Excess Stock Returns Research Articles

Firm Performance and Managing Downside Business Risk

This research focuses on the ultimate impact of hedging effectiveness on firm performance. Successful risk management in firms should lead to a lesser occurrence of lower business outcomes than higher outcomes (e.g., positive skewness in quarterly earnings per share). We empirically estimate the hedging profile of 5,586 non-financial firms (spanning 25 industries) from their quarterly earnings per shares (EPS) and relate it to Tobin’s Q, excess stock returns, ROE and ROA. The estimation controls for important firm characteristics (e.g., median EPS, firm size, leverage, market risk, earnings management, firm-industry fixed effects) and endogeneity. The resulting coefficient for EPS skewness – after controlling for median EPS and other firm characteristics – identifies the impact of the firm's hedging profile on firm performance. We find a strong positive relation between firm performance and the hedging profile as Tobin’s Q, ROE and ROA rise by 0.035, 2.6% and 1.4%, respectively, when EPS skewness rises by one (14% of firms). Study results provide further support for shareholder maximization and signaling theories of hedging.

Economic policy uncertainty, risk and stock returns: Evidence from G7 stock markets

• This paper examines uncertainties and risks on excess stock returns G7 markets using monthly observations. • The estimated results find evidence supporting positive risk-return relation not only for risk as measured by conditional volatility but also for downside risk. • The stock returns are negatively correlated with economic policy uncertainty innovation (ΔEPU) in both local and global sources. • In addition to the negative effect of ΔEPU on excess stock returns, evidence also demonstrates that lagged ΔEPU positively contributes to a rise in stock return volatility. • The conventional test of risk-return relation without controlling ΔEPU is subject to a specification bias.

Asset price effects of peer benchmarking: Evidence from a natural experiment

We estimate the effects of peer benchmarking by institutional investors on asset prices. In order to isolate the trades driven by peer benchmarking, we use a natural experiment involving a change in a government-imposed under-performance penalty applicable to Colombian pension funds. We find that peer effects generate excess stock return volatility, with stocks exhibiting short-term abnormal returns followed by returns reversal in the subsequent quarter. Additionally, peer benchmarking produces an excess in comovement across stock returns beyond the correlation implied by fundamentals.

IMPACT OF CREDIT RATINGS ON STOCK RETURNS

This study investigates whether a change in credit ratings lead to a change in dailyexcess stock returns. The sample includes daily stock price data for US firms listedon the Standard & Poor’s 500 from January 2006 to December 2015. Firms’ excessstock returns are compared with the market in a 14-day window around credit ratingdowngrades and upgrades. Our results are asymmetric, that is, there is a significantreaction to credit ratings downgrades but not to upgrades. In addition, we report weakevidence of upgrades in credit ratings since the 2008 global credit crisis leading tosignificant changes in security prices.

TESTING THE EFFECTIVENESS OF THE CARHART MODEL FOUR FACTORS ON EXCESS RETURNS IN INDONESIA

One crucial information for investor in making their investment decision is to estimate asset-pricing level. The basic principle would be high risk, high return. This research is using Carhart model (1997): market return, size, book to market, and momentum. The goal of this research is to test and analyze the influence of Carhart Four Factor Model toward Indonesian stock' excess return. Dependent variable used in this research is stock' excess return, while independent variable used are Carhart four factor model. Population used is all non-financial firms listed in Bursa Efek Indonesia from year 2010 to 2012. Total samples are 150 firms. To test the model in this research, we firstly create ten portfolio groups by combining size-book to market and size-momentum. We use multiple regression analysis by using 10 regression analysis model based on previously built 10 portfolio combinations. Statistical test on variable excess market return toward stock return on 10 portfolio shows that there exist positive significant relationship to all models. SMB is significantly impacting portfolio return to 5 models. HML is significantly impacting portfolio return to 6 models. UMD impacting positively significant toward portfolio return on 2 models. Keywords: stock excess return, Carhart four factor model, market return, size, book to market, momentum.

Flight-to-liquidity: Evidence from China's stock market

In an order-driven and strictly regulated stock market, illiquidity risks' effects on asset pricing should be highlighted, particularly in such extreme market conditions as those in China. This paper utilizes panel data from China's stock market in an attempt to answer whether the illiquidity risk in various dimensions—including price impacts, the transaction speed, trading volume, transaction costs, and asymmetric information—can explain stock returns. We find that almost all dimensions of stock illiquidity are positively associated with excess stock returns. More importantly, smaller, less-liquid stocks suffer more liquidity costs, providing a strong evidence for “flight-to-liquidity.” Additionally, the transaction costs and asymmetric information, denoted by bid-ask spreads, robustly account for these illiquidity effects on stock pricing and differ from the findings in the U.S. market. We also find that the “flight-to-liquidity” can partially explain the idiosyncratic volatility puzzle, investors' gambling, and herding psychologies. This study provides substantial policy implications in regulation and portfolio management for emerging markets.

