Investor sentiment and the stock market’s reaction to monetary policy

This paper shows that monetary policy decisions have a significant effect on investor sentiment. The effect of monetary news on sentiment depends on market conditions (bull versus bear market). We also find that monetary policy actions in bear market periods have a larger effect on stocks that are more sensitive to changes in investor sentiment and credit market conditions. Overall, the results show that investor sentiment plays a significant role in the effect of monetary policy on the stock market.     

Nonlinear mean reversion in stock prices

This paper provides new evidence on the time-series predictability of stock market returns by introducing a test of nonlinear mean reversion. The performance of extreme daily returns is evaluated in terms of their power to predict short- and long-horizon returns on various stock market indices and size portfolios. The paper shows that the speed of mean reversion is significantly higher during the large falls of the market. The parameter estimates indicate a negative and significant relation between the monthly portfolio returns and the extreme daily returns observed over the past one to eight months. Specifically, in a quarter in which the minimum daily return is −2% the expected excess return is 37 basis points higher than in a month in which the minimum return is only −1%. This result holds for the value-weighted and equal-weighted stock market indices and for each of the size decile portfolios. The findings are also robust to different sample periods, different indices, and investment horizons.     

On the persistence and volatility in European,American and Asian stocks bull and bear markets

In this paper we examine the statistical properties of several stock market indices in Europe, the US and Asia by means of determining the degree of dependence in both the level and the volatility of the processes. In the latter case, we use the squared returns as a proxy for the volatility. We also investigate the cyclical pattern observed in the data and in particular, if the degree of dependence changes depending on whether there is a bull or a bear period. We use fractional integration and GARCH specifications. The results indicate that the indices are all nonstationary I(1) processes with the squared returns displaying a degree of long memory behaviour. With respect to the bull and bear periods, we do not observe a systematic pattern in terms of the degree of persistence though for some of the indices (FTSE, Dax, Hang Seng and STI) there is a higher degree of dependence in both the level and the volatility during the bull periods.     

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.     

The Asymmetric Relation Between Initial Margin Requirements and Stock Market Volatility Across Bull and Bear Markets

Higher initial margin requirements are associated with lowersubsequent stock market volatility during normal and bull periods,but show no relationship during bear periods. Higher marginsare also negatively related to the conditional mean of stockreturns, apparently because they reduce systemic risk. We concludethat a prudential rule for setting margins (or other regulatoryrestrictions) is to lower them in sharply declining marketsin order to enhance liquidity and avoid a depyramiding effectin stock prices, but subsequently raise them and keep them atthe higher level in order to prevent a future pyramiding effect.     

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.     

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.     

Bear beta

We test whether bear market risk, time variation in the probability of future bear market states, is priced. We construct an Arrow–Debreu security that pays off in bear market states (AD Bear) from traded Standard & Poor’s (S&P) 500 index options and use its returns to measure bear market risk. We find that bear beta (exposure to bear market risk) has a strong relation with expected stock returns that is robust, persistent, and remains strong among liquid and large stocks. Historical bear beta also predicts future bear market risk exposure. We conclude that bear market risk is priced in the cross section of stock returns.     

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.     

The pricing and performance of leveraged exchange-traded funds

Leveraged ETFs are a recent and very successful financial innovation. They provide daily returns that are in a multiple or a negative multiple of the daily returns on a market benchmark. In this paper, we examine the characteristics, trading statistics, pricing efficiency and tracking errors of a sample of leveraged ETFs. We find that these ETFs are traded mainly by retail traders with very short holding periods. Price deviations (from NAV) are small on average, but large premiums and discounts are prone to occur. More interestingly, the behavior of premiums is different between bull (i.e., those with a positive multiple) and bear ETFs (i.e., those with a negative multiple). Our findings are consistent with the argument that the end-of-day rebalancing of the funds’ exposures increases market volatility at the close of a trading day. As for tracking errors, they are small for holding periods of up to a week, but become increasingly larger for longer horizons.     

Co-movements of sector index returns in the world's major stock markets in bull and bear markets: Portfolio diversification implications

In this paper, principal components analysis and Granger causality tests are used to study the portfolio diversification implications of the co-movements of sector indexes in the US, UK, German, French, and Japanese stock markets in bull and bear markets. We find that, in a bull market, investors can obtain more benefit with global diversification than with domestic diversification even if they invest in the same sector in different countries as opposed to investing in different sectors within the same country. In a bear market, the sectors of different countries tend to be more closely correlated and country diversification opportunities are limited.     

THE TIME‐VARYING EQUITY PREMIUM AND THE S&P 500 IN THE TWENTIETH CENTURY

I present a new hindcast stock market index for the United States over the twentieth century. This is constructed by calibrating a rational asset pricing model that allows for a time‐varying equity premium driven by heteroskedasticity in consumption growth. By incorporating this variation in risk, the mean square error of the generated index series, when compared to the observed levels of the S&P 500, is significantly reduced. The model also explains the broad magnitudes and timings of the major bull and bear markets of the twentieth century, particularly before 1973, and the excess volatility puzzle is largely resolved.     

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.     

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.     

Futures trading volume as a determinant of prices in different momentum phases

Recent studies contend that trading volume has predictive power for ex ante stock prices, particularly small stocks that do not react quickly to macroeconomic information. This study postulates that a significant amount of macro-information that flows on to stock markets is derived from derivative markets. We examine the impact of short-term futures trading volume and prices on cash stock prices using a case study of 15-min data from the Australian stock index futures market which reports actual trading volume. After applying vector error correction modelling (VECM), variance decomposition and impulse functions, we conclude that futures prices provide a short-term information lead to stock prices that dominates trading volume effects. We also observe asymmetric changes in the impact of trading volume between bull and bear price momentum phases and after large trading volume shocks. These results suggest that, in future, studies on trading volume should control for the cross-correlation impact from derivative prices and the differential impact of trading phases.     

Valuation ratios and price deviations from fundamentals

This paper sheds light on US stock price deviations from fundamentals by analyzing the time-series dynamics of post-1870 S&P valuation ratios. It employs a non-linear, two-regime framework that allows for different behavior over phases of the stock market cycle. Persistence in the ratios implies prolonged price deviations from fundamentals stemming from short run continuation fueled by investor sentiment during bull markets. However, the pull from fundamentals ensures that valuation ratios and prices move toward their equilibrium levels in bear markets. Impulse response functions highlight sluggish adjustment and indicate that the effects of positive shocks are more pronounced and long-lasting in bull markets. The main conclusion is that, while market sentiment plays an important transitory role, valuation ratios do mean revert and so prices reflect fundamentals in the long run.     

Insider Trading Activity,Different Market Regimens,and Abnormal Returns

This paper show that corporate insiders earn abnormal returns by adjusting their own firm's stock trading to future market movements. Insider trading activity in bear markets is characterized by decreases in insider sales and increases in purchases, consistent with the view that those markets are followed by improved economic conditions. Conversely, insider sales increase and purchases decrease in bull markets, consistent with the view that inferior market conditions tend to follow those periods.     

不同股市行情下的星期效应研究——基于沪深股市的实证检验

本文基于我国股票市场中一波最长的熊市和牛市行情,运用GARCH模型研究不同行情下的星期效应。研究发现,整个样本区间内星期效应并不显著,但在熊市和牛市子样本中却分别存在明显的星期效应,具体表现：熊市行情下,最高收益率出现在周二,最低收益率出现在周一;牛市行情下,最高收益率出现在周一,最低收益率出现在周四,且结论具有显著性。因此,按行情对股票市场进行星期效应研究,可有效克服总体样本所得结论的模糊性。     

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.     

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。