Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

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 US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US 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 US and Hong Kong has very weak predictive power for Chinese financial market variables.     

Foreign exchange risk and the predictability of carry trade returns

This paper provides an empirical investigation of the time-series predictive ability of foreign exchange risk measures on the return to the carry trade, a popular investment strategy that borrows in low-interest currencies and lends in high-interest currencies. Using quantile regressions, we find that higher market variance is significantly related to large future carry trade losses, which is consistent with the unwinding of the carry trade in times of high volatility. The decomposition of market variance into average variance and average correlation shows that the predictive power of market variance is primarily due to average variance since average correlation is not significantly related to carry trade returns. Finally, a new version of the carry trade that conditions on market variance generates performance gains net of transaction costs.     

The Other January Effect: international evidence

This paper investigates the predictive power of stock market returns in January for the subsequent 11 months’ returns across 19 countries, thereby contributing to the literature on stock market seasonalities. Only 2 out of 19 countries’ stock markets exhibit a robust Other January Effect. In the light of this evidence, we conclude that the Other January Effect is not an international phenomenon.     

The cost of capital- the practitioners view

The application of a SWARCH model to stock market returns allows one to endogenously determine the regime dependence of the stock market volatility. Comparison of the results from a sample of daily data from five major stock markets shows that the majority of the markets switch regimes simultaneously. This fact is used to investigate the relation between market volatility and the behaviour of the variance—;covariance matrix. It is found that the international variance—;covariance matrix is not stable and that changes in the matrix are dependent on the volatility regime. A high level of variance causes an increase in the average correlation coefficient. The co-movement of the markets is further described by a steady increase in the covariance over the whole sample period. It can be shown that both the time component and the regime dependence of the average correlation have separate and significant explanatory power.     

Risk-Adjusted Performances of World Equity Indices

This article investigates whether equity indices of twenty-four emerging and twenty-eight developed markets compensate their investors equally after adjusting for total or downside risk, and examines the predictive power of reward-to-risk ratios for expected market returns. We find that when all fifty-two markets are ranked based on their alternative reward-to-risk ratios, almost all of the countries in the top (bottom) quartile are emerging (developed) markets. The pooled means of the reward-to-risk ratios are also significantly higher for emerging markets. Both portfolio and regressions analysis reveal that there is a significantly positive relation between various reward-to-risk metrics and expected market returns.     

An analysis of trading strategies in eleven European stock markets

Abstract

In recent years, the validity of the weak form efficient market hypothesis (EMH) has been called into question as several studies have uncovered evidence that technical trading rules have predictive ability with respect to both developed and emerging stock market indices. This study analyses the forecasting power of 2 of the most popular trading rules using index data for a selection of 11 European stock markets over the January 1991 to December 2000 period. The findings indicate that the emerging markets included in this paper are informationally inefficient; these markets displayed some degree of predictability in their share returns, although the developed markets did not. Furthermore, the results point to large differences in the performance of the rules examined; while small size filters consistently outperformed the buy-and-hold strategy in the emerging markets examined even after the consideration of transaction costs, the performance of the moving average rules was erratic and varied dramatically from market to market.     

Sources of risk and expected returns in global equity markets

This paper empirically examines multifactor asset pricing models for the returns and expected returns on eighteen national equity markets. The factors are chosen to measure global economic risks. Although previous studies do not reject the unconditional mean variance efficiency of a world market portfolio, our evidence indicates that the tests are low in power, and the world market betas do not provide a good explanation of cross-sectional differences in average returns. Multiple beta models provide an improved explanation of the equity returns.     

Option trading after the opening bell and intraday stock return predictability

Prior literature finds that information is reflected in option markets before stock markets, but no study has explored whether option volume soon after market open has predictive power for intraday stock returns. Using novel intraday signed option-to-stock volume data, we find that a composite option trading score (OTS) in the first 30 min of market open predicts stock returns during the rest of the trading day. Such return predictability is greater for smaller stocks, stocks with higher idiosyncratic volatility, and stocks with higher bid–ask spreads relative to their options’ bid–ask spreads. Moreover, OTS is a significantly stronger predictor of intraday stock returns after overnight earnings announcements. The evidence suggests that option trading in the 30 min after the opening bell has predictive power for intraday stock returns.     

