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We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

The purpose of this paper is to test the disposition effect at market level and propose an appropriate reference point for testing disposition at market level.

This is an empirical study conducted on 500 index stocks of NSE500 (National Stock Exchange). Winning and losing days for each stock are calculated using 52-week high and low prices as reference points. To test disposition effect, abnormal trading volumes of stocks are regressed on their percentage of winning (losing) days. Further using ANOVA, the difference between mean of percentage of winning (losing) days of high abnormal trading volume deciles and low abnormal trading volume deciles is tested.

Results show that a stock’s abnormal trading volume is positively influenced by the percentage of winning days whereas percentage of losing days show no such effect. Findings are consistent even after controlling for volatility and liquidity. ANOVA results show the presence of high percentage of winning days in higher deciles of abnormal trading volumes and no such pattern in case of losing days confirms the presence of disposition effect. Further an ex post analysis indicates that disposition prone investors accumulate losses.

This is the first study, which proposes the use of 52-week high and low prices as reference points to test the market-level disposition effect. Findings of this study enhance the limited literature available on disposition effect in emerging markets by providing evidence from Indian stock markets.

The purpose of this paper is to evaluate the profitability of technical trading relative to buy-and-hold (BH) strategy at firm level, controlling for firm size and trading volume.

This paper applies variable-length moving averages (VMAs) thoroughly to each and every stock listed on Taiwan Stock Exchange (TWSE) and computes the excess returns of technical trading relative to BH strategy. The samples are further grouped by firm size and trading volume. Furthermore, possible data snooping bias is investigated by employing Hansen’s (2005) Superior Predictive Ability tests.

The result shows that VMAs outperform the BH strategy. The profitability of VMAs, remarkably, is positively associated with size and trading volume. After correcting for data snooping bias, VMAs with longer moving averages outperform VMAs with shorter moving averages. The evidence suggests that size and volume information is accountable for trend projection.

Unlike past studies simply applying technical trading rules to market indices, portfolios, or selected stocks, this paper evaluates the profitability of technical trading by applying VMAs comprehensively to each and every individual stock listed on TWSE controlling for the effect of firm size and trading volume, providing more practical insights for trading individual stocks.

Prior research posits that traders with short-lived information favor lit exchanges over dark pools due to execution certainty. This paper aims to focus on the relation between informed trading based on firm fundamentals and dark pool volume because the preferred venue for traders with longer-lived information is less certain.

The authors examine the effect of short interest, a proxy for informed traders with long-lived information, on dark pool volume using fixed effects, first difference and instrumental variable approaches. They examine the effect of dark pools on the profitability of long-lived information using market- and characteristic-adjusted returns.

The proportion of trading volume executed in dark pools is positively correlated with short interest. This result is stronger for stocks that suffer from greater uncertainty and stocks targeted by transient institutional investors. Short sellers profit substantially from their information as subsequent returns are lower for heavily shorted stocks with greater dark pool volume.

In 2014, the Financial Industry Regulatory Authority began making trading data available for dark pools. Before that, only limited information was publicly available. The authors use that data to shed more light on dark pools activity.

The evidence presented in the paper helps inform the current discussion about the role and regulation of dark pools.

This is the first study to show that informed traders with long-lived information favor dark pools due to their opacity and the possibility of price improvement.

The purpose of this paper is to investigate the immediate impact of firm-specific announcements on the trading volume of individual and institutional investors on the Australian Securities Exchange (ASX), during a period when the market becomes fragmented.

This study uses intraday trading volume data in five-minute intervals prior to and after firm-specific announcements to measure individual and institutional abnormal volume. There are 70 such intervals per trading day and 254 trading days in the sample period. The first 10 minutes of trading (from 10.00 to 10.10 a.m.) is excluded to avoid the effect of opening auction and to ensure consistency in the “starting time” for all stocks. The volume transacted during five-minute intervals is aggregated and attributed to individual or institutional investors using Broker IDs.

Institutional investors exhibit abnormal trading volume before and after announcements. However, individual investors indicate abnormal trading volume only after announcements. Consistent with outcomes expected from a dividend washing strategy, abnormal trading volume around dividend announcements is statistically insignificant. Both individual and institutional investors’ buy volumes are higher than sell volumes before and after scheduled and unscheduled announcements.

The study is Australian focused, but the results are applicable to other limit order book markets of similar design.

The results add to the understanding of individual and institutional investors’ trading behaviour around firm-specific announcements in a securities market with continuous disclosure.

The results add to the understanding of individual and institutional investors’ trading behaviour around firm-specific announcements in a securities market with continuous disclosure.

These results will help regulators to design markets that are less predatory on individual investors.

The purpose of this paper is to examine the change in speed of dissemination of order flow information on stock volatility of return in 79 traded companies at the Cairo and Alexandria Stock Exchange (CASE).

The paper examines the interaction of volatility and volume in 79 traded companies in CASE over a period from January 1998 to May 2005 and provides support for the TGARCH specification for explaining the daily time dependence on the rate of information arrival to the market for stocks traded on CASE.

The paper finds that information size and direction have a negligible effect on conditional volatility and, as a result, the presence of noise trading and speculative bubbles is suspected. It was found that the persistence in volatility is not eliminated when lagged or contemporaneous trading volume is incorporated into a GARCH model. It is shown that, when volume is further broken down into its expected and unexpected components, volatility persistence decreases. This is especially true after May 2001, which marks the beginning of a succession of major stock market reforms. It was also found that anticipated information shocks can have a negative impact on the volatility of return, particularly prior to May 2001.

