پیش بینی بازده سهام و فرضیه بازارهای تطبیقی: شواهدی از داده های قرن اخیر در آمریکا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|13236||2011||12 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Empirical Finance, Volume 18, Issue 5, December 2011, Pages 868–879
This paper provides strong evidence of time-varying return predictability of the Dow Jones Industrial Average index from 1900 to 2009. Return predictability is found to be driven by changing market conditions, consistent with the implication of the adaptive markets hypothesis. During market crashes, no statistically significant return predictability is observed, but return predictability is associated with a high degree of uncertainty. In times of economic or political crises, stock returns have been highly predictable with a moderate degree of uncertainty in predictability. We find that return predictability has been smaller during economic bubbles than in normal times. We also find evidence that return predictability is associated with stock market volatility and economic fundamentals.
The efficient market hypothesis (EMH) grew out of the University of Chicago's business school over 40 years ago. It swayed many academics and policy makers into believing that stock prices fully reflect all available information, and no market participant can systematically make abnormal profit (Fama, 1970). When the information set is limited to past prices, the market is said to be weak-form efficient, and asset return is purely unpredictable from past prices. While most finance academics believe that the market is weak-form efficient (see Doran et al., 2010), there are critics from behavioral finance who document irrational but predictable investor behavior such as overreaction and overconfidence (see, for example, De Bondt and Thaler, 1985 and Barber and Odean, 2001) and the momentum effect (Jegadeesh and Titman, 1993). Many commentators even attribute some responsibility for the recent global financial crisis (GFC) to an enduring belief of financial economists and policy makers in the EMH and the self-correcting capacity of markets (see Fox, 2009 and Nocera, 2009). Grossman and Stiglitz (1980) provide a theoretically compelling argument that a perfectly efficient market is impossible because if prices fully reflect all available information, traders would not have any incentive to acquire costly information. Given the impossibility of perfect efficiency, Campbell et al. (1997) propose the notion of relative efficiency, which has led to a shift in research focus from testing the all-or-nothing notion of absolute market efficiency to measuring the degree of market efficiency. There is also a growing empirical literature suggesting that market efficiency varies over time (for a survey, see Lim and Brooks, 2011). Lo (2004) proposes a new framework in the form of the adaptive markets hypothesis (AMH), which can help explain the observed time variation in the degree of market efficiency. The AMH is developed by coupling the evolutionary principle with the notion of bounded rationality (Simon, 1955). A bounded rational investor is said to exhibit satisfying rather than optimal behavior. Optimization can be costly, and market participants with limited access to information or abilities to process information are merely engaged in attaining a satisfactory outcome. Lo (2004) argues that a satisfactory outcome is attained not analytically, but through an evolutionary process involving trial-and-error and natural selection. The process of natural selection ensures the survival of the fittest and determines the number and composition of market participants and trading strategies. Market participants adapt to the constantly changing environment and rely on heuristics to make investment choices. An important implication of the AMH is that return predictability can arise time to time due to changing market conditions. Therefore, market efficiency may not follow a secular trend toward greater efficiency as anticipated by proponents of the EMH, but instead can vary in a cyclical fashion being “highly context dependent and dynamic” ( Lo, 2004). Though a number of recent studies proceed to explain time variation in the degree of return predictability (see Chuluun et al., 2011, Gu and Finnerty, 2002 and Lagoarde-Segot, 2009), none of these previous studies explore the role of changing market conditions. The testable implications of the AMH are twofold. First, the degree of market efficiency fluctuates over time. Second, the degree of market efficiency is governed by market conditions. This paper tests the first implication by tracking the evolution of return predictability of the U.S. stock market over the last century, and the second implication by examining whether the degree of return predictability in the U.S. is dependent upon market conditions as manifested by market crashes, fundamental economic or political crises, economic bubbles and regulatory regimes.1 We measure the degree of return predictability using three alternative test statistics with superior statistical properties, namely, the automatic variance ratio test of Choi (1999), the automatic portmanteau test of Escanciano and Lobato (2009), and the generalized spectral test of Escanciano and Velasco (2006). In addition, the confidence interval is constructed to gauge the degree of uncertainty associated with return predictability. The above methodological advances provide a more rigorous analysis and results than our predecessors.2 We obtain monthly measures of the degree of return predictability from the Dow Jones Industrial Average index over the period from 1900 to 2009, and test whether they are related to different stock market conditions after controlling for macroeconomic fundamentals. Since 1900, the U.S. stock market has experienced a number of exceptional and unexpected events, such as market crashes, economic or political crises, economic bubbles and major regulatory changes. These events have strong implications on the psychology of market participants and the way they incorporate new information to prices, which in turn may generate time variation in the serial correlation of returns as suggested by the AMH. This paper finds strong evidence in favor of time-varying return predictability of the U.S. stock market and dependence of return predictability on market conditions. Both findings are consistent with the implications of the AMH. In particular, during stock market crashes, no return predictability is observed and an extremely high degree of uncertainty is associated with measures of return predictability. In contrast, during economic or political crises, stock returns are found to be highly predictable with a moderate degree of uncertainty. In times of economic bubbles, the degree of return predictability is found to be lower than in normal times. We also find that return predictability is affected by market volatility and macroeconomic fundamentals such as inflation and interest rates. Contrary to the general findings of past studies, we find a higher degree of return predictability before 1980 and a strong tendency to non-predictability afterwards. The next section presents the details of the data. Section 3 presents the methodology, and Section 4 presents the empirical results and their implications. The conclusion is drawn in Section 5.
نتیجه گیری انگلیسی
We examine the degree of return predictability of the U.S. stock market using the century-long Dow Jones Industrial Average index. As measures of the degree of return predictability, we use the statistics from the automatic variance ratio and automatic portmanteau tests. To detect possible nonlinear dependence in stock returns, the generalized spectral test has been implemented. We obtain monthly time-varying measures of return predictability by applying these tests to moving subsample windows over monthly grids. A regression analysis is conducted to determine how these measures of return predictability are related to changing market conditions and economic fundamentals. We find evidence that return predictability fluctuates over time and is governed largely by changing market conditions. It is found that during market crashes, no return predictability is evident, possibly due to extreme degree of associated uncertainty. However, during economic and political crises, a high degree of return predictability with a moderate degree of uncertainty is observed. During bubble times, return predictability and its uncertainty are found to be lower than normal times. However, the relation between return predictability and the state of the market may not be exploited economically because it is difficult to predict crashes, the start and end of bubbles, or the timing and duration of a crisis. We find evidence that inflation, risk-free rates, and stock market volatility are important factors that influence stock return predictability over time. Contrary to the general findings of past empirical and survey studies, we find evidence that the U.S. market has become more efficient after 1980. This is plausible, given that the U.S. market has implemented various measures of market innovation in the 1960s and 1970s, and that U.S. macroeconomic fundamentals have become much more stable since 1980. In addition, apart from the sub-prime lending crisis, there have been fewer occurrences of major economic and political crises after 1980 than before. Overall, our finding is in line with the adaptive markets hypothesis, which argues that dynamic market conditions govern the degree of stock market efficiency. In this paper, we conduct empirical evaluation of the AMH in the context of the U.S. stock market. While a century-long U.S. daily stock price series presents a unique opportunity to examine the AMH and our results are strongly suggestive to other financial markets, it is of interest to examine if the same results can be applicable to other financial prices. Another possible future extension is to adopt alternative ways of assessing predictability, such as evaluating out-of-sample predictability of stock returns (see, for example, Timmermann, 2008). We leave these lines of research for future studies.