Measures Of Trading Volume Research Articles

Does Liquidity Matter When Crisis Is Brewing? Equity Returns and Intraday Liquidity in DAX Futures Market

Inspired by Mishkin (2011), we study a sample of high-frequency Eurex trading data from the peak of economic expansion preceding the global financial crisis of 2007-2009. Trading volume measures and bid￾ask spread in DAX futures market explain DAX equity returns. Advanced variable selection and multinomial model with survey analysis identify most important explanatory variables under specific market conditions. The index futures trading volume, along with bid-ask spread, identify extreme values of the underlying equity index returns. Further, the Markov Chain Monte Carlo (MCMC) model uncovers change points in the non-monotonic relationship between futures liquidity and the equity index returns, and identifies conditions leading to expected negative returns.

Expected return, volume, and mispricing

We find that expected return is related to trading volume positively among underpriced stocks but negatively among overpriced stocks. As such, trading volume amplifies mispricing. Our results are robust to alternative mispricing and trading volume measures, alternative portfolio formation methods, and controlling for variables that are known to have amplification effects on mispricing. By attributing trading volume to investor disagreement, we show that our results are consistent with the recent theoretical model of Atmaz and Basak (2018) in that investor disagreement predicts stock returns conditional on expectation bias.

Volume and Return: The Role of Mispricing

We find that expected return is related to trading volume positively among underpriced stocks but negatively among overpriced stocks. As such, trading volume amplifies mispricing. Our results are robust to alternative mispricing and trading volume measures, alternative portfolio formation methods, and controlling for variables that are known to have amplification effects on mispricing. By attributing trading volume to investor disagreement, we show that our results are consistent with the recent theoretical model of Atmaz and Basak (2018) in that investor disagreement predicts stock returns conditional on expectation bias.

Trading Volume and Stock Investments

Previous studies suggest that trading-volume measures may proxy for a number of factors, including liquidity, momentum, and information. For relatively illiquid (typically smaller) stocks, investors may demand a liquidity premium, which can result in a negative relationship between trading volume (as a proxy for liquidity) and stock returns. For relatively liquid (typically larger) stocks—the focus of this article—momentum and information effects may dominate and result in a positive relationship between trading volume and stock returns. Portfolios of S&P 500 Index and large-capitalization stocks sorted on higher trading volume and turnover tend to have higher subsequent returns (holding periods of 1–12 months) than those with lower trading volume.

20-F Filers and SEC Proposed Changes: Some Evidence of a US Home GAAP Preference

This study uses trading volume measures to provide a richer understanding of US investors trading in 20-F filers and US market reactions to the reconciliation and disclosures included in 20-F filings. A unique aspect of this study is the inclusion of all 20-F filers, including foreign filers that report using US GAAP typically excluded from studies that examine the explanatory power of the reconciliations. Consistent with some prior research and expectations expressed by practitioners, we document no abnormal reaction to the 20-F filing for any foreign filers with or without the reconciliation. In addition, we document that the level of trading by US investors in foreign filers is higher when the firm elects to report using US GAAP instead of either IFRS or other available GAAPs. This increased level of trading is found during information events (earnings announcements and 20-F filings) and non-information trading periods. Our findings add to an understanding of US investors trading in foreign filers and may be of interest to regulators and firms as they move toward changes in the filing requirements.

Overconfidence and trading volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors (number of trades, turnover). Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, the better than average effect, illusion of control, unrealistic optimism). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. The results even hold when we control for several other determinants of trading volume in a cross-sectional regression analysis. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. We argue that our findings might present a psychological foundation for the differences of opinion explanation of high levels of trading volume. Moreover, our way of empirically evaluating behavioral finance models - the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data - seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.

Heterogenous Preferences and Equilibrium Trading Volume

The classic Lucas asset pricing model with complete markets stresses aggregate risk and, hence, fails to investigate the impact of agents heterogeneity on the dynamics of the equilibrium quantities and measures of trading volume. In this paper, we investigate under what conditions non-informational heterogeneity, i.e., differences in preferences and endowments, leads to non trivial trading volume in equilibrium. Our main result comes in form of a non-informational no trade theorem which provides necessary and sufficient conditions for zero trading volume in a dynamically efficient, continuous time Lucas market model with multiple goods and securities.

Overconfidence and Trading Volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors (number of trades, turnover). Approximately 3000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, the better than average effect, illusion of control, unrealistic optimism). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. The results hold even when we control for several other determinants of trading volume in a cross-sectional regression analysis. In connection with other recent findings, we conclude that the usual way of motivating and modelling overconfidence which is mainly based on the calibration literature has to be treated with caution. We argue that our findings present a psychological foundation for the differences of opinion explanation of high levels of trading volume. In addition, our way of empirically evaluating behavioral finance models - the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data - seems to be a promising way to better understand which psychological phenomena drive economic behavior.

Overconfidence and Trading Volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors (number of trades, turnover). Approximately 3000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, the better than average effect, illusion of control, unrealistic optimism). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. The results hold even when we control for several other determinants of trading volume in a cross-sectional regression analysis. In connection with other recent findings, we conclude that the usual way of motivating and modelling overconfidence which is mainly based on the calibration literature has to be treated with caution. We argue that our findings present a psychological foundation for the differences of opinion'' explanation of high levels of trading volume. In addition, our way of empirically evaluating behavioral finance models - the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data - seems to be a promising way to better understand which psychological phenomena drive economic behavior.

On the Predictability of the Stock Market Volatility: Does History Matter?

This study compares the performance of the ISD, the GARCH(1,1), the historical volatility estimates and of two lagged trading volume measures for predicting the Swiss Stock Market Index's (SMI) volatility. The ISD has a superior daily informational content than the GARCH(1,1) estimate and retains unbiased but decreasing explanatory power over up to 20 days ahead horizons. Mean and spread daily volume measures play a significant correcting role when forecasting stock market volatility over daily and longer intervals respectively and clearly dominate the GARCH(1,1) forecasts. Their significance emphasises heterogeneous horizon traders' influence on the SMI volatility time series properties

Dispersion of analysts' forecasts, precision of earnings, and trading volume reaction

We examine the relation between trading volume and belief dispersion and earnings precision around earnings announcement dates. No study to date has included both information factors (i.e., belief dispersion and earnings precision) simultaneously to explain trading volume behavior. Theoretical studies such as Dontoh and Ronen (1993) and Kim and Verrechia (1991) show that trading volume depends on (a) the contemporaneous dispersion of beliefs, (b) the prior dispersion of beliefs, and (c) the precision of earnings. We model the volume response coefficient on unexpected earnings as a function of earnings precision and the volume response coefficient on contemporaneous dispersion as a function of relative change in dispersion. Contemporaneous (prior) belief dispersion is measured using the coefficient of variation of analysts' forecasts for the next fiscal year during (one month prior to) the earnings announcement month. Earnings precision is measured by (a) the inverse of the coefficient of variation of the analysts' forecast errors for five years, and (b) earnings response coefficient to abnormal earnings. We find that trading volume is (a) positively associated with earnings precision and contemporaneous dispersion of beliefs, and (b) negatively associated with the relative change in dispersion. The results are robust to changes in the measures of trading volume; i.e., similar results are obtained when trading volume levels and abnormal trading volume measures are used. The results are also robust to the number of analysts' forecasts that are available for the firms.