The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

BUSINESS CYCLES AND STOCK MARKET RETURNS: EVIDENCE USING INDUSTRY-BASED PORTFOLIOS

Much evidence has emerged recently that suggests stock returns are predictable. In representative agent consumption-based asset pricing models, asset returns are related to aggregate output and consumption through changes in the intertemporal marginal rate of substitution. An alternative view is that the amount of variation required in the intertemporal marginal rate of substitution is too large to be rationally explained. We shed further light on this debate by investigating whether the stock returns of certain sectors of the economy can predict future market returns even after controlling for the information contained in the aggregate market index. In the consumption-based models, aggregate output and consumption affect the discount rates of all assets synchronously; no particular sectoral return should have any more predictive ability than the others. We find evidence that the stock returns of five industry-based portfolios have significant information about future market returns that is not in the market index. This stylized empirical result is not consonant with existing models relating output to stock returns.     

Short-term overreaction to specific events: Evidence from an emerging market

This paper investigates the short-term overreaction to specific events and whether stock prices are predictable in the Egyptian stock exchange (EGX). We find evidence of the short-term overreaction in the EGX. Losers (“bad news” portfolios) significantly outperform winners (“good news” portfolios) and investors can earn abnormal return by selling the winners and buying losers. Terrorist attacks have negative and significant abnormal returns for three days post event followed by price reversals on day four post event. Whereas, the tensions in the Middle East region have a negative and significant abnormal returns on event day followed by price reversals on day one post event. Moreover, the formation of a new government has no effect on the average abnormal returns post event in the EGX. The results also show that small firms tend to have greater price reversals compared to large firms. Overall, our results provide evidence of the leakage of information in the EGX.     

The output gap and stock returns: Do cyclical fluctuations predict portfolio returns?

This study examines whether the output gap leads portfolio stock returns. The paper conducts in-sample and out-of-sample forecasting of US stock portfolios formed on the basis of size and value. First, the paper finds cross-sectional portfolios are predictable in-sample by the output gap. Out-of-sample evidence is weaker but still generally supports the finding that the historical average benchmark can be beaten. Secondly and most importantly, we find mixed evidence that the Fama–French factor mimicking portfolios can be forecasted by the output gap. In particular, there is some out-of-sample predictability of the size effect (SMB) suggesting this lags the output gap. However, the output gap, a key business cycle indicator, cannot predict the value effect (HML) either in-sample or out-of-sample. Our results add to the prior literature which finds that the factor mimicking returns are related contemporaneously (Petkova and Zhang, 2005) or lead (Liew and Vassalou, 2000) economic indicators.     

The price adjustment and lead-lag relations between stock returns: microstructure evidence from the Taiwan stock market

This paper investigates the price adjustment and lead-lag relations between returns on five size-based portfolios in the Taiwan stock market. It finds evidence that the price adjustment of small-stock portfolios is not slower than that of large-stock portfolios. Additionally, limited evidence supports a positive leading role of large-stock portfolio returns over small-stock portfolio returns. These two findings are substantially different from the results of previous research on developed markets.     

A Time-Series Model of Stock Returns with a Positive Short-Term Correlation and a Negative Long-Term Correlation

We study portfolio stock return behavior that exhibits both a positive autocorrelation over short horizons and a negative autocorrelation over long horizons. These autocorrelations are more significant in small size portfolios. Among various forms of temporary components in stock prices, an AR(2) component is the simplest model compatible with this pattern of returns, which yields an ARMA(2,2) model of stock returns. We show that the significance of this model is that it requires the presence of feedback trading, which is a form of irrational trades, and the market's slow adjustment to the market fundamentals, which is consistent with recent modelings of stock prices. We find that the variation of the temporary component becomes greater as the firm size gets smaller. This implies that the deviation from the market fundamentals is larger in small size portfolios than in large size portfolios.     

Overreaction after Controlling for Size and Book-to-Market Effects and its Mimicking Portfolio in Japan

In this paper we observe that firm size (SZ) and book-to-market (BM) cannot fully explain stock returns on prior-return- (PR-) based portfolios in the Japanese stock market. The overreaction effect after controlling for the SZ and BM effects is significant and persistent, and accounts for a large part of the zero-investment returns on the loser to the winner. We therefore propose a new mimicking portfolio whose returns mimic the common factor in returns related to overreaction. Our evidence shows that the proposed four-factor model captures common variation in returns on portfolios, based on stocks SZ, BM, and PR, better than the well-known three-factor model does.     

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.     

Style investing,comovement and return predictability

Barberis and Shleifer (2003) argue that style investing generates momentum and reversals in style and individual asset returns, as well as comovement between individual assets and their styles. Consistent with these predictions, in some specifications, past style returns help explain future stock returns after controlling for size, book-to-market and past stock returns. We also use comovement to identify style investing and assess its impact on momentum. High comovement momentum portfolios have significantly higher future returns than low comovement momentum portfolios. Overall, our results suggest that style investing plays a role in the predictability of asset returns.     

State‐Level Business Cycles and Local Return Predictability

This study examines whether local stock returns vary with local business cycles in a predictable manner. We find that U.S. state portfolios earn higher future returns when state‐level unemployment rates are higher and housing collateral ratios are lower. During the 1978 to 2009 period, geography‐based trading strategies earn annualized risk‐adjusted returns of 5%. This abnormal performance reflects time‐varying systematic risks and local‐trading induced mispricing. Consistent with the mispricing explanation, the evidence of predictability is stronger among firms with low visibility and high local ownership. Nonlocal domestic and foreign investors arbitrage away the predictable patterns in local returns in 1 year.     

