Random walks and technical theories: some additional evidence
TL;DR: In this article, the authors examined the profitability of various "filter" trading rules based only on the past price series which purportedly capture the essential characteristics of many technical theories and concluded that these rules do not yield profits net of transactions costs which are higher than those earned by a simple buy-and-hold strategy.
Abstract: THE RANDOM WALK and martingale efficient market theories of security price behavior imply that stock market trading rules based solely on the past price series cannot earn profits greater than those generated by a simple buy-and-hold policy1. A vast amount of statistical testing of the behavior of security prices indicates very little evidence of any important dependencies in security price changes through time.2 Technical analysts or chartists, however, have insisted that this evidence does not imply their methods are invalid and have argued that the dependencies upon which their rules are based are much too subtle to be captured by simple statistical tests. In an effort to meet these criticisms Alexander (1961, 1964) and later Fama and Blume (1966) have examined the profitability of various "filter" trading rules based only on the past price series which purportedly capture the essential characteristics of many technical theories. These studies indicate the "filter" rules do not yield profits net of transactions costs which are higher than those earned by a simple buy-andhold strategy. Similarly, James (1968) and Van Horne and Parker (1967) have found that various trading rules based upon moving averages of past prices do not yield profits greater than those of a buy-and-hold policy.
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TL;DR: In this article, the authors show that strategies that buy stocks that have performed well in the past and sell stocks that had performed poorly in past years generate significant positive returns over 3- to 12-month holding periods.
Abstract: This paper documents that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3- to 12-month holding periods. We find that the profitability of these strategies are not due to their systematic risk or to delayed stock price reactions to common factors. However, part of the abnormal returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented
10,806 citations
TL;DR: In this article, the authors used the Dow Jones Index from 1897 to 1986 to test two of the simplest and most popular trading rules (moving average and trading range break) by utilizing the bootstrap techniques.
Abstract: This paper tests two of the simplest and most popular trading rules—moving average and trading range break—by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, our results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models.
2,236 citations
TL;DR: In this paper, the authors used White's Reality Check bootstrap methodology to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn.
Abstract: In this paper we utilize White’s Reality Check bootstrap methodology ~White ~1999!! to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a comprehensive test of performance across all technical trading rules examined. We consider the study of Brock, Lakonishok, and LeBaron ~1992!, expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data-snooping. TECHNICAL TRADING RULES HAVE BEEN USED in financial markets for more than a century. Numerous studies have been performed to determine whether such rules can be employed to provide superior investing performance. 1 By and large, recent academic literature suggests that technical trading rules are capable of producing valuable economic signals. In perhaps the most comprehensive recent study of technical trading rules using 90 years of daily stock prices, Brock, Lakonishok, and LeBaron ~1992 !~ BLL, hereafter! find that 26 technical trading rules applied to the Dow Jones Industrial Average ~DJIA! significantly outperform a benchmark of holding cash. Their findings are especially strong because every one of the trading rules they consider is capable of beating the benchmark. When taken at face value, these results indicate either that the stock market is not efficient even in the weak form—a conclusion which, if found to be robust, will go against most researchers’ prior beliefs—or that risk premia display considerable variation even over very short periods of time ~i.e., at the daily interval!. An important issue generally encountered, but rarely directly addressed when evaluating technical trading rules, is data-snooping. Data-snooping
1,031 citations
TL;DR: In this paper, the authors use a single unifying framework to analyze the sources of profits to a wide spectrum of return-based trading strategies implemented in the literature and show that less than 50% of the 120 strategies implemented by the authors yield statistically significant profits.
Abstract: In this article we use a single unifying framework to analyze the sources of profits to a wide spectrum of returnbased trading strategies implemented in the literature. We show that less than 50% of the 120 strategies implemented in the article yield statistically significant profits and, unconditionally, momentum and contrarian strategies are equally likely to be successful. However, when we condition on the return horizon (short, medium, or long) of the strategy, or the time period during which it is implemented, two patterns emerge. A momentum strategy is usually profitable at the medium (3to 12-month) horizon, while a contrarian strategy nets statistically significant profits at long horizons, but only during the 19261947 subperiod. More importantly, our results show that the cross-sectional variation in the mean returns of individual securities included in these strategies plays an important role in their profitability. The cross-sectional variation can potentially account for the profitability of momentum strategies and it is also responsible for atten-
947 citations
TL;DR: Jiang et al. as mentioned in this paper compared the predictive ability of technical indicators with that of macroeconomic variables and showed that combining information from both technical indicators and macroeconomic features significantly improves the prediction of the U.S. equity risk premium.
Abstract: Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial countercyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables.
Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1838 .
This paper was accepted by Wei Jiang, finance.
564 citations
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TL;DR: In this paper, the authors present a body of positive microeconomic theory dealing with conditions of risk, which can be used to predict the behavior of capital marcets under certain conditions.
Abstract: One of the problems which has plagued thouse attempting to predict the behavior of capital marcets is the absence of a body of positive of microeconomic theory dealing with conditions of risk/ Althuogh many usefull insights can be obtaine from the traditional model of investment under conditions of certainty, the pervasive influense of risk in finansial transactions has forced those working in this area to adobt models of price behavior which are little more than assertions. A typical classroom explanation of the determinationof capital asset prices, for example, usually begins with a carefull and relatively rigorous description of the process through which individuals preferences and phisical relationship to determine an equilibrium pure interest rate. This is generally followed by the assertion that somehow a market risk-premium is also determined, with the prices of asset adjusting accordingly to account for differences of their risk.
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TL;DR: This paper showed that the text, first written in 1964, is still relevant and relevant at the beginning of the 21st century, which is known to a generation of financial economists having marked the beginnings of the field known as financial econometrics.
Abstract: This work is known to a generation of financial economists having marked the beginnings of the field known as financial econometrics. This edition sets out to show that the text, first written in 1964, is still relevant is still relevant at the beginning of the 21st century.
886 citations
TL;DR: In this article, the authors consider a random-walk market, where successive price changes in individual securities are independent random variables and the past history of a series of changes cannot be used to predict future changes in any "meaningful" way.
Abstract: IN THE recent literature there has been a considerable interest in the theory of random walks in stock-market prices. The basic hypothesis of the theory is that successive price changes in individual securities are independent random variables. Independence implies, of course, that the past history of a series of changes cannot be used to predict future changes in any "meaningful" way. What constitutes a "meaningful" prediction depends, of course, on the purpose for which the data are being examined. For example, the investor wants to know whether the history of prices can be used to increase expected gains. In a random-walk market, with either zero or positive drift, no mechanical trading rule applied to an individual security would consistently outperform a policy of simply buying and holding the security. Thus, the investor who must choose between the random-walk model and a more complicated model which assumes the existence of an excessive degree of either persistence (positive dependence) or reaction (negative dependence) in successive price changes, should accept the theory of random walks as the better model if the actual degree of dependence cannot be used to produce greater expected profits than a buy-and-hold policy.' On the other hand, the statistician has different though equally pragmatic notions of what constitutes an important violation of the independence assumption of the random-walk model. He will
865 citations