Is There a Size Effect in the Pricing of Stocks in the Chinese Stock Markets?: The Case of Bull Versus Bear Markets

Size effect studies generally suggest that a return premium exists for small firms. While the size effect has mostly disappeared in recent years in mature markets (e.g., US and UK), it remains mostly strong in developing markets. The purpose of this paper is to examine the relationship between firm size and excess stock returns in the Chinese stock markets, and to examine this effect in both a bull and bear market. No studies have previously examined these relationships in the Chinese markets. The results of the study indicate that a size effect exists in the Chinese stock markets over the 6-year period from 1998 to 2003. We find small firms have significantly greater excess returns than large firms. Moreover, small firms are found to have a stronger reaction to the direction of the market than large firms. Small firms have significantly greater positive excess returns than large firms during the bull market. However, small firms have significantly greater negative returns (using total market value), or no significant difference in returns (using float market value) during the bear market period.     

Revisiting the empirical linkages between stock returns and trading volume

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.     

Market States and the Effect on Equity REIT Returns due to Changes in Monetary Policy Stance

This study investigates the effect of changes in monetary policy on US equity real estate investment trust (EREIT) returns in lower and higher return ranges during bull, bear, and volatile stock market states using quantile regression. Results show that EREIT returns are sensitive to changes in monetary policy at different EREIT return ranges in different market states. During bull markets, changes in monetary policy have a significant negative impact on EREIT when investors have lower expectations of real estate price increases, but are not effective when investors have higher expectations of real estate price increases. During volatile and bear markets, EREIT returns are not sensitive to changes in monetary policy stance. Results also show that EREIT returns respond positively to stock returns in various states and conditions.     

Liquidity Provision of Limit Order Trading in the Futures Market Under Bull and Bear Markets

Abstract:  This study investigates how limit orders affect liquidity in a purely order-driven futures market. Additionally, the possible asymmetric relationship between market depth and transitory volatility in bull and bear markets and the effect of institutional trading on liquidity provision behavior are examined as well. The empirical results demonstrate that subsequent market depth increases as transient volatility increases in bull markets. Market depth exhibits significantly positive relationship to subsequent transient volatility in bull markets. Additionally, although trading volume positively influences transient volatility in bull markets, no such relationship exists in bear markets. Liquidity provision decreases when institutional trading activity intensifies during bear markets. Thus, liquidity provision for limit orders differs between bull and bear markets.     

Predicting bear and bull stock markets with dynamic binary time series models

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for a dynamic structure in the binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns than the buy-and-hold trading strategy in a small-scale market timing experiment.     

Dynamic linkages between monetary policy and the stock market

This paper examines the dynamic linkages between the federal funds rate and the stock market during the 1970–2004 period using the VAR methodology. We detected a disconnection between Fed actions and market responses in the 1990s relative to the 1970s and 1980s. Upon further analyses, we observed asymmetric effects of monetary policy actions on the stock market and that such actions were more turbulent during bear markets than bull markets. Overall, our results appear to suggest that there was consistent dynamic relationship between the conduct of monetary policy and the corresponding behavior by the stock market during the last three decades.     

Forecasting Bull and Bear Markets: Evidence from China

This article extends previous empirical research to forecast Chinese bull and bear stock markets by using three types of binary probit time series models, which are static, autoregressive, and dynamic autoregressive models. This study shows that the dynamic auto regressive model performs the best both in- and out-of-sample. The inflation and market return variables significantly affect the market forecast. The dynamic autoregressive model has successfully forecast the bull and bear markets since 2007. The investment strategy based on this model performs better than the simple buy-and-hold strategy, especially after the Chinese government reformed the non-tradable shares in 2005.     

Weekend gold returns in bull and bear markets

This study examines the weekend effect in gold returns during bull and bear markets over the period 1975 through 2011. It shows that gold returns from close on Friday to close on Monday are significantly lower than returns during the rest of the week. This result is due largely to gold returns during bear markets. During gold bull markets, gold weekend returns are not significantly different from weekday returns. The study shows that the effect has substantial economic implications for gold investors. The effect is shown to be related to a significantly negative skewness in the weekend returns.     

