Narrow Results

This paper analyzes the overconfident and underconfident trading behavior simultaneously in the context extended from Gervais and Odean (2001) [Learning to be overconfident. Review of Financial Studies, 14(1), 1–27]. We find that the overconfidence level will be first decreasing, then increasing and finally decreasing as the number of the successful predictions increases when the underconfident behavior is sufficiently prominent and the expected trading volume in the future will first decrease then increase and finally decrease as the number of successes increases. Furthermore, the insider’s expected profits in the future are a monotonically increasing function of the number of successes when the number of successes is sufficiently small or large but monotonically decreasing function of this number when it is in the intermediate range. This completes the analysis of the investors’ biased behavior.

This paper examines the behavior of volatility and trading volume in an extended Mixture-of-Distribution Hypothesis framework. According to this Hypothesis, both volatility and volume are subordinated to the same latent, stochastic variable: the information flow. One way to enlarge this modeling in order to capture long range dependecies observed in these two variables is to use multi-component volatility models. However, traditional multi-component volatility models seem no longer enough to describe the more complex behavior of the volatility. This is why we introduce in this class of models, a stochastic cascade model of volatility, inherited from the multifractal paradigm. In the empirical section we provide some evidence in favor of this approach. Both volatility and volume are fractionally integrated processes with similar hyperbolic decay rates, but the long range dependence of their power transforms exhibits significant differences. Moreover, we emphasize the existence of lagged cross-correlations between the volatilities at different sampling frequencies, first pointed out by Müller et al. [59], which historically opened the door to a (stochastic) cascade scheme of the volatility in the literature. A multiscaling analysis of the structure functions (generalized volatilities) confirms this result. The empirical results are provided with data on the French stock Alcatel.

Stock splits are a relatively new phenomenon in the Indian context. This paper examines the market effect of stock splits on stock price, return, volatility, and trading volume around the split ex-dates for a sample of stock splits undertaken in the Indian stock market over the period 1999–2005.

The traditional view of stock splits as cosmetic transactions that simply divide the same pie into more slices is inconsistent with the significant wealth effect associated with the announcement of a stock split. However, the empirical evidence confirms a negative effect on price and return of stock splits. The overall cumulative abnormal returns after the split are negative. These results suggest that stock splits have induced the market to revise its optimistic valuation about future firm performance, rejecting signaling hypothesis to which splits convey positive information to markets. Hence, stock splits have reduced the wealth of the shareholders. The results also show that presence of a positive effect on volatility and trading volume following the split events, thus suggesting that split events enhance liquidity.

In this study, we investigate the daily relationships between returns on individual stocks and their corresponding futures contracts in Australian, Hong Kong, and United Kingdom markets. We find that, at the beginning of the life of a futures market, autocorrelation of futures returns is similar to that of individual stock returns. As the market becomes mature, the autocorrelation of futures returns behaves differently from the autocorrelation of stock returns. Through the linkage between return autocorrelations and trading volume, we find that a larger trading volume depresses the return autocorrelation and shrinks the differences of return autocorrelation between stock and its futures. In addition, futures trading volume has more significant impact on the patterns of return autocorrelations than the stock trading volume. The effect is non-linear in the sense that it is much more prominent during high futures trading periods. Summary of these findings suggests that the difference of return autocorrelations between an individual stock and its futures contract is due to low trading activities of futures.

This study empirically investigates the interaction between trading volume and cross-autocorrelations of stock returns in the Taiwan stock market. The result shows that returns on high trading volume portfolios lead returns on low trading volume portfolios when controlled for firm size, indicating that trading volume determines lead-lag cross-autocorrelations of stock returns. Overall, the empirical findings of this study demonstrate similar results for both monthly and daily returns, suggesting that nonsynchronrous trading is not the main reason for the lead-lag cross-autocorrelations presented in this study. Consequently, the empirical results presented here support the speed of adjustment hypothesis, and suggest that some market inefficiency exists in the Taiwan stock market. Additionally, compared with evidence of lead-lag cross-autocorrelations in the larger, less regulated US stock market, as examined by Chordia and Swaminathan (2000), Taiwan stock market displays less evidence of VARs and Dimson beta regressions. We conjecture that this weak evidence may result from the regulations limiting daily price movements in the Taiwan stock market. Although the price limits policy lowers risk and stabilizes stock prices, it also prevents stock prices and trading volume from instantaneously and fully reflecting new information.

