Investigating Predictability of Different "Forms of Return" in Tehran Stock Exchange: Some Rolling Regressions-based Evidence

Document Type: Research Paper

Authors

1 1. Accounting, Faculty of Social Sciences and Economics, AL Zahra University, Tehran, Iran 2. Accounting, Department of Management, Economic and Accounting, Payame Noor University, Tehran, Iran

2 Accounting, Faculty of Social Sciences and Economics, AL Zahra University, Tehran, Iran

3 Industrial Management/ Faculty of Management, University of Tehran, Tehran, Iran

Abstract

This paper has provided "out of sample" evidence of stock returns predictability in Tehran Stock Exchange. 68 qualified companies over the period from 2002 to 2015 were selected and for five different "forms of returns", five superior predictive models have been designed by applying "General to specific" approach of modeling technique. Then "out of sample" analysis, based on rolling regressions, has been used to test the validation of the designed models. The result showed that all designed models have sufficient "out of sample" validity and the aggregate returns have a higher predictability level.

Keywords

Main Subjects


Article Title [Persian]

بررسی پیشبینی پذیری اشکال مختلف بازده سهام در بازار سرمایه تهران: شواهدی مبتنی بر رگرسیونهای غلطان

Authors [Persian]

  • اعظم مهتدی 1
  • رضوان حجازی 2
  • سید علی حسینی 2
  • منصور مومنی 3
1 گروه حسابداری/ دانشکده علوم اجتماعی و اقتصادی، دانشگاه الزهرا(س)، تهران، ایران و گروه حسابداری، بخش مدیریت، اقتصاد و حسابداری/ دانشگاه پیام نور، تهران، ایران.
2 گروه حسابداری/ دانشکدة علوم اجتماعی و اقتصادی، دانشگاه الزهرا(س)، تهران، ایران.
3 گروه مدیریت صنعتی/ دانشکده مدیریت، دانشگاه تهران، تهران، ایران.
Abstract [Persian]

 این مقاله شواهدی "برون نمونهای" در خصوص پیشبینی پذیریِ اشکال مختلف بازده سهام در بورس اوراق بهادار تهران ارائه میکند. ۶۸ شرکت واجد شرایط برای دوره زمانی1381 تا 1394 انتخاب و مورد بررسی قرار گرفتهاند. ابتدا با به کارگیری رویکرد "کل به جزء" از تکنیک های مدل سازی، بهترین مدل پیشبینی کنندهِ 5 شکلِ مختلف از بازده، طراحی شده و سپس با بکارگیری تحلیل های مبتنی بر رگرسیون های غلطان، اعتبارِ "برون نمونهای" مدل های طراحی شده آزمون شده است. نتایج نشان میدهد تمامی مدلهای طراحی شده، اعتبار "برون نمونهای" کافی داشته و بازدههای انباشته قابلیت پیشبینی پذیری بالاتری دارند.

Keywords [Persian]

