Explaining Stock Returns Research Articles

Stock Markets during Different Waves of COVID-19 Pandemic: An Empirical Study on the Comparative Significance of Information and Sentiment

This study has investigated the impact of Covid-19 on stock markets in perspective of cases, deaths, and sentiments during different waves of pandemic in emerging countries. In order to run analysis OLS technique have been applied. The study found that information and sentiment might have different implications for different markets, where information seemed to be. Sentiment was deemed important for USA and India, whereas information aspect was deemed important for India and Pakistan, while both sentiment and information failed to explain stock returns in China. This implied that sentiment was important for countries where pandemic was not managed or controlled, whereas information was important for countries with some control on the pandemic, and both information and sentiment were not important for the country with stringent control on the pandemic and also on media. It is recommended on findings of the study that both information and sentiment could be important in different markets, during different situations and investors should consider dynamics of individual markets for their investment decisions

Determinants of stock return in 10 biggest market capitalization on the Indonesian stock exchange

Several research results on the capital market in Indonesia fall into the category of market efficiency in the weak form, so the results of previous research do not reflect the theory of capital market efficiency in the strong form. The purpose of this research is to analyze and answer the existence of inconsistencies in the results of previous research as well as the phenomenon of stock returns which are not as described in capital market theory in strong form. This is what prompted researchers to do it again using different time series and cross-sectional data. This type of research is quantitative descriptive with a panel data multiple regression analysis method using 10 sample companies that are members of 50 large capitalization companies for 7 years. This research formula is to maximize the Stock Return value through leverage as an intervening variable using research objects of companies on the Indonesia Stock Exchange. Two research models are integrated into one and each goes through a model selection test stage, namely the Chow Test, Hausman Test, and Lagrange Multiplier Test using the eviews12 application. The results of this research explain that increasing ROA can have an impact on reducing Leverage (DER) and through DER it can explain the impact on Stock Returns, but ROA cannot directly explain Stock Returns. It is hoped that these results can help as a guide for investors to get maximum Stock Returns.

The Role of Political Uncertainty in Climate-Related Disaster Impacts on Financial Markets

This research presents a new model for analyzing the effects of government policies on climatic disasters on financial markets. Using Fama–MacBeth rolling regressions and the construction of model-proposed risk factors, three major risk factors are found to be significant in explaining stock returns. First, there is the risk of climate disasters. Second, there is the risk of uncertainty regarding government actions. Third, there is the risk of government response to climatic disasters. Through the increase in the cost of capital from climate disasters and the uncertainty of government response, the future cost of capital is higher, leading to less investment and lower productivity. However, the government’s actions to compensate for losses due to climate damage help offset the damages from disasters. This implies that the previous estimates of economic damages due to climate risk have been underestimated. This work adds to the literature by providing a fuller estimate of the economic implications of climate change

Labor market peer firms: understanding firms’ labor market linkages through employees’ internet “also viewed” firms

AbstractThis paper studies the grouping of firms based on their labor-market connections, a significant departure from the traditional approach of grouping based on product-market connections. It also proposes a measure of labor market peers by analyzing the “also viewed” companies on two major online labor market platforms, LinkedIn and Glassdoor. Using the labor market peer measure, I examine whether firms that hire employees with similar skills and that are presumably exposed to the same labor-related risks and shocks exhibit a strong comovement of stock returns and accounting-based performance variables. I find that labor market peers overlap but differ from traditional product-market-based industry groupings, have significant incremental power to explain stock return and accounting-based performance measure comovements, and outperform traditional industry groupings in explaining return and wage comovements when a base firm shares more labor skills with its peers. Overall, the study highlights that labor market peers capture fundamental linkages between firms that are challenging to identify using traditional industry measures.