Monetary policy and stock market dynamics across monetary regimes

This paper examines the dynamic linkages between monetary policy and the stock market during the three distinct monetary regimes of Burns, Volcker and Greenspan since the 1970s. Some major findings are the following. First, in the 1990s it appears that there was a disconnection between Federal Reserve actions (via the federal funds rate) and responses by the stock market. Second, the impact of inflation on the stock market did not surface as significant in the later parts of 1980s and the 1990s. And third, significant asymmetric effects of monetary policy on the stock markets were observed throughout each monetary regime but these were more pronounced during bear markets than bull markets. These results suggest that there was no consistent dynamic relationship between monetary policy and the stock market and that the nature of such dynamics was different in each of the three monetary regimes.     

Rumor Clarification and Stock Returns: Do Bull Markets Behave Differently from Bear Markets?

This paper analyzes the effects of official rumor clarification on Chinese stock returns under different market conditions. The results show that the average cumulative abnormal return after the clarification event is significantly positive in a bull market, and significantly negative in a bear market. The results are robust across various types of rumors, including rumors of mergers and acquisitions, asset restructuring, and positive changes in a firm's operations. Moreover, in both bull and bear markets, investors are unable to distinguish between rumors that prove true and those that prove false, or between strong and weak rumor denial. Furthermore, investors are also unable to adjust their strategies accordingly.     

Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method

This study combines the variational mode decomposition (VMD) method and static and time-varying symmetric and asymmetric copula functions to examine the dependence structure between crude oil prices and major regional developed stock markets (S&P500, stoxx600, DJPI and TSX indexes) during bear, normal and bull markets under different investment horizons. Furthermore, it analyzes the upside and downside short- and long-run risk spillovers between oil and stock markets by quantifying three market risk measures, namely the value at risk (VaR), conditional VaR (CoVaR) and the delta CoVaR (∆CoVaR). The results show that there is a tail dependence between oil and all stock markets for the raw return series. By considering time horizons, we show that there is an average dependence between the considered markets for the short-run horizons. However, the tail dependence is also found for the long-run horizons between the oil and stock markets, with the exception of the S&P500 index which exhibits average dependence with the oil market. Moreover, we find strong evidence of up and down risk asymmetric spillovers from oil to stock markets and vice versa in the short-and long run horizons. Finally, the market risk spillovers are asymmetric over the time and investment horizons.     

Stock Returns, Dividend Yields, and Taxes

Using an improved measure of a common stock's annualized dividend yield, we document that risk-adjusted NYSE stock returns increase in dividend yield during the period from 1963 to 1994. This relation between return and yield is robust to various specifications of multifactor asset pricing models that incorporate the Fama–French factors. The magnitude of the yield effect is too large to be explained by a "tax penalty" on dividend income and is not explained by previously documented anomalies. Interestingly, the effect is primarily driven by smaller market capitalization stocks and zero-yield stocks.     

THE EFFECT OF MARKET CONDITIONS AND RISK CLASSIFICATIONS ON MARKET MODEL PARAMETERS

This article reports the results of tests used to detect shifts in market model parameters during bull and bear market conditions. The evidence indicates that the parameters exhibit nonstationarity during market advances and market declines for certain predetermined stock groups. Specifically, the parameters of stocks in high-risk and low-risk classifications behave as if they are affected by the alternating forces of bull and bear markets.     

Time varying stock return predictability: Evidence from US sectors

This paper argues that dividend yield stock return predictability is time-varying. We conjecture that such time-variation is linked to the business cycle. Employing monthly data for US sector portfolios we estimate 5-year rolling fixed window predictive regressions. The resulting series of time-varying predictive coefficients is regressed on industrial production growth and a recession dummy. Our results support the view of a negative relationship between predictability and output growth. That is the strength of the predictive relationship between returns and the dividend yield is stronger during contractionary periods, while during expansions the magnitude of the relationship declines.     

Identifying Bull and Bear Markets in Japan

This study investigates the returns and volatility of bull and bear markets as represented by the Tokyo Stock Price Index (TOPIX). Our results show that bull markets are characterized by high returns and low volatility and that the opposite is true for bear markets. Further, this study uncovers a relationship between the duration of bull and bear markets and the point at which the TOPIX has turned from bull to bear and vice versa. Our results indicate that a bull or bear market has a higher probability of continuing as the duration of market’s current trend lengthens. If a bull or bear market trend persists for more than nine months, its probability of continuing approaches 1. Conversely, the transition from a rising to a declining market, and vice versa, is more likely to occur when the previous trend has persisted for less than nine months.     

