Stock Return Dynamics Research Articles

Capital Structure's Role in Shaping Stock Return Dynamics

This study examines the intricate relationships among capital structure, current ratio, return on assets (ROA), long-term debt-to-equity ratio, and stock returns within the property and real estate sector, utilizing a sample of 16 companies. Employing Structural Equation Modeling (SEM), capital structure is introduced as a mediating variable to elucidate its role in the association between the investigated financial metrics and stock returns. Results indicate that neither the current ratio nor ROA significantly influences stock returns, while the long-term debt-to-equity ratio negatively impacts capital structure. Surprisingly, the current ratio exhibits a positive effect on stock returns, while the long-term debt-to-equity ratio shows no significant impact. Notably, capital structure emerges as a positive determinant of stock returns. This study provides a novel perspective by introducing capital structure as a mediator, contributing to the existing body of knowledge. Practical implications suggest that property and real estate investors should consider financial ratios alongside external factors like exchange rates and economic conditions when making investment decisions. However, limitations associated with the study's focus on IDX-listed companies in this sector are acknowledged, necessitating careful interpretation of the findings.

Financial uncertainty and stock market volatility

AbstractThis study explores the relation between financial uncertainty and volatility in China. The time variation in financial uncertainty shocks is theoretically closely related to stock return dynamics. Empirically, the financial uncertainty measure is based on a large set of economic and financial variables and captures its unpredictable component. Over the sample period from 2000 to 2021, we find that financial uncertainty positively impacts the trend component of market volatility and that it improves volatility predictions in both statistical and economic terms. Our study sheds new light on the sources driving volatility and the dynamic relation between uncertainty and volatility components.

Cross-sectional implications of dynamic asset pricing with stochastic volatility and ambiguity aversion

Building on recent research that highlights the importance of macroeconomic volatility and ambiguity aversion in explaining the dynamics of stock returns, in this paper we propose a dynamic asset pricing model that simultaneously accounts for stochastic macroeconomic volatility and ambiguity, assuming that investors deal with uncertainty about the mechanics of macroeconomic fluctuations using first-release consumption and revisions to aggregate consumption on vintage data. Our results show that the proposed model captures a large fraction of the cross-sectional variation of excess returns for a wide range of market anomaly portfolios. Furthermore, while the price of risk for ambiguity is positive and significant for the vast majority of assets under study, macroeconomic volatility yields ambiguous outcomes, although it significantly increases the explanatory power of the model for specific assets. Our results suggest that macroeconomic volatility and ambiguity complement each other in explaining the cross-sectional behavior of stock returns.

Jump-diffusion volatility models for variance swaps: An empirical performance analysis

This paper studies a class of tractable jump-diffusion models, including stochastic volatility models with various specifications of jump intensity for stock returns and variance processes. We employ the Markov chain Monte Carlo (MCMC) method to implement model estimation, and investigate the performance of all models in capturing the term structure of variance swap rates and fitting the dynamics of stock returns. It is evident that the stochastic volatility models, equipped with self-exciting jumps in the spot variance and linearly-dependent jumps in the central-tendency variance, can produce consistent model estimates, aptly explain the stylized facts in variance swaps, and boost pricing performance. Moreover, our empirical results show that large self-exciting jumps in the spot variance, as an independent risk source, facilitate term structure modeling for variance swaps, whilst the central-tendency variance may jump with small sizes, but signaling substantial regime changes in the long run. Both types of jumps occur infrequently, and are more related to market turmoils over the period from 2008 to 2021.

A Stock’s 52-week Price Anchor and the Subsequent Dynamics of Stock and Corporate Bond Monthly Returns

A Stock’s 52-week Price Anchor and the Subsequent Dynamics of Stock and Corporate Bond Monthly Returns

Joint Dynamics of Stock Returns and Cash Flows: A Time-Varying Present-Value Framework

Joint Dynamics of Stock Returns and Cash Flows: A Time-Varying Present-Value Framework

Testing the animal spirits theory for ethical investments: further evidence from aggregated and disaggregated data.