Impact of Firm's Value Relevance of Accounting Information on Stock Returns during Market over and under Reactions in Pakistan

Value relevance of accounting information (VRAI) is a fundamental characteristic of influencing stock returns. This study analyzes the impact of VRAI on stock returns during market under and overreaction in Pakistan. The data of 322 non-financial firms listed in PSX from 2005 to 2022 is used. To explain stock returns, we employ the annual stock returns and buying and holding returns model. The data on financial variables has been collected from annual reports published on the company's websites and PSX Data Stream. The outcomes show that the accounting information variables earnings per share and change in earnings per share are positively related to the stock returns during market over and under reaction. Earnings per share, net income and book value are significantly related to the BHAR diuuring market under and overreaction while earnings per share and book value are significantly related to the BHAR during market overreaction of non-financial firms. It is also concluded that balance sheet variables attract more investors to invest in the stock market during the underreaction of non-financial firms. Therefore, it is suggested that the non-financial firms must present adequate accounting information on their financial statements within the specified time frame.

An Empirical Research of Asset Pricing Models in the US Market under COVID-19

Asset pricing theory has been a key area of research in finance. Since the in-troduction of CAPM, asset pricing models have been very widely used. Re-searchers have discovered many phenomena that cannot be explained by the CAPM model, so they have made many refinements to the asset pricing model and developed various improved versions that can better explain stock returns. The Fama and French three-factor model (FFTFM) and the five-factor model (FFFFM) are two of the most classic and most used asset pricing models. However, as time goes by and financial markets continue to evolve, many scholars have questioned whether traditional asset pricing models are still valid. Starting from the theoretical explanation of CAPM, FFTFM, and FFFFM factor selection, this paper uses the stock market data from 2000 to March 2023 to divide stocks into six portfolios based on their size and book value and conduct regressions according to the above three models respectively to evaluate and compare the recent performance of these models. In addition, this paper also performs separate regressions using data from the novel coronavirus epidemic period in particular to verify the robustness of the model to different market environments. According to the results of this paper, the FFFFM model has the best explanatory power. The role of the factors in the model on stock returns varies with portfolio selec-tion, i.e., firm size and book value, and the market environment also has an impact on the significance of the factors, leaving room for improvement in the model to cope with particular markets, but overall, FFFFM is still effec-tive in explaining present market stock prices.

How does ESG explain excess returns in emerging market? An Asset-Pricing Approach

Objective: Previous studies found several important risk factors for the capital market in explaining stock performance. However, most studies only consider conventional investment factors without considering sustainable ones. This study examines Environmental, Social, and Governance (ESG) performance’s effect as a risk factor in a multi-factor model. Design/Methods/Approach: This study employs secondary data from the company’s financial reports, annual reports, and Thomson Reuters ESG score data. The sample for this study were companies listed on the LQ45 index during the 2015-2019 period, which were selected using the purposive sampling method and produced a selection of 19 non-financial companies that met the criteria. Findings: The results show that ESG negatively affects 21 out of 30 portfolios, and the four-factor ESG model is better at explaining excess returns than the three-factor Fama-French model. Originality/Value: This study provides new insights by including ESG as a risk factor in the three-factor Fama-French model in explaining stock returns. The existence of the ESG variable allows us to identify whether sustainability is an essential determinant in explaining the average portfolio return. This study adds new insights, where using sustainability reports in the form of ESG can capture cross-sectional variations in stock returns, not only on market factors, size factors, and book-to-market factors. Practical/Policy implication: Given the established evidence that ESG factors can mitigate risk, investors are encouraged to thoroughly evaluate a company’s sustainability report to assess the efficacy of its ESG performance. For managers of companies, this serves as the foundation for developing strategies that will enhance the long-term profitability and sustainability of the organization.

