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.     

Mean reversion in stock market prices: New evidence based on bull and bear markets

In this paper we test whether mean reversion in stock market prices presents a different behavior in bull and bear markets. We date the US bull and bear periods using Bry and Boschan (1971) algorithm. We examine the order of integration in the S&P 500 stock market index covering a daily period from August 1929 to December 2006 in bull and bear phases. Our results indicate the existence of different episodes of mean reversion, which mainly correspond to bull market periods.     

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.     

Predicting the bear stock market: Macroeconomic variables as leading indicators

This paper investigates whether macroeconomic variables can predict recessions in the stock market, i.e., bear markets. Series such as interest rate spreads, inflation rates, money stocks, aggregate output, unemployment rates, federal funds rates, federal government debt, and nominal exchange rates are evaluated. After using parametric and nonparametric approaches to identify recession periods in the stock market, we consider both in-sample and out-of-sample tests of the variables’ predictive ability. Empirical evidence from monthly data on the Standard & Poor’s S&P 500 price index suggests that among the macroeconomic variables we have evaluated, yield curve spreads and inflation rates are the most useful predictors of recessions in the US stock market, according to both in-sample and out-of-sample forecasting performance. Moreover, comparing the bear market prediction to the stock return predictability has shown that it is easier to predict bear markets using macroeconomic variables.     

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.     

MARKET TIMING OF INTERNATIONAL STOCK MARKETS USING THE YIELD SPREAD

We use probit modeling to forecast bear stock markets in the United States and in eight major foreign stock markets. In general, we find that the U.S. yield spread contains more important market‐timing information than does the home‐country yield spread for profitable market timing. At a 35% probability screen, our simulations show that the U.S. dollar (representative local currency) investor could earn a median compound annual return across eight foreign (non‐U.S.) stock markets of 15.75% (17.67%) by following a market‐timing strategy versus a median buy‐and‐hold return of 13.56% (16.55%).     

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.     

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.     

Monetary policy and stock returns: Financing constraints and asymmetries in bull and bear markets

We examine asymmetries in the impact of monetary policy surprises on stock returns between bull and bear markets in the period 1994 to 2005. We ask how these impacts respond to the relative ability of firms to obtain external finance. We find that the impact of a surprise monetary policy in a bear market is large, negative, and statistically significant, and this holds across size decile portfolios. The impact of a surprise policy action in a bear market for most industries is significantly greater than the impact of surprise monetary policy in a bull market. Controlling for the capacity for external finance, stock returns of firms in bear states respond more than firms in bull states. Capacity for external finance is more important in a bear market, as it partially mitigates the larger impact of monetary policy in a bear market.     

Nonlinear relationship between monetary policy and stock returns: Evidence from the U.S.

This paper studies the relationship between monetary policy and stock market return in the U.S. using nonlinear econometric models. It first employs a univariate Markov-switching model on each of the three stock indices and three monetary policy variables, displaying significant regime-switching patterns and common movements. This paper then uses a Markov-switching dynamic bi-factor model to simultaneously extract two latent common factors from stock indices and monetary policy variables to represent monetary policy changes and stock market movements separately. The smoothed probabilities of regimes demonstrate that expansionary monetary policy regimes follow economic recessions, but bear stock markets usually occur before economic recessions. The maximum likelihood estimation results show that expansionary monetary policy such as a decrease in the federal funds rate raises stock returns, but stock returns don't directly influence monetary policy decision.     

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.     

Evaluating the Forecast Accuracy of Emerging Market Stock Returns

This paper analyzes the forecast performance of emerging market stock returns using standard autoregressive moving average (ARMA) and more elaborated autoregressive conditional heteroskedasticity (ARCH) models. Our results indicate that the ARMA and ARCH specifications generally outperform random walk models. Models that allow for asymmetric shocks to volatility are better for in-sample estimation (threshold autoregressive conditional heteroskedasticity for daily returns and exponential generalized autoregressive conditional heteroskedasticity for longer periods), and ARMA models are better for out-of-sample forecasts. The results are valid using both U. S. dollar and domestic currencies. Overall, the forecast errors of each Latin American market can be explained by the forecasts of other Latin American markets and Asian markets. The forecast errors of each Asian market can be explained by the forecasts of other Asian markets, but not by Latin American markets. Our predictability results are economically significant and may be useful for portfolio managers to enter or leave the market.     

Share issuance and cross-sectional returns: International evidence

Share issuance predicts cross-sectional returns in a non-U.S. sample of stocks from 41 different countries. Issuance predictability has greater statistical significance than either size or momentum, and is similar to book-to-market. As in the U.S., the international issuance effect is robust across both small and large firms. Unlike the U.S., the effect is driven more by low returns after share creation rather than positive returns following share repurchases. Issuance return predictability is stronger in countries with greater issuance activity, greater stock market development, and stronger investor protection. The results suggest that the share issuance effect is related to the ease with which firms can issue and repurchase their shares.     

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.     

DIFFERENCES OF OPINION,OVERCONFIDENCE, AND THE HIGH‐VOLUME PREMIUM

We argue that both differences of opinion and overconfidence lead to high‐volume shocks. However, a high‐volume shock induced mainly by differences of opinion (overconfidence) will lead to superior (inferior) stock returns. Empirically, Asian financial markets, in contrast to U.S. markets, reveal weaker and inconsistent high‐volume premiums. The inconsistency may be attributable to investor's overconfidence. Additional evidence based on U.S. data supports this view, as a high‐volume shock accompanied by increased institutional ownership yields substantially higher high‐volume premiums than otherwise, and high‐volume premiums generally are much stronger in down‐market states than up‐market states.     

Lack of consumer confidence and stock returns

This paper investigates the link between the lack of consumer confidence and stock returns during market fluctuations. Using a Markov-switching framework, we first focus on whether the shock to consumer confidence has asymmetric effects on stock returns. We also examine whether the decreased confidence pushes the stock market into bear territory. Empirical evidence using monthly returns on Standard & Poor's S&P 500 price index suggests that market pessimism has larger impacts on stock returns during bear markets. Moreover, the lack of consumer confidence leads to a higher probability of switching to a bear market regime.     

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.     

中国货币政策对股票市场影响的再考察

本文运用DAG方法、VAR模型和马尔科夫转换模型考察了货币政策对股市价格水平的影响,结果表明中短期内货币政策对股票市场价格水平存在影响显著,并表现出较强的非对称效应。股市低迷期的紧缩性货币政策会进一步降低股市收益率,减小股市从熊市转入牛市的概率;相反,股市繁荣期的紧缩性货币政策将增加股市从牛市转入熊市的概率。     

Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data

This paper provides strong evidence of time-varying return predictability of the Dow Jones Industrial Average index from 1900 to 2009. Return predictability is found to be driven by changing market conditions, consistent with the implication of the adaptive markets hypothesis. During market crashes, no statistically significant return predictability is observed, but return predictability is associated with a high degree of uncertainty. In times of economic or political crises, stock returns have been highly predictable with a moderate degree of uncertainty in predictability. We find that return predictability has been smaller during economic bubbles than in normal times. We also find evidence that return predictability is associated with stock market volatility and economic fundamentals.