Comparison of the ability of AdaBoost machine learning algorithm to explain the accrual anomaly using arbitrage pricing model, capital asset pricing model and Fama-French five-factor model

Document Type : Research Paper

Authors

1 Assistant Professor of Accounting, Islamic Azad University, Baft Branch, Baft, Iran.

2 Ph.D. Student of Accounting, Shiraz University, Shiraz, Iran.

Abstract

  Introduction
Purpose of research is investigating the effect of accrual anomaly on stock return short arbitrage financial model and capital asset pricing by using a neural network. Bankruptcy of companies is one of the ways that leads to wasting resources and not taking advantage of investment opportunities. Predicting financial distress can alert companies to the occurrence of financial distress and subsequent bankruptcy with the necessary warnings so that they can take appropriate action according to these warnings and investors can take advantage of unfavorable opportunities. Recognize and invest their resources in the right opportunities and places. One way to predict the continuity of corporate activity is to use models to predict financial distress; Therefore, the main purpose of this study is to predict the financial distress of companies based on working capital management using artificial neural network.
 
 Research questions
Considering that no coherent research has been done in the field of forecasting financial distress of companies based on working capital management using neural networks method, this research can be an introduction to identify the impact of the role of capital management in Circulation, in order to find solutions to increase the continuity of the company; Therefore, the main questions that this study seeks to answer are as follows. What is the accuracy of predicting companies' financial distress using artificial neural network method based on working capital management variable? How accurate are artificial neural network models, decision tree, support vector machine, multiple audit analysis, and logistic regression in predicting corporate financial distress?
 
 Methods
In order to achieve the purpose of the research, samples consisting of 120 companies listed on the Tehran Stock Exchange during the period 2008-2019 have been studied. In this study, the hypotheses have been tested using the AdaBoost machine learning algorithm, and sales arbitrage pricing models, capital asset pricing model, and Fama-French five-factor model have been used to analyze the anomalies of accruals.
 
Results
In this research after the testing of research hypotheses we got this result, if the effect of accrual anomaly on stock returns is considered, the risks of arbitrage pricing models, capital asset pricing model and the Fama and French five-factor model will be reduced and thus closer to the real stock price. This will increase the return and confidence of investors. The results of comparing the three models based on AdaBoost machine learning algorithm showed that the development of Fama-French five-factor model reduces the neural network training error with AdaBoost algorithm to a greater extent than arbitrage pricing models and capital asset pricing model. In explaining the abnormality of items, it has an obligation on stock returns. This result shows the effectiveness of the inclusion of accruals in the securities pricing models. In other words, the addition of anomalies of accruals to arbitrage financial models, capital asset pricing model, and the Fama-French five-factor model leads to an improvement in stock returns.
 
 Discussion and Conclusion
Investors should distinguish between the stability of profit components (cash and accrual) when valuing companies. The disregard of this difference has made investors optimistic about the future performance of companies when the Firm-Specific Discretionary Accruals is high, and pessimistic about the future of companies when Firm-Specific Discretionary Accruals is low. So, the purpose of testing the first research hypothesis is to investigate the effect of adding accruals anomalies to stock returns of the financial arbitrage sales model; Therefore, using the Adabost machine learning algorithm, the expected return and the actual return have been calculated to determine its impact on market indicators. For this purpose, calculations have been performed without the anomalous effect of accruals. The test results of the first hypothesis showed that the percentage of accuracy and prediction of expected return has multiple errors. Then, calculations were performed based on the effect of accrual anomalies on market indices. The result of these calculations showed a reduction in errors in expected returns. The results of the second hypothesis showed that the addition of anomalies of accruals to the capital asset pricing model in assessing stock returns increases the predictive power of the model. This finding indicates that accruals have informational value. And plays an important role in the stock price valuation process; Because it reduces the scheduling problems and the lack of conformity in the cash figures. The purpose of testing the third hypothesis of the research is to investigate the effect of adding accruals anomalies to the stock returns of the Fama and French five-factor model. The results of testing the third hypothesis showed that adding anomalies of accruals to the capital asset pricing model in assessing stock returns increases the predictive power of the model. In general, the results of the third hypothesis of the research indicate that investors in the processing of accounting information, especially accruals and consequently the valuation of corporate stocks, are faced with incorrect pricing. The purpose of testing the fourth hypothesis of the research is the ability of the five-factor model of Fama and French compared to the traditional model of arbitrage of sales and pricing of capital assets in explaining the anomalies of accruals. The results of the fourth hypothesis test showed that there is an anomaly in accruals on the Tehran Stock Exchange and this anomaly is better explained by the five-factor model of Fama and Farang. For example, the real rate of return in 1387 is equal to 29.103, the projected rate of return after adding the anomaly of accruals to the arbitrage model of 021/28; The projected rate of return after adding the anomaly of accruals to the capital asset pricing model is 25.471; If the projected rate of return after adding the anomaly of accruals to the five factors of Fama and French is equal to 30.221. This trend continues in the same way for the rest of the years under study, and indicates that the predictability of the Fama and French five-factor model is greater than the arbitrage model and pricing of capital assets.
 
 
Keywords: Accrual Anomaly, Arbitrage Pricing Model, Capital Assets Pricing Model, Fama-French Five-Factor Model
 
 
 
 

Keywords

References

 
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Journal of Accounting Advances
Volume 13, Issue 1 - Serial Number 81
October 2021
Pages 261-298

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History

  • Receive Date: 10 March 2021
  • Revise Date: 20 December 2021
  • Accept Date: 20 December 2021

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