This study aims to test the animal spirits theory by Akerlof and Shiller (Animal spirits - how human psychology drives the economy, and why it matters for global capitalism? Princeton University Press) for ethical stock markets using Islamic and sustainable stock indexes during calm and crisis periods. This question helps determine whether ethical finance is driven more by its specific rules or determined by animal spirits. We used data covering January 1996–September 2021, which includes both calm periods and crisis periods (dot-com bubble of 2000, subprime crisis of 2007, global financial crisis of 2008–2009, and COVID-19 recession). Accordingly, we applied different time series tests, ran a quantile regression, and built an econometric framework to empirically test the animal spirits theory. We provide two key findings. First, investor sentiment and consumer confidence significantly affect the dynamics of both ethical stock returns, suggesting further evidence of animal spirits. This finding supports the assumption that investors’ emotions and sentiments affect their behaviors and related feelings, for example, spontaneous instinctive that urge to action than inaction, optimism, and so forth, might help to apprehend some investment actions. Second, and interestingly, animal spirit effects enter asymmetrically and nonlinearly as their effects on ethical stock returns are time-varying and vary with the quantile under consideration.

Are ESG-committed hotels financially resilient to the COVID-19 pandemic? An autoregressive jump intensity trend model.

Given that the United Nations views environmental, social, and governance (ESG) as a practical framework for anchoring responsible corporate behavior to achieve its sustainable development goals, this study constructs an autoregressive jump intensity trend (ARJI-trend) model to determine if ESG can improve future resilience and create crisis-resilient value for chained-brand hotel corporations from the effects of COVID-19. The findings indicate that the ARJI-trend model indeed captures both the permanent and transitory components of the hotel corporation's ESG performance related to stock return dynamics. When ESG rating is taken into account, the following conclusions emerge: 1) the transitory component of time-varying return variance decreases but the permanent component does not; 2) the hotel corporation portfolios with a lower transitory component experiences a higher return, implying that the hotel corporations with a higher ESG rating appear to be more defensiveness; and 3) with proper asset reallocation, a portfolio centered on strong ESG-conscious hotel corporations is a safe-haven asset during market turmoil.

Understanding how ESG-focused airlines reduce the impact of the COVID-19 pandemic on stock returns

Incorporating environmental-social-governance (ESG) into a company's operations is an innovation strategy for contemporary businesses and a countermeasure for airline companies under COVID-19's influence. This research employs an autoregressive jump intensity trend (ARJI-trend) model to analyze the effects of COVID-19 and ESG ratings on the stock performance of the U.S. airline industry. We find that the ARJI-trend model captures the short- and long-run impacts of COVID-19 and ESG on stock return dynamics. Moreover, short-run stock return volatility converges to the original equilibrium level faster when a company has a higher ESG score, implying that promoting ESG does offer a defense mechanism to airline companies and that ESG performance is suitable for integration into business operational goals. The results lay the groundwork for understanding how an ESG focus might help airline companies to suffer less of an economic/financial impact during crises such as the COVID-19 pandemic.

Efekat dana u nedelji na južnoafričkom tržištu akcija - a garch analysis

Understanding dynamics of daily stock returns provide insight in trading opportunities available in stock markets. The purpose of the study was to examine whether day of the week effect exists in the South African equity market. Daily data from Top 40, All Shares, Basic Materials, Industrials, Consumer Goods, Health Care, Consumer Services, Telecommunications, Financials and Technology indices were collected for period 1995 to 2018. Exponential and threshold generalized auto regressive conditional heteroskedasticity models were employed to analyse day of the week anomaly. Findings of the day of the week for the mean equation revealed a positive Monday effect for aggregate indices namely Top 40 and All shares whilst the sectorial analysis showed a positive Monday effect for Basic materials, Consumer goods, Health care and Telecommunication. Furthermore, the mean equation for day of the week depicted a positive Tuesday effect for Financials sector, positive Wednesday effect for Consumer services sector and Thursday effect for Industrials and Technology sectors. The variance equation highlighted negative Monday effect for Top 40 and All shares as well as Basic materials, Consumer goods, Health care, Consumer services, Telecommunication and Financials sectors. However, Industrials sector indicated a negative Friday effect. The existence of day of the week effect nullifies the efficient market hypothesis in its weak form. In practice, it is recommended that for Mondays investors should invest in Top 40 and All shares, for Tuesday and Wednesday it would be prudent for investors to invest in Financial and Consumer services sectors respectively. Returns for Thursdays are attractive to an investor investing in the Industrial sector. An investor can reduce exposure by diversifying in the Health sector on Monday and in the by Industrial sector on Friday. Unlike previous studies that focussed on aggregate market indices, this study extended the analysis to sectors that constitute the market index.