Are the gains from international portfolio diversification exaggerated? The influence of downside risk in bear markets

The fundamental rationale for international portfolio diversification is that it expands the opportunities for gains from portfolio diversification beyond those that are available through domestic securities. However, if international stock market correlations are higher than normal in bear markets, then international diversification will fail to yield the promised gains just when they are needed most. We evaluate the extent to which observed correlations to monthly returns in bear, calm and bull markets are captured by three popular bivariate distributions: (1) the normal, (2) the restricted GARCH(1,1) of J. P. Morgan’s RiskMetrics, and (3) the Student-t with four degrees of freedom. Observed correlations during calm and bull markets are unexceptional compared to these models. In contrast, observed correlations during bear markets are significantly higher than predicted. Higher-than-normal correlations during extreme market downturns result in monthly returns to equal-weighted portfolios of domestic and international stocks that are, on average, more than two percent lower than those predicted by the normal distribution. If the extent of non-normality during bear markets persists over time, then a US investor allocating assets into foreign markets might want to allocate more assets into foreign markets with near-normal correlation profiles and avoid markets with higher-than-normal bear market co-movements.     

The efficiency of the buy-write strategy: Evidence from Australia

We examine the performance of the buy-write option strategy (BWS) on the Australian Stock Exchange and analyse whether such an investment opportunity violates the efficient market hypothesis on the basis of its risk and returns. This study investigates the relationship between buy-write portfolios returns and past trading volume and other fundamental financial factors including dividend yield, firm size, book to market ratio, earnings per share (EPS), price earnings ratio and value stocks within these portfolios. We also test the profitability of the buy-write strategy during bull and bear markets. Consistent with the literature, it is observed that BWS offers superior risk adjusted returns for low levels of out-of-moneyness and contrary evidence is observed for deeper out-of-money portfolios. Consistent with a preference for options with a maturity of around 3 months in Australia, this research shows that quarterly rebalancing periods offer better returns for the BWS.     

Does institutional ownership increase stock return volatility? Evidence from Vietnam

The literature on institutional ownership and stock return volatility often ignores small emerging countries. However, this issue is more profound, due to the large size of institutional investors and small stock market size, in emerging equity markets. This paper examines the effects of the institutional ownership on the firm-level volatility of stock returns in Vietnam. Our data cover most of non-financial firms listed on the Ho Chi Minh City stock exchange for the period 2006–2012. Employing different analysis techniques for panel data and controlling for possible endogeneity problems, our empirical results suggest that institutional investors stabilize the stock return volatility. Moreover, we document that: i) the stabilizing effect of institutional investor ownership is higher in dividend paying firms, and ii) if firms are paying out more dividends, this stabilizing effect is greater. Our results outline the important role of institutional investors in maintaining the stability in emerging stock markets.     

The effect of personal taxes and dividends on capital asset prices: Theory and empirical evidence

This paper derives an after tax version of the Capital Asset Pricing Model. The model accounts for a progressive tax scheme and for wealth and income related constraints on borrowing. The equilibrium relationship indicates that before-tax expected rates of return are linearly related to systematic risk and to dividend yield. The sample estimates of the variances of observed betas are used to arrive at maximum likelihood estimators of the coefficients. The results indicate that, unlike prior studies, there is a strong positive relationship between dividend yield and expected return for NYSE stocks. Evidence is also presented for a clientele effect.     

Stock volatility predictability in bull and bear markets

The recent literature on stock return predictability suggests that it varies substantially across economic states, being strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state dependent. In particular, in this paper, we use a large data set of high-frequency data on individual stocks and a few popular time-series volatility models to comprehensively examine how volatility forecastability varies across bull and bear states of the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state than when it is in a bull state. In addition, over all but the shortest horizons, the volatility forecast accuracy is higher when the market is in a bear state. This difference increases as the forecast horizon lengthens. Our study concludes that stock volatility predictability is strongest during bad economic times, proxied by bear market states.