Comparison of the Performances for CAPM and Fama-French models During COVID-19

Since the outbreak of COVID-19, it has caused a startling stun to both society and economy in numerous nations, where different industries suffered unequally. This paper reviews the various performance of the Capital Asset Pricing Model (CAPM), and the Fama-French three-factor model and the five-factor model in different regions and industries. To metric the performance, various statistics models and scaling are applied including Pearson correlation, linear regression, R2 scores, t-test, etc. Specifically, this paper demonstrates the different performances of the CAPM model on the US and Egyptian stock markets, whereas using generalized method of moments in a panel data analysis to evaluate the performance in the U.S. market and the paired sample t-test and Wilcoxon signed-rank to evaluate the performance in the Egyptian market. The Fama-French three-factor model and five-factor model are both based on the U.S. market and analyze the model's performance (measured by significant level) in the U.S. market in general and in individual sectors, respectively. Whereas, in terms of three-factors model, the OLS estimation and relapse expected excess return are used onto the variables and multiple linear regression method was used to study the significance of factors in three sub-industries. Regarding to five-factors model, a multivariate regression with covariates and OLS estimation are the method for evaluation. These results shed light for deeply understanding the model and recognizing the impact on the security market of the COVID-19. © 2023 SPIE.

BETA INVERSION EFFECT OF COVID-19 PANDEMIC USING CAPITAL ASSET PRICING MODEL

This paper aims to analyze the effect of the beta inversion on COVID-19 by applying the capital asset pricing model and difference-in-differences (DiD) model in the US covering the five-year period from April 26, 2017, to April 22, 2022. Coronavirus induced pandemic has altered the fundamentals of the market (Baker, Bloom, Davis, Kost, et al., 2020;Mazur et al., 2021). The higher the value beta, the greater the potential for better long-term returns, according to the capital asset pricing model (CAPM). This study showed that companies that appeared to be safe to invest in are suddenly more dangerous, and the opposite is also true. Such as industries that previously shown a contractionary effect — aviation and retail, during COVID-19 have shown more benign effects on the market. The DiD model also reveals the same. The World Health Organization (WHO) intervention had a negligible effect on the treatment group, according to the model. It is obvious that beta has been inverted before investing in these sectors. The companies that are expected to perform better like pharma and biotech, have underperformed. This study deploys the understanding of the capital asset pricing model to see how different markets performed during and before the pandemic. © 2023 The Authors.

The impact of COVID-19 on the banking sector. Are we heading for the next banking crisis?

Purpose: This study aims to examine the effect of the COVID-19 pandemic on the banking sector and to assess if COVID-19 was a trigger for the banking crisis. Design/methodology/approach: To achieve the main objective, the beta of the banking sector was calculated and analysed. In addition, a fixed panel regression model was applied over the period from the 30th of December 2019 until the 24th of September 2021. Findings: The results suggest that the pandemic contributed to higher volatility and risk in banking sector but did not confirm a systematic banking crisis. Originality/value: This paper contributes to the literature by analysing the COVID-19 pandemic as a potential trigger for a banking crisis. This paper also contributed by studying the effects of COVID-19 on the banking sector, especially the risk in the banking sector. © 2023, Emerald Publishing Limited.

Investing style and sensitivity analysis in U.S. stock market during Covid-19 based on multi-factor model

According to the unimaginable influence of Covid-19 and the essential of capital asset pricing in the market, this article analyzes the TV industry of the US stock market before and during the epidemic based on the Fama-French five-factor model. Fama-French five-factor model comprehensively considers the impact of market risk premium (Mkt-RF), market value scale factor, (SMB), book-to-market value ratio factor (HML), profit factor (RMW) and investment factor (CMA) on this industry. Meanwhile, it can conduct a comprehensive evaluation of the impact of Covid-19 on the TV industry. The data in this article was selected from Kenneth R. French's databases and used multiple linear regression to obtain the results. The performance of factors is different due to the outbreak of Covid-19. By analyzing the result, it found that Mkt-RF, SMB are not significant in the model, but HML, RMW, CMA have changed from insignificant to significant. It indicates that during the Covid-19, investors are recommended to pay more attention to the firms with high book-to-market ratios, stable profitability, and aggressive investment style in the USA TV industry. Therefore, research on the stock market of the TV industry plays an important role in the steady development of the economy, the creation of social wealth, and the improvement of people's living standards. © 2021 IEEE.

Research on the impact of Covid-19 to the clothing industry based on fama-french model

During the Covid-19 pandemic period, the U.S. government and the Federal Reserve started a radical monetary simulation plan to save the economy. The current capital market experiences more fluctuation and are more unpredictable. Capital asset pricing tools are more important at such a special time. This article uses the Fama-French five-factor Model and analyzes the clothing industry in the United States from July 2019 to November 2020, separating into pre and post pandemic periods. The regression analysis performed contains five coefficient factors, and each corresponds to a specific property of the clothing industry after Covid-19 emerged. The market factor indicates that the clothing industry is negatively affected by the pandemic. SMB shows that small businesses are more favorable to investors. HML indicates that value stocks are more preferred. RMW shows that investors tend to look for companies with higher profitability, whereas CMA indicates a minimal change to the clothing industry. It is recommended that more attention should be paid to the companies with small-cap, high BM ratio and stable profitability. © 2021 ACM.

Research on the Impact of Covid-19 on the chemistry industry in us stock market based on the fama-french five-factor model

The capital asset pricing model (CAPM) and the Fama-French model are of great significance to the study of all aspects of the capital market, which lays the foundation of modern finance. This paper analyzes both data before Covid-19 and after Covid-19, which are daily data with the same time length in the chemistry industry. It finds that Covid-19 makes a significant impact on the chemistry industry due to its negative influence on economics. The data were adopted from Kenneth R. French's database and fitted with the Fama-French five-factor model. After data processing, the T-value used for significant testing and five corresponding coefficients is obtained using multiple linear regression. The results indicated that Covid-19 causes an anomaly because the intercept is significant after the epidemic, a decrease of market sensitivity from changes of market factor, investors' more attention to the companies that have a good ability to gain profitability by analyzing RMW, and better returns for the companies with high book-to-market ratios due to HML. Besides, SMB and CMA factors are redundant. In conclusion, the influence made by Covid-19 on the chemistry industry is significant, and the investors are recommended to pay attention to companies with robust profitability and high book-to-market. © 2021 ACM.

CIRD-F: Spread and Influence of COVID-19 in China.

The outbreak of coronavirus disease 2019 (COVID-19) has been spreading rapidly in China and the Chinese government took a series of policies to control the epidemic. Therefore, it will be helpful to predict the tendency of the epidemic and analyze the influence of official policies. Existing models for prediction, such as cabin models and individual-based models, are either oversimplified or too meticulous, and the influence of the epidemic was studied much more than that of official policies. To predict the epidemic tendency, we consider four groups of people, and establish a propagation dynamics model. We also create a negative feedback to quantify the public vigilance to the epidemic. We evaluate the tendency of epidemic in Hubei and China except Hubei separately to predict the situation of the whole country. Experiments show that the epidemic will terminate around 17 March 2020 and the final number of cumulative infections will be about 78 191 (prediction interval, 74 872 to 82 474). By changing the parameters of the model accordingly, we demonstrate the control effect of the policies of the government on the epidemic situation, which can reduce about 68% possible infections. At the same time, we use the capital asset pricing model with dummy variable to evaluate the effects of the epidemic and official policies on the revenue of multiple industries.