Examination and Modification of Multi-Factor Model in Explaining Stock Excess Return with Hybrid Approach in Empirical Study of Chinese Stock Market

Jian, Huang; Liu, Huazhang

doi:10.3390/jrfm12020091

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Similarly, these factors are not significant in pricing the assets on the Turkish stock market Azimli (2020) and German equity market (Dirkx & Peter, 2020). Huang and Liu (2019) documents the limitation of the five-factor model in describing average equity returns across different industries in China. In addition, it has also been found that the inclusion of the profitability and investment factors does not enhance the three-factor model's explanatory power (Zaremba et al, 2021).…”

Section: Research Themes and Discussionmentioning

confidence: 90%

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

Sohor,

Low

2024

IJARAFMS

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Despite producing mixed findings, the Fama and French's (2015) model has been widely used to explain stock returns. This paper conducts a systematic literature review of the Fama and French's (2015) five factor model to evaluate its empirical validity in estimating the average returns of stocks. The review revolves around four major themes: performance of the fivefactor model in explaining stock returns and whether it outperforms the three-factor model in stock returns estimation; predictive power of the two new factors in the five-factor model namely profitability and investment factors; value factor's redundancy for describing average stock returns after accounting for profitability and investment factors; and the exploration of other factors that could enhance the model's explanatory power. The paper used the PRISMA (The Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method as a search strategy to conduct the systematic reviews. Highly cited articles published the Web of Science database from 2015 to 2022 were explored and a final sample of 61 were used in the review. The reviews find that in most studies, the Fama-French five-factor model is superior to the three-factor model in predicting the average stock market return at the national and regional levels. On the predictive power of profitability and investment factors, the findings were mixed. Similar conclusion was reached for the redundancy of value factor in explaining stock returns. Several other factors have also been found to increase the explanatory power of the five-factor model. The systematic literature of this paper evaluates several specific research questions related to Fama-French models, thereby providing researchers an understanding of the research topic and its evolution over time. Future review could be further expanded further to include articles from the Scopus-indexed journals. Insights from this paper are also useful for investors engaging in factor investing and in formulating asset allocation strategy.

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Section: Research Themes and Discussionmentioning

confidence: 90%

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

Sohor,

Low

2024

IJARAFMS

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“…The study analyzed the P/E ratios of 10 leading banks and their relationships to overall stock market performance, focusing on the ROE, the price-book value (PBV) ratio, and the Philippine Stock Exchange (PSE) index as significant variables. Huang and Liu [77] investigated the application of a multifactor model to understand correlations between various factors and stock returns on the Chinese market. Their hybrid approach combined traditional statistical analysis and machine learning techniques to enhance the accuracy of predicting stock returns.…”

Section: Other Techniques Related To the Stock Marketmentioning

confidence: 99%

A Hybrid MCDM Approach Using the BWM and the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks

Alsanousi,

Alqahtani,

Makki

et al. 2024

Information

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This study presents a hybrid multicriteria decision-making approach for evaluating stocks in the Saudi Stock Market. The objective is to provide investors and stakeholders with a robust evaluation methodology to inform their investment decisions. With a market value of USD 2.89 trillion dollars in September 2022, the Saudi Stock Market is of significant importance for the country’s economy. However, navigating the complexities of stock market performance poses investment challenges. This study employs the best–worst method and the technique for order preference by similarity to identify an ideal solution to address these challenges. Utilizing data from the Saudi Stock Market (Tadawul), this study evaluates stock performance based on financial criteria, including return on equity, return on assets, net profit margin, and asset turnover. The findings reveal valuable insights, particularly in the banking sector, which exhibited the highest net profit margin ratios among sectors. The hybrid multicriteria decision-making-based approach enhances investment decisions. This research provides a foundation for future investigations, facilitating a deeper exploration and analysis of additional aspects of the Saudi Stock Market’s performance. The developed methodology and findings have implications for investors and stakeholders, aiding their investment decisions and maximizing returns.

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“…Huang and Liu proposed hypotheses that 7 risk factors (market premium, size premium, book-tomarket premium, profitability premium, investment growth premium, momentum premium, asset turnover premium) have a positive relationship with excess return. This research used some statistical methods (e.g., stability test, OLS regression, ridge regression, robustness test, Chi-Square test) to verify the hypotheses, providing some theoretical guidance for others [5].…”

Section: Introductionmentioning

confidence: 99%

Volatility Prediton based on Multiple-factor Model

Yang

2023

HBEM

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Generally, due to unpredictable and vulnerable features of the stock market, traditional statistical methods with single factor cannot explain the movements of stock prices well. In this paper, multi-factor model of quantitative stock selection is applied to predict stock price index to perceive the fluctuation of stock in multiple aspects and improve the accuracy of prediction. This study also combines machine learning measurements with historical data fitting effects. The data used for analysis is all collected from Tushare website, and the stock data are from the 2015 to 2022 data of Shanghai Pudong Development Bank and China Merchants Bank, and the selected factors include price-to-earnings ratio, price-to-book ratio, volume ratio, total equity and so forth. Ordinary least square linear regression and random forest nonlinear approaches are utilized to predict the stock price. According to the analysis, the accuracy of random forest is higher than OLS in stock price prediction. Among all the factors, the opening price, the highest price and the lowest price have relatively large impacts on the closing price of the next day. However, when selecting different factors, stocks and train-testing periods, the obtained regression models are different. Therefore, the determination of the correlation coefficient is not invariable and needs to be analyzed on a case-by-case basis. Overall, these results shed light on the superiority of machine learning model and the significant contribution of some factors on stock price prediction.

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Examination and Modification of Multi-Factor Model in Explaining Stock Excess Return with Hybrid Approach in Empirical Study of Chinese Stock Market

Cited by 4 publications

References 24 publications

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

A Hybrid MCDM Approach Using the BWM and the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks

Volatility Prediton based on Multiple-factor Model

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