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This study explores the Granger causal relationship between return and volume in the KOSPI200 spot and option markets for the period from December 13. 2002 to December 9. 2004. using minute-by-minute data. Specifically, we examine the lead-lag relationship among OPtion volume, option return, cash volume, and cash return to determine whether option volume and return impact cash return.

Our results show that option volume has no direct impact on cash return as cash return unilaterally leads option volume‘ While option volume impacts cash volume. cash return unilaterally leads cash volume. implying no indirect impact of option volume on cash return.

However, there is evidence that option return impacts cash return directly, given a bilateral causality between option return and casll return. Option return also impacts cash volume, but again cash volume has no impact on cash return. meaning no indirect impact of option return on cash return. Our findings were generally robust across days of the week and different maturities. Finally, we analyzed lead-lag relationship within the option market. and found a bilateral causality between option volume and option return. This implies that option volume may impact cash return indirectly via option return.

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.

Using the next-day and next-week returns of stocks in the Korean market, we examine the association of option volume ratios – i.e. the option-to-stock (O/S) ratio, which is the total volume of put options and call options scaled by total underlying equity volume, and the put-call (P/C) ratio, which is the put volume scaled by total put and call volume – with future returns. We find that O/S ratios are positively related to future returns, but P/C ratios have no significant association with returns. We calculate individual, institutional, and foreign investors’ option ratios to determine which ratios are significantly related to future returns and find that, for all investors, higher O/S ratios predict higher future returns. The predictability of P/C depends on the investors: institutional and individual investors’ P/C ratios are not related to returns, but foreign P/C predicts negative next-day returns. For net-buying O/S ratios, institutional net-buying put-to-stock ratios consistently predict negative future returns. Institutions’ buying and selling put ratios also predict returns. In short, institutional put-to-share ratios predict future returns when we use various option ratios, but individual option ratios do not.

This study examines the impact of the trading volume on Initial Public Offering (IPO) initial return in the context of an emerging market from January 2006 to December 2016. Models consist of hierarchical and multiple regressions have been evaluated. Our results show, firstly, IPO provides an average of 21.90% of initial return to investors on the first trading day, 9.08% of return on the second day of trading, and 7.12% of return on the third day of return. Secondly, there is a positive relationship between the oversubscription ratio and initial return and no relationship between trading volume and initial return on the first three trading day. Thirdly, the trading volume does not act as a moderator that worsens the relationship between the oversubscription ratio and initial return. Lastly, this study shows that investors should actively participate in the subsequent trading of an IPO. Higher participation will bring greater liquidity and shareholder wealth in the stock market. To the authors' knowledge, this is the first study on the moderating effect of trading volume on IPO initial return in an emerging market.

When a stock is added into the S&P 500 Index, it in effect becomes cross-listed in the Index derivative markets. When index-based trading strategies such as index arbitrage are executed, the component stocks are directly affected by such trading. We find increased volatility of daily returns, plus increased trading volume for the underlying stocks. Utilizing a list of S&P 500 Index composition changes over the period September 1976 to December 2005, we study the market-adjusted volume turnover and return variance of the stocks added to and deleted from the Index. The results indicate that after the introduction of the S&P 500 Index futures and options contracts, stocks added to the S&P 500 experience statistically significant increase in both trading volume and return volatility. Both daily and monthly return variances increase following index inclusion. When stocks are removed from the index, though, neither volatility of returns nor trading volume experiences any significant change. So, we have new evidence showing that Index inclusion changes a firm's return volatility, and supporting the destabilization hypothesis.

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.

Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.

As the world's largest emerging market, the evidence of momentum effect in China is also mixed. Meanwhile, prior studies mainly examined individual stock momentum in China, with little concern for industry momentum and its relationship with trading volume. The motivation of this study is to investigate industry momentum in China and examine whether trading volume can enhance its profitability.

Firstly, the authors test the existence of industry momentum in China; secondly, the authors test the correlation between trading volume and momentum returns using the double ranking method; finally, the authors test whether trading volume enhances the momentum returns using Fama–French five-factor model.

The authors find that there is a significant industry momentum effect in China, and the momentum returns jointly come from winner and loser portfolios. The intervals between the formation and holding periods have an impact on the performance of momentum portfolios. In terms of trading volume, the authors find that high-volume industries have industry momentum effects while low-volume industries do not. The industry momentum strategies achieve higher excess returns in high-volume industries.

Prior literature found higher momentum returns in low-volume stocks in China, but the research in this study suggests that implementing an industry momentum strategy in low-volume industries will miss out on higher returns or even bring losses, and instead the investors should invest in high-volume industries to get the best performance.

This study extends existing research by focusing on industry momentum and its relationship with trading volume in the Chinese stock market and finds an interesting relationship between industry momentum returns and trading volume, which is different from related studies.

Using intraday prices for the S&P 500 and Nikkei Stock Average stock indexes and aggregate trading volume for the New York and Tokyo Stock Exchanges, we show how short-run comovements between national stock market returns vary over time in a way related to the trading volume and liquidity in those markets. We frame our analysis in the context of the heterogeneous-agent models of trading developed by Campbell, Grossman and Wang (1993) and Blume, Easley and O’Hara (1994) and Wang (1994) which predict that trading volume acts as a signal of the information content of a given price move. While we find that there exists significant short-run dependence in returns and volatility between Japan and the U.S., we offer new evidence that these return “spillovers” are sensitive to interactions with trading volume in those markets. The cross-market effects with volume are revealed in both close-to-open and open-to-close returns and often exhibit non-linear patterns that are not predicted by theory.

Using vector autoregressive modelling (VAR) and Granger causality tests, this paper attempts to empirically investigate the dynamic relationship between return and volume of transactions of two main cryptocurrencies: Bitcoin and Ethereum.

Based on a generalized autoregressive conditional heteroskedasticity (GARCH) model with a transaction volume parameter in the conditional volatility equation.

The results provide empirical evidence of a positive contemporaneous relationship between the variation in transaction volume and the daily return of Bitcoin and Ethereum. The results also show that the conditional volatility of the returns is affected by the past volatility, which implies weak-form inefficiency for both Bitcoin and Ethereum markets. The results of the VAR model, testing Granger causality, indicate that the volume of transactions Granger-Causes Bitcoin and Ethereum returns. Furthermore, the findings show a Granger causal relation from returns to volume.

This result suggests that cryptocurrency returns can predict transaction volumes and vice versa.