Individual stock sentiment beta and stock returns

Using the data in Chinese stock market, we measure the individual stock sentiment beta, which is defined as the sensitivity of individual stock returns to the individual stock sentiment changes. We demonstrate that stocks in the highest individual stock sentiment beta portfolio have significantly higher excess returns, CAPM alpha, Fama-French three-factor alpha and Fama-French five-factor alpha. Besides, we find that the high individual stock sentiment beta stocks are smaller, younger, more volatile stocks with higher price and higher market beta. After controlling for firm characteristic, the returns of High-Low individual stock sentiment beta portfolios are still significantly positive. Moreover, we show the effect of the individual stock sentiment beta on stock returns is positive and significant in different stock markets, in different sample periods, and in bull and bear market. Besides, the results of the Bayes-Stein individual stock sentiment beta are still stable.     

Firm efficiency and stock returns

In this paper, we investigate the role of firm efficiency in asset pricing using a sample of US publicly listed companies for the period 1988–2007. We employ non-parametric data envelopment analysis (DEA) on various input/output combinations, focusing on sales and market value as output measures in the construction of the frontier technologies. Using these performance measures, we examine whether efficient firms perform differently from inefficient firms following standard financial analysis procedures. First, we employ performance attribution regressions, by forming portfolios based on efficiency scores and tracking the performance of the various portfolios over time. Second, we perform cross-sectional/panel regressions to determine whether firm efficiency indeed has explanatory power for the cross-section of stock returns. Our results suggest that firm efficiency plays an important role in asset pricing and that efficient firms significantly outperform inefficient firms even after controlling for known risk factors.     

A non-linear filtering approach to stochastic volatility models with an application to daily stock returns

This paper develops a new model for the analysis of stochastic volatility (SV) models. Since volatility is a latent variable in SV models, it is difficult to evaluate the exact likelihood. In this paper, a non-linear filter which yields the exact likelihood of SV models is employed. Solving a series of integrals in this filter by piecewise linear approximations with randomly chosen nodes produces the likelihood, which is maximized to obtain estimates of the SV parameters. A smoothing algorithm for volatility estimation is also constructed. Monte Carlo experiments show that the method performs well with respect to both parameter estimates and volatility estimates. We illustrate our model by analysing daily stock returns on the Tokyo Stock Exchange. Since the method can be applied to more general models, the SV model is extended so that several characteristics of daily stock returns are allowed, and this more general model is also estimated. Copyright © 1999 John Wiley & Sons, Ltd.     

Dynamic relationships between industry returns and stock market returns

Different from prior studies which concentrate on the unidirectional impact of industry leading, this study examines the bi-directional dynamical causal relation between industry returns and stock market returns by considering multiple structural breaks for ten major eastern and southern Asia countries. Our results show that finance and consumer service industry returns have significant power in explaining the movements of market returns. Further, we apply logit regressions to explore the determinants of the leading hypotheses and find exchange rate and interest rate are important in explaining the industry–market nexus. In a developed market the industry and the market have feedback relations, but in a highly controlled economy the influence from the stock market dominates.     

The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns

In this article, we construct mixed-frequency individual stock sentiment using MIDAS model. We first investigate the influence power of mixed-frequency individual stock sentiment on excess returns. The results indicate that the higher the frequency of individual stock sentiment is, the better it explains the variation of excess returns, that mixed-frequency individual stock sentiment, especially mixed high-frequency sentiment, exerts greater influence on excess returns than the same frequency one and that the mixed-frequency sentiment has a stronger explanatory power to the variation of excess returns than size factor, book-to-market factor, profitability factor and investment factor do. Then, we study the predictive content of mixed-frequency individual stock sentiment. The results show that the higher the frequency of individual stock sentiment is, the better the forecast performs. Moreover, by comparing the corresponding statistics in influence and predictive power models, we find that the influence power of mixed-frequency individual stock sentiment is more significant than its predictive power.     

Overnight stock returns and realized volatility

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has yet emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introduced formal testing procedure, we find that for the S&P 500 index, a realized volatility estimator that optimally incorporates overnight information is more accurate in-sample. In contrast, estimators that do not incorporate overnight information are more accurate for individual stocks. We also show that accounting for overnight returns may affect the conclusions drawn in an out-of-sample horserace of forecasting models. Finally, there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate in-sample RV estimators are considered.     

