The Evaluation of Return and Risk on Investment in Stocks based on the Integration of Asset Pricing Multi-Factor Model and Penalty Function

Subject Areas : Financial engineering

Aliakbar Farzinfar ¹ (Ph.D. Student of Accounting, Faculty of Humanities, Qom branch, Islamic Azad University, Qom, Iran)
hossein Jahangirnia ² (Department of Accounting, Faculty of Humanities, Qom Branch, Islamic Azad University, Qom, Iran.)
Reza Gholami Jamkarani ³ (Department of Accounting, Faculty of Humanities, Qom Branch, Islamic Azad University, Qom, Iran.)
Hasan Ghodrati Ghazaani ⁴ (Department of Accounting and Management, Faculty of Humanities, Kashan Branch, Islamic Azad University, Kashan, Iran.)

Received: 2019-04-09 Accepted : 2019-05-29 Published : 2019-12-22

Keywords: Stock Return, Penalty function, At Risk Value, Capital Assets (Stocks), Multi-factor Model,

Abstract :

Evaluation of stocks based on return and risk related to capital assets is one of the important issues of this field. The majority of multifactor models are defined based on the assessment of one of the return and risk criteria. Nevertheless, the model presented in this study evaluated return and risk simultaneously. The multifactor patterns are static and do not express dynamic changes during time intervals affected by latent factors. In this research, unpredicted fluctuations in stock return were defined as latent factors in the penalty function. A more accurate estimate was provided by using the simulation of Fama–MacBeth regression in the estimation of effective parameters and separation of the effects of latent and manifest factors affecting stock return and risk. According to the analysis of the field of knowledge and content analysis, factors affecting the stock return were recognized, and the most effective factors including market measures were refined as manifest factors based on the tolerances. Finally, the model proposed(P-PCA) was exploited in risk prediction (at risk value). According to the results of the study, the mentioned model more efficiently showed the effects of latent and manifest factors on stock return over a long period. In addition, it was able to predict the risk of investment with proper accuracy and similar to patterns of conditional variance, such as ARCH and GARCH.

References:

• آلاله نرگس، تمیمی محمد، نعمت پوردزفولی علی محمد، "تبیین تغییرات بازده در سه مدل FFPM, TFPM, CAPM در بورس اوراق بهادار تهران." 115-128، 1392.


• ابراهیمی، محمد و سعیدی، علی، تأثیر متغیرهای حسابداری و ویژگی­های شرکت بر قیمت سهام شرکت­های پذیرفته شده در بورس اوراق بهادار تهران، بررسی های حسابداری و حسابرسی، دوره 17، شماره 62، صص 1تا 16، 1389.


• راعی، رضا و پویان‌ فر، احمد، مدیریت سرمایه‌ گذاری پیشرفته، 1395.

• Ando, T., & Bai, J., Asset pricing with a general multifactor structure. Journal of Financial Econometrics, 13(3), 556-604, 2014.


• Black, F., Scholes M., The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81 (May–June), 637–654, 1973.


• Carhart, M. M., On persistence in mutual fund performance, The Journal of Finance, 52(1), 57e82, 1997.


• Chou, R. Y., Yen, T. J., & Yen, Y. M., Risk evaluations with robust approximate factor models. Journal of Banking & Finance, 82, 244-264, 2017.


• Fama, E.F., French, K.R., The Cross-Section of Expected Returns. Journal of Finance, 47(2), 427-465, 1992.


• Fama, E. F., French, K. R., Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics, 33(1), 3-56, 1993.


• Fama, E. F., & French, K. R., A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1e22. https://doi.org/10.1016/j. 2014.10.010, 2015.


• Francis, B., Low-Dimensional Geometry: from Euclidean Surfaces to Hyperbolic Knots, AMS Bookstore, 2009, https://doi.org/10.1090/stml/049.


• Gray, D.F., Merton, R.C., Bodie, Z., Contingent Claims Approach to Measuring and Managing Sovereign Credit Risk., Journal of Investment Management, 5(4), 5–28, 2007.


• Kim, D., Kim, T. S., & Min, B. K., Future labor income growth and the cross-section of equity returns, Journal of Banking & Finance, 35(1), 67e81, 2011. https://doi.org/10.1016/j.jbankfin.2010.07.014.


• Kuehn, L.-A., Simutin, M., & Wang, J. J., A labor capital asset pricing model, The Journal of Finance, 72(5), 2131e2178, 2017. https://doi.org/10. 1111/jofi.12504.


• Lintner, J., The valuation of risk assets and selection of risky investments in stock portfolios and capital budgets, Review of Economics and Statistics, Vol.47, PP 13-37, 1965.


• Mayers, D., Nonmarketable Assets and Capital Market Equilibrium Under Uncertainty, Studies in the Theory of Capital Markets (edited by Michael C. Jensen), 223-48, Praeger Publishers, N.Y., (cited in Jagannathan, R. and Z. Wang, (1996)), 1972.


• Mossin, J., Equilibrium in a Capital Asset Market, Econometrica, 34(4), 768-83, 1966.


• Ng, L., Tests of the CAPM with Time-VaRying CoVaRiances: A MultiVaRiate GARCH Approach, The Journal of Finance, 46(4), 1507-1521, 2009.


• Racicot, F.-E., & Rentz, W. F., Testing FamaeFrench’s new five-factor asset pricing model: Evidence from robust instruments, Applied Economics Letters, 23(6), 1e5, 2017. https://doi.org/10.1080/13504851.2015.1080798.


• Roy, R., & Shijin, S., Dissecting anomalies and dynamic human capital: The global evidence, Borsa Istanbul Review, 18(1), 1e32, 2018. https://doi. org/10.1016/j.bir.2017.08.005.


• Sharpe, W.F., Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk, Journal of Finance, Vol. 19:3, PP 425-442, 1964. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x.


• Stock, J.H., Watson, M.W., Forecasting using principal components from a large number of predictors, Journal of the American Statistical Association 97, 1167–1179, 2002.


• Takács, G., Gas Lift Manual, Pennwell Books, 2005.


• Tsay, R.S., Analysis of Financial Time Series, third ed. Wiley. Wasserman, L., 2006, All of Nonparametric Statistics. Springer. Yen, Y.-M., 2016. Sparse weighted-norm minimum VaRiance portfolios. Review of Finance 20, 1259–1287, 2010.