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This study shows that past trading volume provides an important link between "momentum" and "value" strategies. Specifically, we find that firms with high (low) past turnover ratios exhibit many glamour (value) characteristics, earn lower (higher) future returns, and have consistently more negative (positive) earnings surprises over the next eight quarters. Past trading volume also predicts both the magnitude and persistence of price momentum. Specifically, price momentum effects reverse over the next five years and high (low) volume winners (losers) experience faster reversals. Collectively, our findings show that past volume helps to reconcile intermediate-horizon "underreaction" and long-horizon "overreaction" effects. 1 Financial academics and practitioners have long recognized that past trading volume may provide valuable information about a security. However, there is little agreement on how volume information should be handled and interpreted. Even less is known about how past...

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. Several of these studies conclude that technical analysis does have merit, however, it is noted that the effects of data-snooping are not fully accounted for. In this paper we utilize White's Reality Check bootstrap methodology (White (1997)) 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 means of calculating a comprehensive test of performance across all trading rules. In particular, 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. During the sample period inspected by Brock, Lakonishok and LeBaron, we find that the best technical trading rule is capable of generating superior performance even after accounting for data- snooping. However, we also find that the best technical trading rule does not provide superior performance when used to trade in the subsequent 10-year post-sample period.

Technical analysis, also known as “charting, ” has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis—the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution—conditioned on specific technical indicators such as head-and-shoulders or double-bottoms—we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. ONE OF THE GREATEST GULFS between academic finance and industry practice

We study return volatility and trading volume at times of earnings announcements to see if the increased disclosure faced by non-U.S. firms when listing shares in the U.S. has economically significant consequences. We find a surprising change in market behavior around earnings releases: return and volume reactions to earnings announcements typically increase significantly once a stock cross-lists in the U.S. Furthermore, the change in information environment is greatest for firms from developed countries and firms that do not list on an organized stock exchange. The increased market reaction to earnings shocks after cross listing is particularly prominent for firms from developed countries with relatively weak disclosure requirements.

The paper presents a model of rational Bayesian agents with speculative attacks in a regime of exchange rate which is pegged within a band. Speculators learn from the observation of the exchange rate within the band whether their mass is sufficiently large for a successful attack. Multiple periods are necessary for the existence of speculative attacks. Various defense policies are analyzed. A trading policy by the central bank may defend the peg if it is unobserved and diminishes the market’s information for the coordination of speculators. Key words: Speculative attack, currency crisis, coordination, informational

and the participants of the Taipei conference on “Analysis of High-Frequency Financial Data and Market Microstructure ” for their valuable comments and suggestions. We also thank P. R. Hansen for sharing his

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We introduce trading rules which are selected by an artificially intelligent agent who learns from experience - an Artificial Technical Analyst. It is shown that these rules can lead to the recognition of subtle regularities in return processes whilst reducing data-mining problems inherent in simple rules proposed as model evaluation devices. The relationship between the efficiency of financial markets and the efficacy of technical analysis is investigated and it is shown that the Artificial Technical Analyst can be used to provide a quantifiable measure of market efficiency. The measure is applied to the DJIA daily index from 1962 to 1986 and implications for the behaviour of traditional agents are derived.