The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion

This study analyzes how the 2008 and 2010 financial crises, which began in the US and Greece respectively, affected the Hurst exponents of index returns of the stock markets of Belgium, France, Greece, Japan, the Netherlands, Portugal, the UK and US. We perform two innovative statistical tests for this purpose. The first assesses whether the returns exhibit a long memory in the pre-crisis and crisis periods and determines the extent to which the Hurst exponents, calculated with the multifractal detrended moving average technique (MFDMA), differ from the tranquil to the crisis periods. The second test uses copula models to assess whether the correlation between the local Hurst exponents of the markets where the crises originated and those of the other markets increased due to the crises. The results of the first test suggest that although most of the returns exhibit a long memory in the 2008 crisis period, this is not the case in either the pre-crisis or the 2010 crisis periods. These findings shed light on the dynamics of market efficiency. The results of the second test show a significant increase in correlation between the local Hurst exponents of several markets, suggesting the existence of financial contagion. We observed that the 2008 crisis had a greater impact on the memory properties of stock returns than the 2010 financial crisis.     

Can Managers Forecast Aggregate Market Returns?

Previous studies have found that the proportion of equity in total new debt and equity issues is negatively correlated with future equity market returns. Researchers have interpreted this finding as evidence that corporate managers are able to predict the systematic component of their stock returns and to issue equity when the market is overvalued. In this article we show that the predictive power of the share of equity in total new issues stems from pseudo‐market timing and not from any abnormal ability of managers to time the equity markets.     

Predictability of Stock Returns: Robustness and Economic Significance

This article examines the robustness of the evidence on predictability of U.S. stock returns, and addresses the issue of whether this predictability could have been historically exploited by investors to earn profits in excess of a buy-and-hold strategy in the market index. We find that the predictive power of various economic factors over stock returns changes through time and tends to vary with the volatility of returns. The degree to which stock returns were predictable seemed quite low during the relatively calm markets in the 1960s, but increased to a level where, net of transaction costs, it could have been exploited by investors in the volatile markets of the 1970s.     

The calm after the storm: implied volatility and future stock index returns

This article explores the predictive power of five implied volatility indices for subsequent returns on the corresponding underlying stock indices from January 2000 through October 2013. Contrary to previous research, very low volatility levels appear to be followed by significantly positive average returns over the next 20, 40 or 60 trading days. Rolling trading simulations show that positive adjusted excess returns can be achieved when long positions in the stock indices are taken on days of very low implied volatility. This may be a hint that market inefficiencies exist in some markets, especially outside the USA. The excess returns measured against a buy and hold benchmark are significant for the German and Japanese market when tested with a bootstrap methodology. The results are robust against a broad spectrum of specifications.     

Financial ratios used by equity analysts in Mexico and stock returns

A sample of recommendation reports by equity analysts covering Mexican publicly traded firms in Mexico is studied. We propose a set of “most preferred” financial ratios from this sample. It is found that the most preferred ratios by equity analysts, a group of sophisticated users, are not those ratios typically covered in financial textbooks. Moreover, by using panel regression analysis, we test the relationship between financial ratios and leading stock returns during the 1995–2011 period. Overall, consistent with the efficient market hypothesis, the results show that estimates of financial ratios most preferred by equity analysts have predictive power on 1-year future stock returns. We find no evidence of predictive power on 2-year stock returns.     

Speculators,commodities and cross-market linkages

We use a unique, non-public dataset of trader positions in 17 U.S. commodity futures markets to provide novel evidence on those markets' financialization in the past decade. We then show that the correlation between the rates of return on investible commodity and equity indices rises amid greater participation by speculators generally, hedge funds especially, and hedge funds that hold positions in both equity and commodity futures markets in particular. We find no such relationship for commodity swap dealers, including index traders (CITs). The predictive power of hedge fund positions is weaker in periods of generalized financial market stress. Our results support the notion that who trades helps predict the joint distribution of commodity and equity returns. We find qualitatively similar but statistically weaker results using a proxy for hedge fund activity based on publicly available data.     