COMPARING VALUE - BASED AND TRADITIONAL MEASURING TOOLS. EVIDENCE FROM PAKISTAN

This paper's primary goal is to evaluate the assertion that value-based measures are preferable to traditional accounting based financial-performance measures, such as profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE), and return on investment (ROI), in the non-financial sector of Pakistan, while also providing empirical evidence to support the claim. Additionally, it evaluates and tests the informational value of different performance measures and how they relate to stock return. The sample of 97 non-financial Pakistani companies included in this study was obtained from the Pakistan Stock Exchange (PSX) between 2016 and 2021. The information richness of value-based measurements and conventional performance measures in explaining shareholders' returns is investigated using a variety of regression models. Relative information content tests shows that traditional accounting-based measures such as EPS, ROE and ROA performed better than value based measures in explaining the returns of Pakistan non-financial companies. Incremental information content of value based measures doesn’t have any contribution to information content above traditional performance measures. The claim of superiority of value based measures over accounting-based measures in association with shareholder returns is proved invalid in Pakistan non-financial companies. According to the study's findings, value-based financial performance indicators are not any better at explaining stock returns of Pakistani non-financial enterprises than traditional accounting-based financial performance measures. Panel data are tested using the Hausman's test for fixed and random effects, the variance inflation factor (VIF) test for multicollinearity, and the Durbin-Watson test for autocorrelation in order to ensure substantial results.

Idiosyncratic volatility, network centrality, and stock returns

To study interconnectedness in the stock market and examine the impact of network centrality on stock returns, this study constructs a network of Turkish stocks based on idiosyncratic volatility correlations. We find that the book-to-market ratio and market size are negatively associated with network centrality, whereas idiosyncratic volatility and trading volume are positively associated with it. Furthermore, we show that the centrality factor can explain stock returns in the Turkish stock exchange better than size and value factors. The inclusion of the centrality factor increases R2 by as much as twenty percentage points. Furthermore, we find that higher network centrality scores can predict higher future stock returns, even when size, the book-to-market ratio, idiosyncratic volatility, trading volume, and lagged returns are controlled for. We also find a positive relationship between one-month-lagged idiosyncratic volatility and stock returns and show that this relationship is stronger for less visible stocks.

Inclusion of debt claims in asset pricing models: Evidence from the CDS Index

Asset pricing theory suggests that the correct proxy for the market portfolio should contain both the debt and equity claims of the economy, whereas prevailing empirical studies fail to include the debt claim. Motived by the discrepancy between the theoretical and empirical models and the difficulty in constructing proxies, the study uses the Credit Default Swaps (CDS) market index as a proxy for the debt market and empirically tests its explanatory power in explaining stock return variations. Employing panel regression and Fama-MacBeth regression of all publicly traded U.S. companies from 2005 to 2020, the study finds a negative relationship between CDS index returns and stock returns. On average, a one standard deviation increase in CDS index return is associated with a 0.02% decrease in daily stock returns. Results of two-stage regressions show that the estimated systematic credit risk is positively priced in stock returns with similar economic magnitude as the well-documented beta risk. These results support asset pricing theories in the inclusion of debt claim and the risk-return tradeoff, while contradicting the credit risk puzzle documented in prior studies.

How to explain stock returns of utility companies from an environmental, social and corporate governance perspective

AbstractSustainability is a major challenge in today's business world, making environmental, social and governance criteria an indispensable tool for business management and investment decisions. Using 2020 data of 47 companies from the STOXX Europe Total Market Utilities Index and qualitative comparative analysis, this study aims to analyse how the combination of CO2 equivalent emissions, sustainability compensation incentives, environmental investments, environment management training, and policy fair competition leads to utility companies' stock market returns. Two subsample models (electricity and non‐electricity utilities) have been considered in the study. The results point to the absence of CO2 equivalent emissions, the absence of incentives, and the presence of environmental investment as key variables to lead to stock market returns. The findings provide relevant information for managers and practitioners of utilities industry to achieve improvements in the environmental, social and governance management practices, maintaining profitability in financial markets and facilitating decision‐making for investors.

