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.     

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.     

PORTFOLIO MANAGEMENT: THE ROLE OF CALIBRATION,SHARPNESS, AND UNCERTAINTY

I evaluate the out‐of‐sample predictability of several major indicators for bull and bear markets in monthly S&P 500 series with three quadratic probability score components: calibration, sharpness, and uncertainty. I find that uncertainty limits the trend characterization and thus provides a new perspective from which to identify bull and bear markets. I also find that sharpness plays a key role in determining portfolio returns. Trading strategies that capitalize on sharpness generate higher Sharpe ratios and portfolio returns. The Aruoba–Diebold–Scotti business conditions index is the most profitable indicator for both medium‐ and long‐term trends.     

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.     

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.     

Higher moments and beta asymmetry: evidence from Australia

We examine whether systematic higher moments capture beta asymmetry in an asset pricing model whereby the conditional beta of a risky asset increases (decreases) during a bear (bull) market state. We first provide a simple conceptual outline from the microeconomic literature to show that beta asymmetry is driven by time‐varying higher‐order risk preferences (prudence and temperance) across different market states. We then empirically relate these higher‐order risk preferences to systematic skewness and systematic kurtosis. We find that beta asymmetry in Australian stock returns cannot be explained by Carhart (1997) 4‐factor model but is subsumed by systematic higher moments.     

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.     

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.     

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.     

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。     

Does beta react to market conditions? Estimates of ‘bull’ and ‘bear’ betas using a nonlinear market model with an endogenous threshold parameter

The authors use a logistic smooth transition market (LSTM) model to investigate whether ‘bull’ and ‘bear’ market betas for Australian industry portfolios returns differ. The LSTM model allows the data to determine a threshold parameter that differentiates between ‘bull’ and ‘bear’ states, and it also allows for smooth transition between these two states. Their results indicate that ‘bull’ and ‘bear’ betas are significantly different for most industries, and that up-market risk is not always lower than down-market risk. LSTM models indicate that the transition between ‘bull’ and ‘bear’ states is abrupt, supporting a dual-beta market modelling framework.     

A top-down approach to identifying bull and bear market states

Bull and bear markets receive considerable media and academic attention. It is widely believed that such states are important determinants of wider market dynamics, yet no agreed definition exists. This paper investigates frameworks for ex post classification of asset prices in two-state (bull and bear) markets. An emphasis is placed on identifying state transition points that might achieve consensus. A number of potential difficulties with existing methodologies are highlighted. A principle-based approach is adopted from which a new, flexible, hierarchical methodology is proposed that addresses these issues and permits varying degrees of resolution allowing secondary trends such as bear rallies to be incorporated. The methodology is shown to be optimal under one measure of performance.     

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。     

Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios

A recent literature has shown that REIT returns contain strong evidence of bull and bear dynamic regimes that may be best captured using nonlinear econometric models of the Markov switching type. In fact, REIT returns would display regime shifts that are more abrupt and persistent than in the case of other asset classes. In this paper we ask whether and how simple linear predictability models of the vector autoregressive (VAR) type may be extended to capture the bull and bear patterns typical of many asset classes, including REITs. We find that nonlinearities are so deep that it is impossibile for a large family of VAR models to either produce similar portfolio weights or to yield realized, ex-post out-of-sample long-horizon portfolio performances that may compete with those typical of bull and bear models. A typical investor with intermediate risk aversion and a 5-year horizon ought to be ready to pay an annual fee of up to 5.7 % to have access to forecasts of REIT returns that take their bull and bear dynamics into account instead of simpler, linear forecast.     

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.     

Partial moment volatility indices

Forward‐looking partial moment volatility indices are developed using state‐pricing, called the bear index (BEX) and bull index (BUX). Using S&P 500 index (SPX) option prices, we find that BEX and BUX provide superior forecasts for the lower and upper partial moments of future market realised volatility, respectively. We examine the relation between SPX returns and changes in BEX and BUX at the daily level. Results are consistent with the volatility feedback hypothesis. Further, we show that BEX may be more suitable as the ‘investor fear gauge’ than VIX.     

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.     

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.     

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.     

Cross‐region and cross‐sector asset allocation with regimes

Cross‐region and cross‐sector asset allocation decisions are one of the most fundamental issues in international equity portfolio management. Equity returns exhibit higher volatilities and correlations, and lower expected returns, in bear markets compared to bull markets. However, static mean–variance analysis fails to capture this salient feature of equity returns. We accommodate the nonlinearity of returns using a regime switching model across both regions and sectors. The regime‐dependent asset allocation potentially adds value to the traditional static mean–variance allocation. In addition, optimal allocation across sectors provide greater benefits compared to international diversification, which is characterized by higher returns, lower risks, lower correlations with the world market and a higher Sharpe ratio.