The decrease in the negative relationship between expected volume and volatility after May 2001 suggests that trading efficiency and information dissemination have improved. This is an important finding for CASE as it encourages the reform momentum and reinsures foreign investors.

The purpose of this paper is to investigate whether there is any difference across individual investors, domestic and foreign institutional investors in trading volume responses to analyst reports. The authors also examine the determinants of trading volume responses using firm as well as forecast characteristics.

The authors use trading data from the Korean equity market. The authors divide investors into three classes of investors; namely, individual investors, domestic institutional investors, and foreign institutional investors. The authors then examine whether the trading responses to analyst reports vary across investor types, and how firm characteristics and characteristics of analyst reports influence the trading activities on the release dates across investor types.

Individual investors are the most responsive investor group, being responsive to analyst reports on small, neglected firms with large inside ownership as well as to analyst reports with optimistic forecasts. Domestic institutional investors are responsive to reports on neglected firms with high return volatility while foreign institutional investors show least responses.

There are few studies that investigate whether the trading responses to analyst reports vary across investor types and how firm characteristics and characteristics of analyst reports influence the trading activities on the release dates across investor types. Taking advantage of the trading volume data for the three main investor types in the Korean stock market, the authors study the trading volume responses for each investor type and make comparisons across investor types.

The purpose of the paper is to study the explainability of expected and unexpected trade volume and open interest as information flow, and the asymmetric effects of unexpected shocks to the information flow on volatility in Indian commodity markets.

After having dissected into expected and unexpected components, the effects of trade volume and open interest on volatility are tested. A new interaction term is also added to measure asymmetry. Four commodities, namely, cumin, soy oil and pepper in food commodity category and guar seed in non-food commodity category are selected for the present study. These four commodities are selected based on their economic and trading importance, i.e. weight in the index and trading volume (liquidity).

It is mostly found that unexpected volatility is positively related to the volatility, and the effect of the unexpected component is more than the expected component of the trading volume. The expected open interest is negatively related to volatility while the unexpected open interest is found to be positively related in all the commodities. The effects of unexpected component are higher than the expected open interest. The effects of positive unexpected shocks to the trade volume are more than those of negative unexpected shocks. The evidence of asymmetry in unexpected shocks to open interest is inconclusive. However, the inclusion of volume of trade and open interest could not vanish away the volatility. This indicates that the trading volume and open interest are not the variable with mixed distribution. Thus, it contrasts the assumption of mixed distribution hypothesis, and they do not proxy the flow of information.

It is the unexpected information flow that matters more than the expected one. Positive unexpected shocks to trade volume are more influential than the negative shocks. However, trade volume and open interest are not good proxy of information flow in the Indian commodity markets. This study would definitely broaden the horizon of managers and policymakers to understand the volatility better.

The paper is unique in terms of understanding the effect of expected and unexpected trade volume and open interest and the asymmetric effects of unexpected shocks to volume and open interest in the Indian commodity markets.

The purpose of this paper is to examine the contemporaneous and causal relationship between returns (volatility) and trading volume in the Indian currency futures market for selected currency pairs; USD-INR, EUR-INR, GBP-INR and JPY-INR, from August 2008 to December 2014.

The data for all the currency futures series has been taken from National Stock Exchange of India Limited which represents the daily settlement prices along with trading volume. The contemporaneous returns-volume relation is tested using the generalized method of moments, and Granger-causality framework impulse response function is used to test the predictive ability of returns (volatility) and volume for each other.

The author reports a positive contemporaneous relationship between futures returns and trading volume which persists even after controlling for heteroskedasticity providing support to mixture of distribution hypothesis. The results show a unidirectional Granger causality from futures returns to volume. However, there is a significant bidirectional Granger causality between returns volatility and volume lending support to sequential arrival of information hypothesis. Next, the results for cross-currencies show significant influence of US dollar on the volume and returns of all other currencies. Overall, the author suggests that the short- to medium-term movements in the currency markets are dominated by market microstructure and not by fundamentals.

The findings of this paper are very important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant relationship between futures returns (volatility) and trading volume implies that the current trading volume help predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Further, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market. Based on returns-volume relation, they need to set forth market restrictions such as daily price movement and position limits.

To the best of the knowledge, no study has yet investigated the forecast ability of trading volume to price changes and their volatility in the Indian currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price-volume relationship in the Indian currency futures market.

This study examines the role of behavioural factors, such as confidence, optimism, pessimism and rational expectation, in affecting investment decisions in the Pakistani stock market.

Using daily trading data of Karachi Stock Exchange-100 index from January 2012 to December 2015, different regression models, including descriptive statistics and stationarity tests, are performed.

Results indicate that stock market trading has suffered from pessimistic behaviour of investors. In the first model, the authors find a positive sign of confidence and negative sign of optimism with the trading volume. The second model shows a positive role of confidence and rational expectations in affecting the trading volume in daily, Monday and Friday samples. The results of the third model show a negative sign of both optimism and rational expectation with the trading volume. Furthermore, the next model shows a negative sign of confidence combined with pessimism while testing their relationship with the trading volume. Finally, results of the final model suggest that optimism negatively affects the trading volume, and on the other hand, pessimism has a positive impact on the trading volume.

The method and empirical testing of behavioural biases and their relationship with economic variable used in this study seem to be a promising way to better understand the role of psychology in deriving financial decisions for academics and policymakers.

This study uses secondary data for measuring behavioural biases and decomposes the effect between rational expectation and behavioural biases.