Individual Stock Cash Inflow–Outflow Imbalance, Individual Stock Investor Sentiment and Excess Returns

ABSTRACTBehavioral finance research presents evidence of the importance of “anomaly factors” in the stock market. In this article, we develop an individual stock cash inflow–outflow imbalance index based on individual stock cash inflow and cash outflow and further examine the combined effects of individual stock cash inflow–outflow imbalance and individual stock investor sentiment on excess returns. Our results show that the combined effect of individual stock cash inflow–outflow imbalance and individual stock investor sentiment on excess returns is stronger positive relation. Furthermore, we find that increasing individual stock cash inflow will increase excess returns; on the contrary, increasing individual stock cash outflow will decrease excess returns. Overall, our results highlight the importance of individual stock cash inflow, cash out flow, and cash inflow–outflow imbalance on asset pricing.

Standing at Attention: The Impact of FOMC Press Conferences on Asset Prices

A press conference (PC) organized by the Federal Open Market Committee (FOMC) followed half of the scheduled announcements from 2011 to 2018. We document that excess stock returns are strongly and positively related to their betas on announcement days with a PC. In addition, the cross-sectional dispersion in betas declines substantially on PC days when measured using both daily and intraday return data. These effects are absent on announcement days without a PC. Last, we find that stock-bond correlations are positive (negative) on PC (all other) days and that their variations are related to uncertainty and yield curve information. We discuss implications and possible explanations for our findings.

An efficient sequential learning algorithm in regime-switching environments

Abstract We provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. “Combined Parameter and State Estimation in Simulation-Based Filtering.” In Sequential Monte Carlo Methods in Practice, 197–223. Springer. by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return.

An Efficient Sequential Learning Algorithm in Regime-Switching Environments

We provide a noble approach of estimating a regime-switching nonlinear and non-Gaussian state space model based on a particle learning scheme. Particularly, we extend the particle learning method in Liu and West (2001) by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For an empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of the U.S. excess stock return.

Predicting stock market movements with a time-varying consumption-aggregate wealth ratio

We develop a time-varying measure of cay (cayTVP) using time-varying cointegration, and then compare the predictive ability of cayTVP with cay and a Markov-switching cay (cayMS) for excess stock returns and volatility in the US over the period 1952:Q2-2015:Q3, using a k-th order nonparametric causality-in-quantiles test. We find that time-varying cointegration exists between consumption, asset wealth, and labor income. In addition, while there is no evidence of predictability of volatility of excess returns from cay, cayMS, or cayTVP, they tend to act as strong predictors of stock returns, with cayTVP being important during the bearish phases of the equity market.

Liquidity and Stock Returns: New Evidence From Johannesburg Stock Exchange

After the 2007/9 financial crisis, liquidity risk became the most dreaded financial risk of all times. However, our modern finance theories are modeled on the premises that markets are frictionless and hence liquidity plays no role, yet there is a plethora of literature that attest to the fact that liquidity is a cost that needs to be priced. This confirms the fact that markets are indeed full of friction and therefore, liquidity needs to be modeled accordingly. In this paper, a time-series regression approach is adopted to investigate the relationship between stock illiquidity and stock excess return on the JSE. The basis of the methodology followed in this study is that of Fama and MacBeth's (1973) cross-sectional multiple regression analysis. Specifically, Fama and French three-factor model plus liquidity is employed to test empirically the relationship between stock excess returns and liquidity together with other known, important time-series determinants of stock returns, such as beta, size, and book-to-market ratio. Liquidity is then added to the three factor model as the fourth factor thus, in addition to the excess return on the market. The results from this study show that liquidity is an important factor in pricing returns on the JSE as the stock excess returns are positively related to illiquidity and the relationship is significant. More specifically, the study revealed that the expected excess returns are positively and significantly related to stock systematic risk as measured by beta. This was the case across all the portfolios that were investigated. The three factor model shows a negative relationship though not significant. The there-factor model plus liquidity showed a positive relationship but not significant. Given the exhibited importance of liquidity in determining stock return, stock liquidity should be incorporated in dynamic stochastic general equilibrium models since markets in reality are not frictionless. Given that highly illiquid stocks are associated with higher expected returns as compared to liquid stocks, the conversional finance theories should be revised to reflect this new insight as this is a confirmation that markets have friction, thus liquidity plays an important role in the modeling of required return.

Foreign institutional investors and stock return comovement

We investigate whether foreign institutional investors facilitate firm-specific information flow in the global market. Specifically, using annual institutional ownership data from firms across 40 countries, we find that foreign institutional ownership is negatively associated with excess stock return comovement. Our results are more pronounced when foreign institutional investors originate from common-law countries and hold a large equity stake in invested firms; and when the invested firms are located in civil-law countries. Overall, the evidence suggests that foreign institutional investors from countries with strong investor protection play an important informational role in mitigating excess stock return comovement around the world.

Can investors attention on oil markets predict stock returns?