Simultaneous dependence between firm-level stock returns

We show that use of ordinary least-squares to explore relationships involving firm-level stock returns as the dependent variable in the face of structured dependence between individual firms leads to an endogeneity problem. This in turn leads to biased and inconsistent least-squares estimates. A maximum likelihood estimation procedure that will produce consistent estimates in these situations is illustrated. This is done using methods that have been developed to deal with spatial dependence between regional data observations, which can be applied to situations involving firm-level observations that exhibit a structure of dependence. In addition, we show how to correctly interpret maximum likelihood parameter estimates from these models in the context of firm-level dependence, and provide a Monte Carlo as well as applied illustration of the magnitude of bias that can arise.     

The potential effect of US baby-boom retirees on stock returns

Empirical studies demonstrated that US baby boomers consumption and savings patterns have affected economic aggregates over the past decades, among them equity returns. Boomers’ retirement is expected to mitigate the demand for equities until 2050, but its impact varies with the specific population age structure along decades. This paper employs a dynamic asset pricing model with optimum consumption and portfolio rules to estimate aging effects on S&P500 returns between 1950 and 2050. Calibration for demographic and economic data between 1950 and 2005 yields model estimates that significantly explain the moving average of S&P500 returns. Further, taking into account the present value of expected demographic effects until 2050 suggests that the S&P500 was fairly priced at the heart of the financial crisis, on April 2009, but overpriced thereafter.     

Continuous‐time models,realized volatilities,and testable distributional implications for daily stock returns

We provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous‐time jump diffusion models traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures and non‐parametric jump detection statistics constructed from high‐frequency intra‐day data. A sequence of simple‐to‐implement moment‐based tests involving various transformations of the daily returns speak directly to the importance of different distributional features, and may serve as useful diagnostic tools in the specification of empirically more realistic continuous‐time asset pricing models. On applying the tests to the 30 individual stocks in the Dow Jones Industrial Average index, we find that it is important to allow for both time‐varying diffusive volatility, jumps, and leverage effects to satisfactorily describe the daily stock price dynamics. Copyright © 2009 John Wiley & Sons, Ltd.     

Overnight stock returns,intraday returns,and firm-specific investor sentiment

In this study, we propose a new index for measuring firm-specific investor sentiment using overnight and intraday stock returns. We use actual equity data to construct the firm-level investor sentiment index and find that the new index has characteristics expected of a sentiment measure. In addition, we propose a novel sentiment-weighted trading strategy and apply it to momentum and short-term reversal strategies. We find that the sentiment-weighted trading strategy generates better performance in momentum and short-term reversal strategies. The sentiment-weighted trading strategy’s superior performance is evidence that our firm-level investor sentiment index possesses predictive powers with regard to future returns.     

The impact of implementing the airline deregulation act on stock returns

Between 1978 and 1985, the airline industry underwent a period of phased-in deregulation. Airline deregulation resulted from passage of the Airline Deregulation Act on October 24, 1978. This paper investigates the impact of two main provisions of this Act on the security returns in the airline industry. Using event study methodology, this study examines the impact of (1) airlines becoming responsible for determining their domestic routes and schedules (December 31, 1981) and (2) airlines being allowed to set domestic fares and engage in price competition (January 1, 1983) on the returns of a sample of airline common stocks. The study finds a positive market response around the phase-in provision permitting airlines to assume responsibility for determining their domestic routes and schedules, but a negative response around the phase-in provision allowing airlines to set domestic fares and engage in price competition.     

US elections and monthly stock market returns

Using monthly market returns over a period of 104 years, we investigate possible relationships between stock market performance and various occurrences in American elections. Unlike most prior studies, we find little relationship between the two. In the relatively few cases where we do find statistically significant relationships, the degree of explanatory power is quite small. Specifically, market returns do not appear to vary based on partisan control of the government, a result that is robust to the inclusion or exclusion of macroeconomic control variables. Further, the often-discussed “second-half” effect, which predicts higher returns during the second half of a given presidential term, turns out to be both weaker and less straightforward than is commonly believed. Overall, neither election results nor the election cycle appears to offer much help in predicting stock market returns.      

Excess volatility and the cross-section of stock returns

We document a reliable positive relation between excess volatility and the cross-section of stock returns over the sample period of 1963 to 2010. Significantly positive differentials have been found between the two decile portfolios with the largest and the least excess volatility, under all the situations we have examined. Size, value, and momentum effects cannot explain our empirical results. Likewise they cannot be explained by liquidity, bid-ask bounce, and risk-aversion-related inventory effects.     

