مدل قیمت‌گذاری دارایی‌های سرمایه‌‌ای با بازده فازی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشگاه آزاد اسلامی واحد نجف آباد

2 استادیار گروه حسابداری، دانشگاه آزاد اسلامی، واحد اصفهان (خوراسگان)

چکیده

در چند دهه اخیر مدل­های مختلفی در‌خصوص پیش­بینی بازده سهام ارائه شده است. در‌این‌میان، مدل قیمت‌گذاری دارایی­های سرمایه­ای (CAPM) موردتوجه بسیار قرار گرفت، اما انتقادهای بسیاری نیز به آن مطرح شد. یکی از انتقادهای واردشده بر این مدل نادیده‌گرفتن نوسان‌های درون‌دوره­ای و طول دوره در محاسبات است. بر‌این‌اساس، در پژوهش حاضر با به­کارگیری رگرسیون فازی تلاش شده است تا نوسان‌های درون‌دوره‌‌ای در این مدل درنظر گرفته شود. همچنین به بررسی و مقایسه پایداری بتا و دقت پیش­بینی بازده در مدل قیمت­گذاری دارایی‌های سرمایه­ای با‌استفاده‌از مدل رگرسیون فازی (FLS)، مدل رگرسیون حداقل مربعات معمولی (OLS) و گشتاورهای تعمیم‌یافته (GMM) پرداخته شده است. جامعه آماری عبارت است ازهمه شرکت­های پذیرفته‌شده در بورس اوراق بهادار تهران و نمونه پژوهش شامل 31 شرکت است که در دوره 1391 تا 1393دارای بیشترین حجم معاملات بوده­اند. نتایج پژوهش نشان می­دهد که خطای پیش­بینی بازده سهام در مدل فازی بیشتر از مدل حداقل مربعات معمولی و روش گشتاورهای تعمیم‌یافته است، اما خطای پیش­بینی بازده سهام در دو مدل رگرسیون حداقل مربعات معمولی و گشتاورهای تعمیم‌یافته تفاوت قابل‌ملاحظه‌ای با یکدیگر ندارند؛ بنابراین بتای کلاسیک همچنان بتای پایدارتری را نسبت به بتای فازی ارائه کرده و بدین‌ترتیب پیش­بینی بهتری از بازده سهام ارائه می­دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Capital Assets Pricing Model with Fuzzy Return

نویسندگان [English]

  • Leila Vahabi 1
  • afsaneh Soroushyar 2
1 Najaf Abad Branch, Islamic Azad University Najaf Abad Branch, Islamic Azad University
2 Department of Accounting, Isfahan (Khorasgan) Branch, Islamic Azad University
چکیده [English]

During the last decades several models have provided for predicting stock returns. The Capital Asset Pricing Model (CAPM), has attracted much attention, but also raised to it a lot of criticism. One of the criticisms is mentioned to this model is ignoring the intra-period volatility, structural breaks and duration of the calculations. Therefore, this study by using fuzzy regression has been tried to intra-period volatility to be included in this model. Then the stability of beta and accurately of the return prediction on the Capital Asset Pricing Model by using of Fuzzy logic system (FLS), Ordinary Least Squares regression model (OLS) and Generalized Method of Moments (GMM) are compared. The population of research is all companies listed in Tehran Stock Exchange and the research sample is consisted of 31 companies that in the period of 2011-2014, have been with the highest turnover. The results of research shows that the prediction error of stock returns in fuzzy model is more than OLS and GMM model, but the prediction error of stock returns in both of ordinary least squares regression model and Generalized method of moments doesn’t have any significant difference with each other. So the classical beta still has offered the more stable beta than fuzzy beta and thus is offering a better prediction of stock returns.

کلیدواژه‌ها [English]