Analysis of Chinese stock market based on Fama-French five-factor model

After almost 25 years of expansion since its founding in late 1990, our stock market has expanded to rank second in the world in terms of market capitalisation. However, the stock market's speculative nature, information asymmetry, and extreme volatility can frequently raise serious concerns. The Fama-French five-factor (FF) model is used in this study to determine the variables affecting stock returns. and compares it to the three-factor model to see which model has a stronger ability to explain stock returns in the A-share market in the People’s Republic of China from the standpoint of asset pricing. The monthly data of A-share listed businesses for a total of 264 months between January 2000 and December 2021 are chosen as the sample in this article. Five factors are built using these data. Additionally, descriptive statistics and correlation analysis are carried out for the Chinese A-share market's five-factor model. The returns of the first four variables for regression are used to explain the returns of the fifth component in a five-factor model redundancy test.Finally, the three-factor intercept model and the five-factor intercept model are then obtained by doing a linear regression on the five-factor model. The results of the empirical test allow for the following inferences: All five factors are significant and have more explanatory power than the three factors since the CMA factor is redundant in the five-factor intercept model and is replaced by CMAO.Significant variables in the three-factor intercept model include MKT ,SMB and HML.

Macroeconomic Factors and Stock Returns: An Approach of Arbitrage Pricing Theory in the Food and Beverage Industry

We examine the efficiency and efficacy of the Arbitrage Pricing Theory model in explaining stock returns in the food and beverage industry. To that end, we analyze the relationship between macroeconomic variables and stock returns before and during the pandemic. In this paper, we use unexpected changes in economic growth, inflation, interest rate, exchange rate, and crude oil price as generating factors and find that these factors have come to explain stock returns movement. Using the generalized least square (GLS) technique, we study stocks from 19 firms listed in Indonesia Stock Exchange over sixteen months from 2018 to 2021. While most significant relations are negative, some are significantly positive. The significant associations suggest that the theory is supported in that estimated expected returns depend on selected generating factors. The result also shows that investors are rational because they consider these factors as systematic risks influencing their profit. Furthermore, we find that there is a difference in stock returns before and during the pandemic, with the average being greater during the pandemic. We assume that this is due to investors’ choice to seek out safe-haven assets in order to limit their exposure to losses.

Macroeconomic Factors and Stock Returns: An Approach of Arbitrage Pricing Theory in the Food and Beverage Industry

We examine the efficiency and efficacy of the Arbitrage Pricing Theory model in explaining stock returns in the food and beverage industry. To that end, we analyze the relationship between macroeconomic variables and stock returns before and during the pandemic. In this paper, we use unexpected changes in economic growth, inflation, interest rate, exchange rate, and crude oil price as generating factors and find that these factors have come to explain stock returns movement. Using the generalized least square (GLS) technique, we study stocks from 19 firms listed in Indonesia Stock Exchange over sixteen months from 2018 to 2021. While most significant relations are negative, some are significantly positive. The significant associations suggest that the theory is supported in that estimated expected returns depend on selected generating factors. The result also shows that investors are rational because they consider these factors as systematic risks influencing their profit. Furthermore, we find that there is a difference in stock returns before and during the pandemic, with the average being greater during the pandemic. We assume that this is due to investors’ choice to seek out safe-haven assets in order to limit their exposure to losses.

Construction and Analysis of Individual Stock Sentiment Anomalies in the A-share Market

Behavioral finance heralds investor sentiment as an anomaly in traditional asset pricing models, and the financing and financing business is an important indicator of individual stock sentiment. In this paper, this paper try to use the growth rate of monthly balance of financing and financing securities as a factor SEN to explain stock returns in the context of Chinese A-share market. This paper finds that market capitalization and SEN have a significant inverse monotonicity, and their information crosses to a large extent to make SEN redundant. This paper argues that the reason for the redundancy of SEN due to market capitalization is that the A-share stock market has set a high margin for financing and financing business, which leads to a weaker liquidity of market transactions and limits the investment choices of rational investors to stocks with relatively small market capitalization. Based on the conclusion, this paper suggests that small investors adopt a strategy of going long on small-cap stocks and short on large-cap stocks.