Earnings announcements and the variability of stock returns

This article is concerned with the dissemination process of firm-specific annual earnings information in the Norwegian capital market. We find a significant reduction in stock price volatility in the post-announcement period relative to the pre-announcement period for companies traded on the Oslo Stock Exchange in the period 1990–1995. Potential explanations for this phenomenon are tested by relating the observed return volatility to changes in the volatility of the underlying business, the speed at which information is incorporated into stock prices, and the amount of noise in the price process. The empirical analyses reveal no significant changes in either the underlying business variance or the price adjustment coefficients. However, we find a significant decline in the noise term for the largest companies after the earnings release date, supporting the hypothesis that earnings announcements reduce informational asymmetries among investors.     

Individual stock crowded trades,individual stock investor sentiment and excess returns

Recent behavioral asset pricing models and the popular press suggest that investors may follow similar strategies resulting in crowded equity positions to push prices further away from fundamentals. This paper develops a new approach to measure individual stock crowded trades, and further investigates the joint effects of individual stock crowded trades and individual stock investor sentiment on excess returns. Specifically, our results show that the combined effect of individual stock crowded trades and individual stock investor sentiment on excess returns is positive and significant, which reveals the importance of “anomaly factors” in asset pricing. Furthermore, our results suggest that increasing individual stock buyer-initiated crowded trades will increase excess returns simultaneously; however, increasing individual stock seller-initiated crowded trades will decrease excess returns simultaneously. Collectively, our results highlight the importance of individual stock crowded trades and individual stock investor sentiment on the formation of stock prices.     

The effect of interconnectivity on stock returns during the Global Financial Crisis

This paper examines the role of interconnectivity in global stock markets during the Global Financial Crisis (GFC) using a comprehensive dataset of 8,827 firms traded in developed and emerging markets. Our contribution includes two key findings. We first use a difference-in-differences approach to show that stocks in countries with higher trade openness ex-ante to the GFC experienced lower average and annual cumulative returns of 9.08 p.p. and 36.32 p.p., respectively, compared to stocks traded in less exposed countries, one year after the crisis outbreak. Second, we employ complex network theory to analyze the role of network interconnectedness in our baseline results. To construct the network of interdependence between stock returns, we utilize a regularized Vector Autoregression Model, which enables us to overcome the limitations of commonly used correlation networks. Our findings suggest that while a firm’s high connectivity before the crisis can alleviate adverse shock effects resulting from export dependence, this effect may be weakened if the firm’s performance is closely linked to central firms.     

Pricing the hedging factor in the cross-section of stock returns

We investigate the role of investors’ net hedging strategy (factor) in predicting stock returns and pricing the cross-section of individual stocks and equity portfolios. We estimate stock exposure to changes in the hedging factor and show that the hedging premium is driven by outperformance of stocks with large positive net hedging betas, which explains their higher average returns. We find the positive hedging premium indicates risk-averse investors demand extra compensation to hold stocks with higher equity risk premiums, and they are themselves willing to pay higher prices for stocks with positive hedging betas.     

The yearend effect in stock returns over business cycles: A technical note

This paper provides evidence in support of the claim that the well-knownJanuary effect is influenced by the stage of the business cycle. Using monthly data for the S&P Composite Index for the period from November 1948 through December 1988 and the standard methodology for seasonal anomalies, the authors show that theJanuary effect is present during the entire period examined as well as in the expansionary phases of that period. However, its existence was not detected during the contractionary phases of that period.     

Ownership structure and returns to defensive stock repurchase

This study investigates the wealth effect of defensive common stock repurchase (CSR) on outside shareholders. It is found that the impact varies with the type of corporate control activity that precipitates the repurchase. Outside shareholders suffer wealth losses when a CSR announcement follows an unsolicited bid for the firm. The impact of the CSR announcement following a partial acquisition is negative but not as strong. However, outside shareholders benefit from CSR following antitakeover charter amendments. This study also documents a nonlinear relationship between managerial equity ownership and changes in the value of the firm at the announcement of a defensive CSR.     

Abnormal research and development investments and stock returns

We investigate the relation between abnormal research and development (R&D) investments change and expected stock returns. We provide evidence that firms that abnormally increase their R&D investments (RDI) earn higher returns in comparison to the market portfolio. Specifically, our findings document an economically significant annual positive abnormal RDI returns that ranges from 3.2% to 11.5%. These findings are robust to well-established risk factors in the literature and suggest that the abnormal increases in RDI impacts stock returns.