Dynamics of bond and stock returns

A production-based equilibrium model jointly prices bond and stock returns and produces time-varying correlation between stock and real treasury returns that changes in both magnitude and sign. The term premium is time-varying and changes sign. The model incorporates time-varying risk aversion and two physical technologies with different cash-flow risks. Bonds hedge risk-aversion shocks and command negative term premium through this channel. Cash-flow shocks produce co-movement of bond and stock returns and positive term premium. Relative strength of these two mechanisms varies over time. The correlation is a powerful predictor of relative bond-stock and long-short equity returns in the data.

On the time-varying dynamics of stock and commodity momentum returns

Using a state-of-the-art Markov switching framework augmented by popular proxies of arbitrage activity and investor sentiment, we reexamine the dynamics of stock momentum returns and provide a first structured time-series analysis of commodity momentum portfolios. Our study arrives at the important finding that, in contrast to previous studies relying on restrictive static models, we cannot detect persuasive links between momentum returns and such variables in recent data. Consequently, the evolution of momentum returns remains puzzling. Furthermore, putting the behavior of extremes aside, stock and commodity momentum returns exhibit quite similar regime-switching behavior. This supports the frequent statement that the financialization of commodity futures markets has non-trivially linked stock and commodity returns.

The Role of Investor Sentiment and Valuation Uncertainty in the Changes around Analyst Recommendations: Evidence from U.S. Firms

The authors investigate the empirical relation among investor sentiment, valuation uncertainty, and announcements of changes in analyst recommendation decisions among U.S. firms. Recent behavioral finance evidence shows market sentiment to have predictive content that affects the classical relationship between analyst recommendations and stock return dynamics. Contrary to this evidence, the authors find that degree of valuation uncertainty is associated to the impact of investor sentiment when examining a likelihood of consensus recommendation upgrade or downgrade. While not totally eliminating the significant investor sentiment effect under high valuation uncertainty, the investor sentiment does not powerfully explain the stock market reactions to analyst recommendation changes under low valuation uncertainty. Furthermore, the authors show that analyst recommendations provide significant buy or sell signals if valuation uncertainty is great, referring to the market being highly competitive. However, in less competitive markets, analyst reports become less informative. Overall, the authors demonstrate that magnitude of valuation uncertainty is an important complement to investor sentiment for further understanding analyst recommendations.

Firm, Industry and Macroeconomics Dynamics of Stock Returns: A Case of Pakistan Non-Financial Sector

The available research literature on stock performance has primarily stressed the importance of asset price theories, macroeconomic and microeconomic, and institutional differences. However, there is still an open question: Are there any other factors those influence stock performance? This research aims to answer this question by providing new insights into industry factors along with country-level and firm-specific factors in conjunction with the stock performance of the non-financial sector firms listed at the Pakistan Stock Exchange. The study provides new insights into the prevailing research literature by considering an emerging economy, Pakistan. We find that non-financial sector firms are heterogeneous, suggesting applying a fixed effect approach for reliable estimation. To investigate the issue, data from 80 companies spanning 17 years (2004–2020) were analyzed with a fixed-effect model. Our study results revealed that firm tangibility, munificence, gross domestic product, inflation and money supply have negative, while size, growth, dynamism, Herfindahl–Hirschman index, exchange rate and oil prices have a positive relationship with financial performance. The results are robust under alternative estimation approaches and offer useful policy implications.