We study the stock price and trading volume reactions to dividend initiations by high-tech firms relative to those by non-high tech firms. We find significant positive cumulative abnormal returns and abnormal trading volume for both high-tech and non-high tech firms surrounding dividend initiations. However, when we control for variables such as size and dividend yield, stock returns and trading volume around dividend initiations are higher for high-tech firms than for non-high tech firms. We also find evidence that stock returns and trading volume for high-tech firms are higher with increases in liquid assets, although the volume reaction to increases in liquid assets is stronger than the return reaction, perhaps indicating clientele shifts. Overall, our findings convey stronger investor reaction to dividend initiations by high-tech firms, especially those with sufficient liquid assets.

In this paper, I examine the ability of equity market illiquidity to predict Australian macroeconomic variables, between 1976 and 2010. In contrast to existing, U.S.-based, studies, I find that stock market illiquidity does not, on average, have much predictive power over economic growth. Consistent with the weak in-sample predictive power, economic growth forecasts from models that exclude stock illiquidity from the set of explanatory financial variables are statistically no worse than forecasts from models that include illiquidity. However, I find strong evidence that the predictive power of equity market illiquidity is state-contingent, with much higher predictability in states associated with economic and financial stress. The difference between the single-state and regime-switching models' results reflects the fact that, as the nonstressed states have been much more prevalent, parameter estimates from a single-state model averages over both stressed and non-stressed states thus lowering the statistical and economic significance of the estimates.

We review the stylized properties of the stock market and consider effects of the intraday patterns on the analysis of the time series for the stock index and the trading volume in Korean stock market. In the stock market the probability distribution function (pdf) of the return and volatility followed the power law for the stock index and the change of the volume traded. The volatility of the stock index showed the long-time memory and the autocorrelation function followed a power law. We applied two eliminating methods of the intraday patterns: the intraday patterns of the time series itself, and the intraday patterns of the absolute return for the index or the absolute volume change. We scaled the index and return by two types of the intraday patterns. We considered the probability distribution function and the autocorrelation function (ACF) for the time series scaled by the intraday patterns. The cumulative probability distribution function of the returns scaled by the intraday patterns showed a power law, P_>(r) ~ r^-α_±, where α_± corresponds to the exponent of the positive and negative fat tails. The pdf of the return scaled by intraday patterns by the absolute return decayed much steeper than that of the return scaled by intraday patterns of the index itself. The pdf for the volume change also followed the power law for both methods of eliminating intraday patterns. However, the exponents of the power law at fat tails do not depend on the intraday patterns. The ACF of the absolute return showed long-time correlation and followed the power law for the scaled index and for the scaled volume. The daily periodicity of the ACF was removed for scaled time series by the intraday patterns of the absolute return or the absolute volume change.

Volatility–volume regressions provide a convenient framework to study sources of volatility predictability. We apply this approach to the daily realized volatility of common stocks. We find that unexpected volume plays a more significant role in explaining realized volatility than expected volume, and accounts for about one-third of the non-persistent component in the volatility process. Contrary to the findings of Lamoureux and Lastrapes (1990), the ARCH effect is robust even in the presence of volume. However, this component explains only about half of the variations in realized volatility. Thus, large portion of realized volatility is clearly stochastic. This presents a significant challenge to the goal of achieving precise realized volatility forecasts.

