, in single market factor regressions of portfolio returns using the CRSP value-weighted NYSE index as the market factor, they confirm that the sensitivity of portfolio returns to closed-end fund discount changes increases as firm size decreases but the pattern is reversed when additional market factors are introduced. Closed-end fund discount changes do not appear to be related to empirically derived factors from a decomposition of the variance–covariance matrix. Furthermore, noise trading may affect expected return through its impact on the market's formation of risk. Existing empirical tests of DSSW focus primarily on first moment contemporaneous correlations between returns and sentiment changes with a few exceptions. Brown (1999) recognizes that noise trading may influence higher moments of return such as volatility. Using a sentiment measure compiled by American Association of Individual Investors, he finds that unusual levels of individual investor sentiment are associated with greater volatility in closed-end fund returns. Utilizing the signal extraction methodology of French and Roll (1986), Brauer (1993) estimates that about 7% of the variation in fund discounts/premiums can be explained by noise trading. Further, based on a sample of 237 investment newsletters, Graham and Harvey (1996) find no evidence of market timing ability but that disagreement among newsletters is associated with volatility. None of these papers however directly address whether noise trader risk is a systematic risk that is priced in financial markets. Because the DSSW (1990) model predicts that the direction and magnitude of changes in noise trader sentiment are relevant in asset pricing, empirical tests focused on the impact of sentiment either on the mean or variance in asset returns alone are mispecified and at best incomplete. To address this issue we propose a return-generating model that explicitly tests the impact of noise trader risk both on the formation of conditional volatility and expected return as suggested in DSSW (1990). Specifically, the `price-pressure' and `hold-more' effects captures the short-run (transitory) impact of noise trading on excess returns resulting from contemporaneous changes in investor sentiment. The `Friedman' and `create-space' effects reflect the long-run (permanent) impact of noise trading on excess returns associated with the influence of the magnitude of sentiment changes on the future volatility of returns. In a market environment where noise traders are present, the `hold-more' effect implies that noise traders' increased holdings of risky assets when their sentiment becomes more bullish raises market risk and thereby increases expected returns; and conversely, when they are bearish. However, noise traders overreact to good and bad news. Consequently, asset prices are either too high or too low depending on whether noise traders are on average optimistic or pessimistic. The overreaction induces `price pressure' and lowers expected returns. Market returns will correlate with changes in investor sentiment and the direction of the correlation depends on which effect dominates. Moreover, the magnitude of the changes in perceptions about the asset's risk by noise traders associated with their shifts in sentiment also impact expected returns. Noise traders usually have poor market timing (buy high and sell low) due to their inclination to transact together with other noise traders. Their capital losses from poor market timing are larger the greater is the magnitude of the change in their misperceptions. The Friedman effect implies that such changes result in higher market risk and lower expected returns. The extent of the adverse impact that the Friedman effect has on expected returns depends on the `space' noise trading creates. A rise in noise traders' misperceptions increases price uncertainty and crowds out risk-averse informed investors. Consequently, the larger is the proportion of noise trading, the higher will be expected returns. We employ a generalized autoregressive conditional heteroscedasticity (GARCH) in-mean model (Bollerslev, 1986 and Bollerslev, 1987; Engle et al., 1987) to show that both the conditional volatility and excess returns are affected by investor sentiment. Numerous studies have examined the tradeoff between risk (often measured by conditional variance) and expected return in securities markets. The collective evidence so far is inconclusive. An often-cited work by French et al. (1987) provides empirical evidence of a positive relationship between market excess returns and predicted volatility. Using a modified model of conditional volatility, Glosten, Jagannathan and Runkle (GJR (1993) hereafter) suggest that shocks to the market have an asymmetric impact on market volatility depending on the nature of the shock.7 More importantly, along with other researchers (Abel, 1988; Backus and Gregory, 1993), their results suggest that the price of risk over time can be negative. Our study sheds light on how sentiment and sentiment-induced noise-trading affect the tradeoff between risk and return. A proper characterization of the temporal variation of the conditional volatility is important. An accurate volatility estimate is useful in determining the prices of many financial instruments including options. It is also crucial in mapping out portfolio strategies over time since diversification decisions hinge on temporal risk. If sentiment has a significant impact on the temporal variation of conditional volatility, leaving sentiment out is likely to lead to inaccurate forecasts of asset prices and suboptimal portfolio decisions. We examine the relationship between market volatility, excess returns, and investor sentiment for three different market indices, the Dow Jones Industrial Average (DJIA), the Standard and Poor's 500 (S&P500) and the NASDAQ, from the beginning of 1973 through October 6, 1995.8 In this paper, we use the widely publicized Investors' Intelligence of New Rochelle, NY, sentiment index as a direct measure of investor sentiment. As a barometer of the temperament of individual investors, this index has been noted to be a good indicator of how market psychology can swing from outright pessimism to extreme overconfidence (Pring, 1991).9 Our main findings suggest that sentiment is a significant factor in explaining equity excess returns and conditional volatility. Specifically, our results consistently show that sentiment is a priced risk factor. Excess returns are contemporaneously positively correlated with shifts in sentiment. Furthermore, we find that the magnitude of shifts in sentiment has a significant impact on the formation of conditional volatility of returns and expected returns. Bullish (bearish) shifts in sentiment lead to downward (upward) revisions in the volatility of returns and are associated with higher (lower) future excess returns. The significance of investor sentiment in explaining the formation of conditional volatility and expected return is robust across different indices and subperiods. The remainder of the paper proceeds as follows. Section 2 of the paper presents the data source and the empirical model. Section 3 discusses the empirical results, diagnostic tests, and the impact of investor sentiment across two subperiods. The last section provides some concluding remarks.