Economic value-added (EVA) myths and realities: evidence from the Indian manufacturing sector

PurposeThe main aim of this paper is to examine the claim that economic value added (EVA) advocates its superiority over the traditional accounting-based financial performance measures, i.e. profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE) and return on investment (ROI) in the Indian manufacturing sector and at the same time, give empirical facts. It also tests and examines the information content of various performance measures and their relationship with stock returns.Design/methodology/approachThe paper uses the sample of 534 Indian manufacturing companies from the Bombay Stock Exchange (BSE) during the period 2000–2018. Multiple regression models are applied to examine the information content of EVA and traditional performance measures in explaining shareholders’ returns.FindingsRelative information content tests revealed that traditional accounting-based measures such as EPS, ROE and ROA performed better than EVA in explaining the returns of Indian manufacturing companies. Incremental information content of EVA adds little contribution to information content above traditional performance measures. The claim of superiority of EVA over accounting-based measures in association with shareholder returns is proved invalid in Indian manufacturing companies.Originality/valueThis study concludes that EVA has no superiority over traditional accounting-based financial performance measures in explaining stock returns of Indian manufacturing companies. To achieve heftiness in outcomes, panel data are tested by using Breusch–Pagan–Godfrey (BPG) test for heteroskedasticity, Hausman’s test for fixed and random effect, variance inflation factor (VIF) test for multicollinearity and Durbin–Watson test for autocorrelation.

Time‐varying neural network for stock return prediction

AbstractWe consider the problem of neural network training in a time‐varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly US stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators exhibit time‐varying predictive power on stock returns. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.

Metoda za odabir odgovarajućih perioda ulaganja za ostvarivanje profita od arbitraže i objašnjenje pojma "stock returns"

The current paper presents theoretical and experimental evidence to justify the need for paying attention to the investment horizon. Therefore, a criterion called the 'Safest Investment Horizon' (SIH) is utilized to select the appropriate investment horizon. To compute this quantity, a ratio called the 'Safest Investment Ratio' (SIR) is calculated, and the relationship between these criteria and arbitrage opportunities, along with methods for making an arbitrage profit through selecting an appropriate time horizon are discussed. Afterward, by applying this method for real-life data, the presence of arbitrage opportunities at different time horizons is confirmed. Furthermore, the effects of the time horizon on optimal portfolio composition are described. Finally, it is shown that these criteria outperform some of the conventional variables in CAPM, the 3-factor, and the 5-factor models for explaining stock returns and using SIH or SIR as a new variable increases the explanatory power of these models.

Estimating the cost of equity for the regulated energy and infrastructure sectors in India

The cost of equity capital is a key input used by regulators to fix permissible rates of return and determine regulated tariffs. Investors also need an appropriate hurdle rate to make investment decisions. Regulators worldwide often use a single-factor model to estimate the cost of equity. The empirical literature shows that such a model has limited efficacy in explaining stock returns, while other factors also capture the risk involved in the market. This study is perhaps the first to apply three asset pricing models—the capital asset pricing model (CAPM) and the Fama-French three- and five-factor models—to estimate the cost of equity for the Indian energy and infrastructure sectors. We find that the reasonable rate of return fixed by the respective regulator based on the CAPM is often higher than this study's estimated cost of equity (using CAPM). The spread between the regulated return across the identified energy and infrastructure sectors is estimated using a single-factor model, and our estimation using the three-factor model is relatively lower. The spread increases when we apply the recent five-factor model for regulated utilities. The study can guide policymakers and regulators in estimating and fixing reasonable rates of return for the infrastructure sectors.

Order imbalance and commonality: Evidence from the options market

Using a market model and principal component analysis, we investigate the existence of common effects in order imbalance in the Borsa Istanbul's option market. Accordingly, we find the presence of commonality in order imbalance for call options and an even more dominant presence in put options. We investigate the impact of this commonality on the underlying equity market's price discovery; however, the results indicate no significant impact. Our results suggest that, from the order imbalance perspective, equity order imbalance contributes more than options to explaining stock return variations.