  • Capital Assets Pricing Model- Fuzzy Least Squares- Beta- Market Returns- Stock Returns
  1. Aït-Sahalia, Y., Mykland, P. A. & Zhang, L. (2011). Ultra high frequency volatility estimation with dependent microstructure noise. Journal of Econometrics, 160, 190-203.
  2. Avadhanam, P. V., Mamidi, S. & Mishra, R. K. (2014). Empirical testing of CAPM for Central Public Sector Enterprises in India. Journal of Institute of Public Enterprise, 37, 50-64.
  3. Black, F., Jensen, M. & Scholes, M. (1972). Capital asset pricing model: some empirical tests. Studies in the Theory of Capital Markets. 1-54.
  4. Blume, M. E. (1971). On the assessment of risk. The Journal of Finance, 26 (1), 1-10.
  5. Blume, M. E. (1975). Beta and the regression tendencies. The Journal of Finance, 30(1), 785-795.
  6. Brailsford, T. J. & Josev, T. (1997). The impact of return interval on the estimation of systematic risk. Pacific-Basin Finance Journal, 5, 353-372.
  7. Brailsford, T. J. & Faff, R. W. (1997). Testing the conditional CAPM and the effect of intervaling: a note. Pacific-Basin Finance Journal, 5, 527-537.
  8. Campbell, J. Y. (2000). Asset pricing at the millennium. Journal of Finance, 55, 1515-1567.
  9. Caporale, T. (2012). Time varying CAPM beta and banking sector risk. Economic Letter, 115, 293-295.
  10. Cohen, K., Hawawini, G., Mayer, S., Schwartz, R. & Whitcomb, D. (1986). The microstructure of securities markets. Prentice-Hall, Sydney.
  11. Cragg, J. G. (1994). Making good inferences from bad data. Canadian Journal of Economics, 27, 776-800.
  12. Eslami, G. R., Abdoh Tabrizi, H., Mohmad, Sh. & Shams, Sh. (2009). A Survey of the time scale of capital asset pricing model (CAPM) by wavelet Transfarm. Journal of Accounting and Auditing Review, 16(5), 35-52 (In Persian).
  13. Fama, E. F. (1990). Stock returns, expected returns, and real activity. The Journal of Finance, 45(4), 1089-1108.
  14. Ferson, W. E. & Harvey, C. R. (1991). The variation of economic risk premiums. Journal of Political Economy, 99(2), 385-415.
  15. Garcia, R. & Ghysels, E. (1998). Structural change and asset pricing in emerging markets. Journal of International Money and Finance, 17, 455-473.
  16. Gençay, R., Selçuk, F. & Whitcher, B. (2005). Multiscale systematic risk. Journal of International Money and Finance, 24(1), 55-70.
  17. Ghanbari, A., Khezri, M. & Torky Samaei, R. (2010). Systematic Risk Estimation at Different Time Scales, by Using Wavelets Analysis an Application on Tehran Stock Exchange. Quarterly Journal of Quantitative Economics, 6(4), 29-50 (In Persian).
  18. Ghysels, E. (1998). On stable factor structures in the pricing of risk: Do time-varying betas help or hurt? Journal of Finance, 53(2), 549-573.
  19. Handa, P., Kothari, S. P. & Wasley, C. (1993). Sensitivity of multivariate tests of the capital asset pricing to the return interval measurement. Journal of Finance, 48, 15-43.
  20. Harvey, C. R. (1989). Time-varying conditional covariances in tests of asset pricing models. Journal of Financial Economics, 24, 289-317.
  21. Kim, D. (1993). The extent of nonstationarity of beta. Review of Quantitative Finance and Accounting, 3, 241-254.
  22. Khodaei, E. (2008). Fuzzy linear regression and its applications in social science research, Journal of Iranian Social Studies, 3(4), 1-14 (In Persian).
  23. Koissi, M-C. & Shapiro, A.F. (2006). Fuzzy formulation of the Lee-Carter model for mortality forecasting. Mathematics and Economics, 39, 287-309.
  24. Kwakernaak, H., (1978). Fuzzy random variables definitions and theorems. Information Sciences, 15, 1-29.
  25. Levhari, D. & Levy, H. (1977). The capital asset pricing model and the investment horizon. Review of Economics and Statistics, 59, 92-104.
  26. Lintner, J. (1965). The valuation of risky assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47, 13-37.
  27. Mazzola, P. & Gerace, D. (2015). A comparison between a dynamic and static approach to asset management using CAPM models on the Australian Securities Market. Australasian Accounting, Business and Finance Journal, 9(2), 43-58.
  28. Mohamadi, SH., Abbasi Nejad, H. & Mirsanei, S. R. (2007). Investigating Beta Estimation Methods in Tehran Stock Exchange. Journal of Accounting and Auditing Review, 14(1), 3-38 (In Persian).
  29. Moussa, A. M., J.S. Kamdem, A. F. Shapiro, & M. Terraza. (2013). CAPM with fuzzy returns and hypothesis testing. Journal of Mathematics and Economics, 55, 40-57.
  30. Novak, J. (2015). Systematic risk changes, negative realized excess returns and time-Varying CAPM beta. Czech Journal of Economics and Finance, 65(2), 167-190.
  31. Puri, M. L. & D. A. Ralescu. (1986). Fuzzy random variables. Journal of mathematical analysis and applications, 114, 409-422.
  32. Rahnama Roodposhti, F. & Moradi, M. R. (2006). Analyzing the Arbitration Pricing Mechanism (APT) Using Factor Analysis in Tehran Stock Exchange. Financial Research Journal, 7(1), 65-91 (In Persian).
  33. Ramazanpoor, E., Gholizadeh, M.H. & Kalantary, A. (2018). Comparative study of the instability and dynamics of systematic risk for Tehran Stock Exchange and a selected group of emerging stock markets. Iranian Journal of Economic Research, 18(56), 157-186 (In Persian).
  34. Rostami, M. R., Moghaddas Bayat, M. & Maghami, R. (2016). Analyzing idisyncratic risk and returns relationship based on quantile regression and Bayesian approach. Financial Management Perspective, 16, 135-151 (In Persian).
  35. Sadefo Kamdem, J., A. Moussa, & M. Terraza. (2012). Fuzzy risk adjusted performance measures: Application to hedge funds. Insurance: Mathematics and Economics, 51, 702-712.
  36. Sharpe, W., (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. Journal of Finance, 19, 425-442.
  37. Smimou, K., C. R. Bector, & G. Jacoby. (2008). Portfolio selection subject to experts’ judgments. International Review of Financial Analysis. 17, 1036-1054.
  38. Taghian Dinani, Z. & Farid, D. (2016). The relationship between excess returns of momentum strategy and systematic risk in Tehran Stock Exchange. Financial Management Perspective, 16, 9-30.
  39. Taheri, S. M. & Mashinchi, M. (2008). Introduction to fuzzy probability and statistics. Kerman, Shahid Bahonar University of Kerman Publications (In Persian).
  40. Tanaka, H. & P. Guo. (1999). Portfolio selection based on upper and lower exponential possibility distributions. European Journal of Operational Research, 114, 115-126.
  41. Tehrani, R. & Chitsazan, H. (2004). Investigating the Trend of Systematic Risk and Beta Stability of Companies Listed in Tehran Stock Exchange. Financial Research Journal, 6(2), 27-37 (In Persian).
  42. Tehrani, R. & Tabatabaei, J. (2008). Evaluating the stability of systematic risk in Tehran stock exchange. Financial Research Journal, 9(2), 13-23 (In Persian).