Non‐linear Predictability of Value and Growth Stocks and Economic Activity

Recent empirical evidence suggests that stock market index returns are predictable from a variety of financial and macroeconomic variables. We extend this research by examining value and growth portfolios constructed by book-to-market ratio, and consider whether such predictability is evident here. Further, we assess whether such predictability is better characterised by a non-linear form and whether such non-linear predictability can be exploited to provide superior forecasts to those obtained from a linear model. General non-linearities are examined using non-parametric techniques, which suggest possible threshold behaviour. This leads to estimation of a smooth-transition threshold model, with the results indicating an improved in-sample performance and marginally superior out-of-sample forecast results.     

Size,value and momentum in stock returns: evidence from India

This paper examines the effects of size, value and momentum on the cross-sectional relation between expected returns and risk in the Indian stock market. We find that the conditional Carhart four-factor model empirically describes the variation of cross-section of return better than the unconditional model. When size, book-to-market and momentum effects are controlled in the conditional model, the positive relation of market beta, book-to-market and momentum with expected returns remains economically and statistically significant. However, this evidence is found to be subject to characteristics of test portfolios. The expected returns are sensitive to changes in predictive macroeconomic variables.     

The Empirical Relationship Between Earnings Information and Stock Returns

Abstract:

The objective of this study was to examine, using a vector autoregressive model, whether the difference in earnings growth rates caused different reaction speeds in stock prices. Monthly returns of stocks listed in the Taiwan stock market from May 2003 to April 2013 were used as empirical data in this study. The analytical results showed that the returns of portfolios with higher earnings growth rates significantly led those portfolios with lower earnings growth rates when size, trading volume, institutional ownership ratio, and revenue factors were controlled, respectively. This paper finds that the earnings growth rate is a significant determinant of the lead-lag patterns observed in monthly stock returns.     

Asymmetric mean-reversion and contrarian profits: ANST-GARCH approach

This paper investigates the time-series evidence of asymmetric reverting patterns in stock returns that is attributable to “contrarian profitability.” Using asymmetric nonlinear smooth-transition (ANST) GARCH(M) models, we find that, for monthly excess returns of US market indexes over the period of 1926:01–1997:12, negative returns on average reverted more quickly, with a greater reverting magnitude, to positive returns than positive returns revert to negative returns. The results are quite consistent when the models are implemented not only for the different sample periods, such as 1926:01–1987:09 and 1947:01–1997:12, but also for portfolios with different characteristics, such as different firm-size portfolios and Fama–French risk-adjusted factor portfolios. We interpret the asymmetrical reversion as evidence of stock market overreaction.     

Momentum and Autocorrelation in Stock Returns

This article studies momentum in stock returns, focusing onthe role of industry, size, and book-to-market (B/M) factors.Size and B/M portfolios exhibit momentum as strong as that inindividual stocks and industries. The size and B/M portfoliosare well diversified, so momentum cannot be attributed to firm-or industry-specific returns. Further, industry, size, and B/Mportfolios are negatively autocorrelated and cross-seriallycorrelated over intermediate horizons. The evidence suggeststhat stocks covary "too strongly" with each other. I argue thatexcess covariance, not underreaction, explains momentum in theportfolios.     

The size effect in the Mexican stock market

This paper tests for a firm size effect in the Mexican stock market using data from January 1987 to December 1992. Our initial tests indicate that average stock returns are positively related to market betas. We also find, however, that average returns are negatively related to firm size. To measure the effects on average return of betas that are unrelated to firm size, we examine portfolios formed on the basis of size and beta We find that beta is priced in addition to firm size for the Mexican stock market, even after carefully separating the effects of beta and size.     

Mean Reversion in the Short Horizon Returns of UK Portfolios

This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976–1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample.     

The financial conglomerate discount: Insights from stock return skewness

Diversified banks (i.e. financial conglomerates) trade often at a discount compared to matched portfolios of specialized stand-alone banks. The existing research explains this evidence primarily with inefficiencies in the cash flow management of banks. This article analyzes the financial conglomerate discount by focusing on the role of expected returns approximated by measures of stock return skewness. Our empirical findings support the hypothesis that diversified banks have less skewed stock returns, i.e. they are more likely to perform badly than non-diversified banks. Due to the lower skewness exposure investors demand higher future returns, thereby lowering corporate value. Although the conglomerate puzzle is observed across industries, the previous literature examines banks separately, as the financial industry is hardly comparable to other sectors. We follow this field of research and show that huge banks quote at a discount as diversification into investment banking activities affects negatively the corporate performance.     

The Return of the Size Anomaly: Evidence from the German Stock Market

This paper examines the size effect in the German stock market and intends to address several unanswered issues on this widely known anomaly. Unlike recent evidence of a reversal of the size anomaly this study documents a conditional relation between size and returns. I also detect strong momentum across size portfolios. The results indicate that the marginal effect of firm size on stock returns is conditional on the firm's past performance. I use an instrumental variable estimation to address Berk's critique of a simultaneity bias in prior studies on the small firm effect and to investigate the economic rationale behind firm size as an explanatory variable for the variation in stock returns. The analysis in this paper indicates that firm size captures firm characteristic components in stock returns and that this regularity cannot be explained by differences in systematic risk.     

The big picture: The industry effect of short interest

We examine whether industry-level short interest predicts industry stock returns and find that the former is negatively associated with the latter. Furthermore, this predictive ability is more pronounced in industries with higher information asymmetry surrounding firms, suggesting that either short sellers' access to private information or their superior information processing skills are important. We also find that this predictive ability is stronger when the short-sale constraint is more binding and when the economic condition is challenging. Overall, our results imply that short sellers' collective activities convey important industry information, leading to predictable and profitable industry portfolios.