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The Chinese stock market is a typical emerging market with special features that are very different from those of mature markets. The objective of this study is to investigate whether and how these features affect the volatility‐volume relation for Chinese stocks.

This paper examines the roles of the number of trades, size of trades, and share volume in explaining the volatility‐volume relation in the Shanghai Stock Exchange with high frequency trade data used.

The results confirm that the volatility‐volume relation is driven mainly by the number of trades on the Chinese stock market. The number of trades explains the volatility‐volume relation better than the size of trades. Furthermore, some results are obtained that differ from those of mature markets, such as the US market. The results show that the second largest sized trades affect the volatility more than other trades on the Chinese market.

The results show that, in the Shanghai Stock Exchange, informed traders camouflage their private information or manipulation behavior through the second largest sized trades. The results may have important implications for work explaining the volatility‐volume relation on the Chinese stock market, further providing a reference by which to regulate emerging markets.

The purpose of this paper is to investigate the profitability of momentum investment strategy and the predictive power of trading volume for equities listed in the Australian Stock Exchange.

Following the Lee and Swaminathan's approach, portfolios on past returns and past trading volume is constructed. In this approach, all stocks are ranked independently on the basis of past returns and past trading volume. The stocks are then assigned to one of five portfolios based on past returns and one of three portfolios based on trading volume over the same period.

A strong momentum effect for the Australian market during the period 1988 through 2002 is observed. Further, momentum plays an important role in providing information about stocks. Past trading volume appears to predict both the magnitude and persistence of price momentum.

Substantial momentum observed in monthly stock returns has investment implications. Abnormal returns vary from 0.3 to 7 per cent per month in the intermediate horizon.

This study provides an out of sample evidence by examining the relationship between “trading volume” (measured by the turnover ratio) and “momentum” strategies in an Australian setting.

In this study, we explore the empirical relationship between trading volume and volatility among KOSPI200 index stock market, futures and options markets. In particular, in explaining the volatility of each market, the trading in other markets, as well as the trading volume of other markets, also served as explanatory variables. In other words, cross-market effects of trading volume by investor types are analyzed. The empirical results show that there exist the cross-market effects of the relationship between trading volume and volatility in deeply integrated financial markets such as KOSPI200 index stock, futures and options markets. That is, the volatility of one market is explained by the trading volume of trader types in other financial markets. And, overall options trading increases the volatility of each market, while the overall futures trading volume of foreign investors reduce the volatility of each market. Trading volume of Individual investors does not reduce the volatilities of KOSPI200 index and futures markets. That is, trading volume of Individual investors in stock, futures, and options markets increase the volatilities of stock and futures. This implies that foreign investors are informed traders, whereas individual investors are liquidity traders.

The purpose of this paper is to bridge the gap between the existing theoretical and empirical studies by examining the asymmetric return–volume relationship. Indeed, the authors aim to shed light on the return–volume linkages for French-listed small and medium-sized enterprises (SMEs) compared to blue chips across different market regimes.

This study includes both large capitalizations included in the CAC 40 index and listed SMEs included in the Euronext Growth All Share index. The Markov-switching (MS) approach is applied to understand the asymmetric relationship between trading volume and stock returns. The study investigates also the causal impact between stock returns and trading volume using regime-dependent Granger causality tests.

Asymmetric contemporaneous and lagged relationships between stock returns and trading volume are found for both large capitalizations and listed SMEs. However, the causality investigation reveals some differences between large capitalizations and SMEs. Indeed, causal relationships depend on market conditions and the size of the market.

This paper explains the asymmetric return–volume relationship for both large capitalizations and listed SMEs by incorporating several psychological biases, such as the disposition effect, investor overconfidence and self-attribution bias. Future research needs to deepen the analysis especially for SMEs as most of the literature focuses on large capitalizations.

This empirical study has fundamental implications for portfolio management. The findings provide a deeper understanding of how trading activity impact current returns and vice versa. The authors’ results constitute an important input to build and control trading strategies.