Dividend Momentum and Stock Return Predictability: A Bayesian Approach

A long tradition in macro-finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra restriction that dividend growth is not persistent. We highlight that persistence in dividend growth induces a previously overlooked channel for return predictability, which we label ``dividend momentum.'' Compared to estimation based on OLS, our restricted informative prior leads to a much more moderate, but still significant, degree of return predictability, with forecasts that are helpful out-of-sample and realistic asset allocation prescriptions with Sharpe ratios that out-perform common benchmarks.

Measuring the effects of investor attention on China's stock returns

<abstract> <p>The increasing abundance of information leads to the scarcity of investor attention, which has become an important factor affecting the financial market. Search engines play the role of information retrieval and record the search behavior of investors, which is a direct and accurate measure of investor attention. This paper investigates the relationship between investor attention and China's stock market. Considering the relationship with stock returns as the mainline, we take the Baidu index as a substitute variable of investor attention to deeply study the correlation and the time-varying nature between investor attention and China's stock returns. To this end, we used quantile regression to examine the relationship over the period 2006–2021 to capture its evolution during calm and turbulent times. We thus investigated the effect of investor attention on the mean and other quantiles. Our findings show that the relationship between investor attention and China's stock returns exhibits time-variation as investor attention significantly impacts the dynamics of China's stock returns, but its sign and effect vary per quantile: investor attention is negatively correlated with stock returns at low quantiles, but it turns positive at high quantiles. In addition, to test the model's robustness, variable replacement method and model replacement method are used to conduct significance tests, respectively. The results are equally significant.</p> </abstract>

Modeling Volatility Spillovers Between Stock Returns, Oil Prices, And Exchange Rates: Evidence from Russia and China

This study investigates the interdependence between crude oil fluctuations and stock return dynamics of major oil BRICS stock market returns namely China and Russia, over the last turbulent period ranging from September 2001 to March 2019. We used a VAR-GARCH model that allows for simultaneous spillover in volatility and return, under the Student’s t- distribution. In addition to crude oil prices, foreign exchange rates are so included in the model to strengthen its explanatory power. The

Investors’ attention: does it impact the Nigerian stock market activities?

PurposeThe author investigates whether investors’ online information demand measured by Google search query and the changes in the numbers of Wikipedia page view can explain and predict stock return, trading volume and volatility dynamics of companies listed on the Nigerian Stock Exchange.Design/methodology/approachThe multiple regression model which encompasses both the univariate and multivariate regression framework was employed as the research methodology. As part of our pre-analysis, we test for multicollinearity and applied the Wu/Hausman specification test to detect whether endogeneity exist in the regression model.FindingsWe provide novel and robust evidence that Google searches neither explain the contemporaneous nor predict stock return, trading volume and volatility dynamics. Similarly, results also indicate that trading volume and volatility dynamics have no relationship with changes in the numbers of Wikipedia pages view related to stock activities.Originality/valueThis study opens new strand of empirical literature of “investors' attention” in the context of African stock markets as empirical evidence. No evidence from previous studies on investors' attention exist, whether in Google search query or Wikipedia page view, with respect to African stock markets, particularly the Nigerian stock market. This study seeks to bridge these knowledge gaps by examining these relations.

Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents’ perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents’ time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents’ learning plays an important role in the dynamics of stock returns.

Value‐at‐risk forecasting via dynamic asymmetric exponential power distributions

AbstractIn the value‐at‐risk (VaR) literature, many existing works assume that the noise distribution is the same over time. To take into account the potential time‐varying dynamics of stock returns, we propose a dynamic asymmetric exponential distribution‐based framework. The new method includes a time‐varying shape parameter to control the dynamic shape of the distribution, a time‐varying probability parameter to control the dynamic proportion of positive returns, and a time‐varying scale parameter to control the dynamic volatility. We combine the generalized method of moments and the exponentially weighted moving average (EWMA) approach to derive specifications for these time‐varying parameters. Empirical applications demonstrate the superior performance of the proposed method when compared with various GARCH and EWMA approaches without time variation in the innovations.