In this study, we examine the trading activity and volatility of stocks influenced by the U.S. Securities and Exchange Commission’s pilot program that increases tick sizes for various samples of stocks. The objective of the program is to improve the market quality of small-cap stocks, which have historically been relatively less liquid than other stocks. Using a difference-in-differences approach, we find that, relative to control stocks, the trading activity of pilot stocks does not appear to be meaningfully affected by the increase in tick sizes. Volatility, however, increases markedly for the pilot stocks compared to non-pilot stocks. These results are robust to the three different sets of pilot stocks, various rollout periods, and different control groups. We also find that pilot stocks tend to cluster on round increments of $0.05 more frequently than non-pilot stocks after the rollout periods. This is true particularly for pilot stocks that quote on $0.05 but trade on $0.01. To the extent that prices convey important information to market participants, these latter results suggest that the discreteness in prices imposed by the pilot program may adversely affect the informativeness of prices in equity markets.

Due to increasing speculation, crude oil futures are now becoming one of the highest traded commodities. This paper studies the relationship between trading volume and serial correlation in crude oil futures returns using high frequency data. We find that volume can positively predict the serial correlation in the short run (within an hour) but negatively predict the serial correlation in the midterm. The trading volume is not able to consistently predict serial correlation in the long run (more than a day). The results from our empirical studies are robust to a variety of controls and our study gives a new insight in the relation between volume and serial correlation of crude oil futures returns.

This chapter discusses the methods and applications of fundamental analysis and technical analysis. In addition, it investigates the ranking performance of the Value Line and the timing and selectivity of mutual funds. A detailed investigation of technical versus fundamental analysis is first presented. This is followed by an analysis of regression time-series and composite methods for forecasting security rates of return. Value Line ranking methods and their performance then are discussed, leading finally into a study of the classification of mutual funds and the mutual-fund managers’ timing and selectivity ability. In addition, the hedging ability is also briefly discussed. Sharpe measure, Treynor measure, and Jensen measure are defined and analyzed. All of these topics can help improve performance in security analysis and portfolio management.

This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators — the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005) — into momentum strategy. The empirical results show that the combined investment strategy includes stocks with a larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.

This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators—the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005)—into momentum strategy. The empirical results show that the combined investment strategy includes stocks with a larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China’s four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China’s stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China’s stock markets and the U.S. stock market and between China and Hong Kong. We find that U.S. return helps predict returns of Shanghai A and Shanghai B stocks. U.S. and Hong Kong volumes do not Granger-cause either return or volatility in China’s stock markets. In short, information contained in returns, volatility, and volume from financial markets in the U.S. and Hong Kong has very weak predictive power for Chinese financial market variables.

Maturity mismatches (MMs) expose banks to interest rate sensitivity, adding to the uncertainty of banks’ performances. Since information regarding MMs is usually not readily available, considering the high correlation between the two, interest rate sensitivities could serve as proxies to these mismatches for short periods. Therefore, we label them as implied MMs. Our analyses of the correlation between banks’ interest rate sensitivity and the trading volume of the banks’ equity reveal positive correlations. The heightened increase in volume suggests that implied MMs increase disagreement among banks’ investors.

This chapter discusses methods and applications of fundamental analysis and technical analysis. In addition, it investigates the ranking performance of the Value Line and the timing and selectivity of mutual funds. A detailed investigation of technical versus fundamental analysis is first presented. This is followed by an analysis of regression time series and composite methods for forecasting security rates of return. Value Line ranking methods and their performance are then discussed, leading finally into a study of the classification of mutual funds and the mutual fund managers’ timing and selectivity ability. In addition, the hedging ability is also briefly discussed. Sharpe measure, Treynor measure, and Jensen measure are defined and analyzed. All of these topics can help improve performance in security analysis and portfolio management.

This research paper originally build up the Wavelet — Kalman Filtering Hybrid Estimating and Forecasting Algorithm (WKHEFA), which incorporates the advantages of both Kalman filtering and wavelet analysis. And it successfully forecasts the 30-minute trading volume of Shanghai Stock Exchange based on WKHEFA.

In a recent article, Darrat et al. (2003) report results for the DJIA in which higher volume causes higher volatility without significant feedback. These empirical results have interesting behavioral interpretations. It is argued that the observed positive causality from volume to volatility supports overconfidence hypothesis over other alternatives, including Andreassen's (1990) salience hypothesis. The evidence suggests that investors suffer from a psychological error (overconfidence), inciting them to trade too aggressively and drive prices away from their fundamental values.