This paper sets out to explore the predictability of the U.S. equity risk premium directly based on investor attention to oil. We find that the predictive power of oil attention exhibits statistical and economic significance within different models in both in-sample and out-of-sample tests. Meanwhile, oil attention reveals considerable and robust economic value for asset allocation in the sense of positive utility gains. Furthermore, supportive evidence that oil attention is closely linked to stock market volatility endues it with a macroeconomic meaning, serving as an explanation for its predictive power. Overall, investor attention to oil does have a direct predictive power to forecast the U.S. stock excess returns.

Empirical analysis of intertemporal relations between downside risks and expected returns—Evidence from Asian markets

This paper tests the risk-return relations for Asian stock markets by employing conditional volatility, local downside risk, regional downside risk, and world/U.S. downside risk. We find positive and significant intertemporal relations between excess stock returns and various risks. The evidence supports the risk-return tradeoff not only from local risk but also from external risk. The model is robust as it pertains to the risk of small variations as well as big shocks. The evidence supports positive risk-return relations across 10 Asian markets after controlling for the lagged dividend yield, higher moments of stock returns, and exchange rate variations.

Can economic policy uncertainty predict stock returns? Global evidence

This paper, using data for 16 countries, tests whether economic policy uncertainty (EPU) predicts stock excess returns. First, we show that the ability of EPU to forecast stock returns depends not only on the country used, but also on the sectors examined. This indicates that forecasting of returns is country-dependent (sector-dependent), suggesting that EPU is relatively more important for some countries (sectors) than others. Second, we test whether the predictability of EPU is from either or both the cash flow and discount rate channels. Our results support the discount rate channel over the cash flow channel. Third, we use positive and negative EPU shocks to predict stock excess returns and find evidence of asymmetric predictability. Finally, we consider a mean–variance investor and show that such investor has positive utility by following forecasts generated from the EPU-based model. Our results are consistent with a number of robustness tests.

Practical Applications of Regime Shifts in Excess Stock Return Predictability: An Out-of-Sample Portfolio Analysis

<h3>Practical Applications Summary</h3> In <b>Regime Shifts in Excess Stock Return Predictability: <i>An Out-of-Sample Portfolio Analysis</i></b>, published in the Winter 2018 issue of <b><i>The Journal of Portfolio Management</i></b>, <b>Giulia Dal Pra, Massimo Guidolin, Manuela Pedio</b>, and <b>Fabiola Vasile</b> analyze whether one can outperform the stock market using asset allocation strategies based on certain financial ratios as signals. They construct linear regressions relating excess returns to selected ratios. They explore both ordinary linear regressions and ones that embody a Markov-switching (regime switching) feature. They assess performance of the strategies over several investment time horizons and include the effect of transaction costs. The authors also consider the efficacy of their strategies in an economic framework that accounts for an investor’s degree of risk aversion. <b>TOPICS:</b>Analysis of individual factors/risk premia, in markets, portfolio theory

Technology-investing countries and stock return predictability

For 77 technology-investing countries we test whether their stock market returns are predictable. We find that exchange rate returns and U.S. stock excess returns predict stock market returns for most countries in our sample, while crude oil and inflation predict returns of less than 40% of countries. While in out-of-sample tests the evidence of predictability declines, U.S. returns still beat the constant returns model for three-quarters of countries in our sample. A portfolio of all 77 countries offers a mean-variance investor annualized profits of between 5.7% and 8.0%, and profits are maximized when return forecasts are based on U.S. returns.

Size, value, profitability, and investment: Evidence from emerging markets

In this study, we investigate how the Fama and French three-, four-, and five-factor models perform in emerging markets. We find that the four- and five-factor models perform better than the three-factor model in most of our tests. We note that the value factor seems to be somewhat redundant in the presence of profitability and investment factors. We find clear evidence of size effects in average stock excess returns, little evidence of value and profitability effects, and some investment effects. Finally, the local factors perform better than the US and global factors do, showing evidence of emerging market segmentation.

Predicting failure risk using financial ratios: Quantile hazard model approach

This study examines the role of financial ratios in predicting companies’ default risk using the quantile hazard model (QHM) approach and compares its results to the discrete hazard model (DHM). We adopt the LASSO method to select essential predictors among the variables mentioned in the literature. We show the preeminence of our proposed QHM through the fact that it presents a different degree of financial ratios’ effect over various quantile levels. While DHM only confirms the aftermaths of “stock return volatilities” and “total liabilities” and the positive effects of “stock price”, “stock excess return”, and “profitability” on businesses, under high quantile levels QHM is able to supplement “cash and short-term investment to total assets”, “market capitalization”, and “current liabilities ratio” into the list of factors that influence a default. More interestingly, “cash and short-term investment to total assets” and “market capitalization” switch signs in high quantile levels, showing their different influence on companies with different risk levels. We also discover evidence for the distinction of default probability among different industrial sectors. Lastly, our proposed QHM empirically demonstrates improved out-of-sample forecasting performance.