This paper fills the literature gap on the asymmetric return–volume relationship across different regimes. To the best of the authors’ knowledge, the present study is the first empirical attempt to test the asymmetric return–volume relationship for listed SMEs by using an accurate MS framework.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The turn-of-the-month (TOM) effect is observed as one of the seasonalities in many markets. The author examines the TOM effect in the KOSDAQ market and finds that the effect is significant. The TOM effect in the KOSDAQ market is not due to size, turn-of-the-year, turn-of-the-quarter or index rebalancing effect. The author also finds that individual and institutional traders do not trade and buy more stocks at the TOM than on the rest days, not consistent with existing explanations of the increased liquidity by individual investors or institutional window-dressing activity. When the author investigated the net buying volume and net turnover of each investor, the net volume and turnover of individual investors at the TOM were significantly lower than those on the other days, rejecting the hypothesis of their increased demand. Interestingly, net foreign volumes at the TOM are significantly higher than on the other days. Finally, using panel regressions, the author finds that stocks with a higher net buying volume of foreigners for the TOM period tend to have higher returns, while stocks with a higher net buying volume of individual traders for the TOM period are likely to have lower returns. The results confirm that the TOM effect is not due to the increased demand of individual investors. Instead, higher net buying volume by foreigners may partially cause the TOM effect. Therefore, this study contributes to the literature by revealing the presence of the TOM effect in the KOSDAQ market and the foreign role in the anomaly in the market even mainly traded by retail investors.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

A number of US firms voluntarily de‐listed their stock from the Tokyo Stock Exchange (TSE) during the years 1977–97. We examine changes in trading volume, return volatility and implicit bid‐ask spreads in the U.S. stock exchange surrounding the de‐listing, and find evidence of an increase both in trading volume and bid‐ask spreads, particularly when the analysis is conditioned upon (a) trading volume on the TSE prior to de‐listing and (b) whether the de‐listing firm had operations in Japan. We also examine the daily stock price movement of the de‐listed firms and find a significantly negative price movement at the time of the de‐listing announcement, and also around the actual date of de‐listing. The results suggest a negative price response reflecting both a temporary information effect and also a more permanent valuation effect. Preliminary tests suggest that the latter is not related to the decrease in liquidity.

This study aims to revisit the stock price–volume relations, providing new evidence from the emerging market of Qatar. In particular, three main issues are examined using both aggregate market- and sector-level data. First, the return–volume relation and whether or not this relation is asymmetric. Second, the common characteristics of return volatility; and third, the nature of the relation between trading volume and return volatility.

The study uses the OLS and VAR modeling approaches to examine the contemporaneous and dynamic (causal) relations between index returns and trading volume, respectively, while an EGARCH-X(1,1) model is used to analyze the volatility–volume relation. The data set comprises daily index observations and the corresponding trading volumes for the entire market and the individual seven sectors of the Qatar Exchange (i.e. banks and financial services, consumer goods and services, industrials, insurance, real estate, telecommunications and transportation).

The empirical analysis reports evidence of a positive contemporaneous return–volume relation in all sectors barring transportation and insurance. This relation appears to be asymmetric for all sectors. For the market and almost all sectors, there is no significant causality between returns and volume. By and large, these findings lend support for the implications of the mixture of distributions hypothesis (MDH). Lastly, the information content of lagged volume seems to have an important role in predicting the future dynamics of return volatility in all sectors, with the industrials being the exception.

The findings provide important implications for portfolio managers and investors, given that the volume of transactions is generally found to be informative about the price movement of sector indices. Specifically, tracking the behavior of trading volume over time can give a broad portrayal of the future direction of market prices and volatility of equity, thereby enriching the information set available to investors for decision-making.

Based on both market- and sector-level data from the emerging stock market of Qatar, this study attempts to fill an important void in the literature by examining the return–volume and volatility–volume linkages.

The real estate markets in Asia have attracted significant investor attention as they have grown rapidly in recent years. Both local and foreign investors continue to display a strong appetite for Asian real estate investment projects. Given the different characteristics of listed real estate stocks, the purpose of this paper is to focus on the causal relations between the financial variables of these stocks. This financial analysis can help investors to understand the characteristics of listed real estate companies, provide implications for optimal asset allocation decisions, and also increase the predictability of portfolio returns.

In this research, the paper investigates the contemporaneous and causal relations between stock returns, trading volume and volatility in a domestic market context and between different national markets for listed real estate companies in seven Asian economies.

The paper finds that there are positive contemporaneous relations between trading volume and both returns and absolute returns. When the paper examines the causal relations between the financial variables, the evidence implies that current trading volume helps to explain the returns indirectly by leading return volatility; however, trading volume does not help to explain future returns directly. Extending the causality test to international markets, the listed real estate portfolios of the four Southeast Asian countries are found to be more closely correlated than those of the other three countries studied here. Among the four Southeast Asian countries, Singapore, the only developed country, is found to play an influential role, its current financial variables having predictive power for the other countries.

This research provides global investors with a better understanding of the Asian listed real estate market, showing that trading volume contains important information regarding returns, that the characteristics of listed real estate companies are closer to those of the financial market than those of the real estate markets, and that the markets of the major economies have extensive influence over the smaller markets. Moreover, given the scarcity of research on the performance of Asian listed real estate companies themselves, this study improves the completeness of the academic literature.