Time-varying neural network for stock return prediction

We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly US stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators exhibit time-varying predictive power on stock returns. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.     

Linkages between Indian and US financial markets: impact of global financial crisis and Eurozone debt crisis

This paper examines inter-linkages between Indian and US equity, foreign exchange and money markets using the vector autoregressive-multivariate GARCH-BEKK framework. We investigate the impact of global financial crisis (GFC) and Eurozone debt crisis (EZDC) on the conditional volatility and conditional correlation estimates derived from the multivariate GARCH model for Indian and US financial markets. Our results indicate that there is significant bidirectional causality-in-mean between the Indian stock market returns and the Rs./USD market returns, and significant unidirectional causality-in-mean from the US stock market returns to the Indian stock market returns. As regards volatility spillovers, we find that volatility in the Indian stock market rises in response to domestic as well as US financial market shocks but Indian financial market shocks do not impact the US markets. Further, impact of the recent crisis episodes on the covariance matrix is found to be significant. We find that volatility in the Indian and US financial markets significantly amplified during GFC. The conditional correlations across asset markets were significantly accentuated in the wake of the two crisis episodes. The impact of GFC on cross-market conditional correlations is higher for majority of the asset market pairs in comparison to the EZDC.     

Why do options prices predict stock returns? Evidence from analyst tipping

We study the role of analysts and options traders in the information transmission between options and stock markets. We first show that the predictive power of option implied volatilities (IVs) on stock returns more than doubles around analyst-related events, indicating that a significant proportion of the options predictability on stock returns comes from informed options traders’ information about upcoming analyst-related news. We examine three explanations for this finding: tipping, reverse tipping and common information. We find that analyst tipping to options traders is the most consistent explanation of these predictive patterns.     

Have European Stocks become More Volatile? An Empirical Investigation of Idiosyncratic and Market Risk in the Euro Area

We examine the dynamics of idiosyncratic risk, market risk and return correlations in European equity markets using weekly observations from 3515 stocks listed in the 12 euro area stock markets over the period 1974–2004. Similarly to Campbell et al. (2001) , we find a rise in idiosyncratic volatility, implying that it now takes more stocks to diversify away idiosyncratic risk. Contrary to the US, however, market risk is trended upwards in Europe and correlations are not trended downwards. Both the volatility and correlation measures are pro‐cyclical, and they rise during times of low market returns. Market and average idiosyncratic volatility jointly predict market wide returns, and the latter impact upon both market and idiosyncratic volatility. This has asset pricing and risk management implications.     

Does volume help in predicting stock returns? An analysis of the Australian market

This paper presents an analysis of the relationship between trading volume and stock returns in the Australian market. We test this hypothesis by using data from a sample of firms listed on the Australian stock market for a period of 5 years from January 2001 to December 2005. We explore this relationship by focusing on the level of trading volume and thin trading in the market. Our results suggest that trading volume does seem to have some predictive power for high volume firms and in certain industries of the Australian market. However, for smaller firms, trading volume does not seem to have the same predictive power to explain stock returns in Australia.     

Forecasting market states

We propose a novel methodology to define, analyze and forecast market states. In our approach, market states are identified by a reference sparse precision matrix and a vector of expectation values. In our procedure, each multivariate observation is associated to a given market state accordingly to a minimization of a penalized Mahalanobis distance. The procedure is made computationally very efficient and can be used with a large number of assets. We demonstrate that this procedure is successful at clustering different states of the markets in an unsupervised manner. In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates states prevalently to pre- and post-crisis periods with one state gathering periods with average positive returns and the other state periods with average negative returns, therefore discovering spontaneously the common classification of ‘bull’ and ‘bear’ markets. In another experiment, with again one hundred log-returns and two states, we demonstrate that this procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy. This methodology opens the way to a range of applications in risk management and trading strategies in the context where the correlation structure plays a central role.