  • بازده
  • برون نمونهای
  • رگرسیونهای غلطان
  • رویکرد
  • کل به جزء
  1. Abbaszadeh, M. R., & AtashiGolestani, H. (2010). Accounting variables and stock return's forecast before and after the Iranian accounting standards. Knowledge and Development, 18(33), 1-28. (In Persian)
  2. Aflatuni, A. (2014). Statistical analysis in financial management and accounting researches by Eviews. Tehran: Terme Publication. (In Persian)
  3. Aflatuni, A. (2016). Statistical analysis in accounting and finance using STATA. Tehran: Terme Publication. (In Persian)
  4. Alimohammadi, A., Abbasi e Mehr, M. H., & Javahery, A.(2015). Prediction of stock return using financial ratios: A decision tree approach. Journal of Financial Management Strategy, 3(4), 124-146. (In Persian)
  5. Armstrong, J. S. (Ed.). (2001). Principles of forecasting: a handbook for researchers and practitioners (Vol. 30). Springer Science & Business Media.
  6. Bahrami, A., Shamsuddin, A., & Uylangco, K. (2016). Out‐of‐sample stock returns predictability in emerging markets. Accounting & Finance.
  7. Campbell, J. Y., & Thompson, S. B. (2007). Predicting excess stock returns "out of sample": Can anything beat the historical average? The Review of Financial Studies21(4), 1509-1531.
  8. Cooper, I., & Priestley, R. (2009). Time-varying risk premiums and the output gap. Review of Financial Studies, 22(7), 2801-2833.
  9. Dow, C. H. (1920). Scientific Stock Speculation. Magazine of Wall Street.
  10. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of financial economics, 33(1), 3-56.
  11. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116 (1), 1:22.
  12. Griffin, J. M. (1997). Determinants of the cross-section of expected stock returns in Japan. (Doctoral dissertation, The Ohio State University).
  13. Hargreaves, C. A., & Mani, C. K. (2015). The Selection of winning stocks using principal component analysis. American Journal of Marketing Research1(3), 183-188.
  14. Hartmann-Wendels, T., Miller, P., & Töws, E. (2014). Loss given default for leasing: Parametric and nonparametric estimations. Journal of Banking & Finance40, 364-375.
  15. Hendry, D. F., & Richard, J. F. (1982). On the formulation of empirical models in dynamic econometrics. Journal of Econometrics, 20(1), 3-33.
  16. Mashayekhi, B., Tahriri, A., Ganji, H., & Askari, M. R. (2010). The impact of macroeconomic variables on the relation between fundamental variables derived from financial statements and stock returns. Quarterly Journal of Securities Exchange, 3(12), 109-126. (In Persian)
  17. McCracken, M. W. (2007). Asymptotics for "out of sample" tests of Granger causality. Journal of Econometrics, 140(2), 719-752.
  18. Namazi, M., & Kasgary, H. (2007). Using a multi-factor model to explain stock returns of companies accepted in Tehran Stock Exchange. Journal of Accounting Advances, 26(1), 157-180. (In Persian)
  19. Ou, J. A., & Penman, S. H. (1989). Financial statement analysis and the prediction of stock returns. Journal of Accounting and Economics, 11(4), 295-329.
  20. Paye, Bradley S., and Allan Timmermann, (2006(. Instability of return prediction models. Journal of EmpiricalFinance, 13, 274-316.
  21. Rangvid, J. (2006). Output and expected returns. Journal of Financial Economics, 81(3), 595-624.
  22. Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. The Review of Financial Studies. 23(2), 821-862.
  23. Rapach, D. E., & Wohar, M. E. (2006). In-sample vs. out-of-sample tests of stock returns predictability in the context of data mining. Journal of Empirical Finance, 13(2), 231-247.
  24. Sajadi, H., Frazmand, H., & Alisufi, H. (2010). Investigating the relationship between macroeconomic variables and exchange price index in Tehran stock exchange. Accounting Research, 10(2), 123-150. (In Persian)
  25. Setayesh, M. H., & Kazemnejad, M. (2015). Investigating usefulness of ensemble regression and feature selection in predicting stock returns of companies listed on Tehran stock exchange. Journal of Financial Accounting and Auditing Research, 8(32), 1-28. (In Persian)
  26. Sheri, S. (2004). The Role of Fundamental Accounting Information in Predicting Stock Returns. (Doctoral dissertation), Allameh Tabatabaei University,Iran. Retrieved from: library.atu.ac.ir. (In Persian)
  27. Sorzano, C. O. S., Vargas, J., & Montano, A. P. (2014). A survey of dimensionality reduction techniques. arXiv preprint arXiv:1403.2877.
  28. Thomsen, M., Moller, S. V. (2010). Predictability of Long-Term Stock Returns (master of art dissertation, Department of Business Studies, AARHUS University, UK)
  29. Valizadeh, A. (2014). Explaining a Model for Predicting Stock Returns. (Doctoral dissertation), Alzahra University, Iran, Retrieved from http://simorgh.alzahra.ac.ir. (In Persian)
  30. Wang, Y., & Choi, I. C. (2013). Market index and stock price direction prediction using machine learning techniques: an empirical study on the KOSPI and HSI. arXiv preprint arXiv:1309.7119.
  31. Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21(4), 1455-1508.
  32. Westerlund, J., Narayan, P. K., & Zheng, X. (2015). Testing for stock return predictability in a large Chinese panel. Emerging Markets Review, 24, 81-100.
  33. Zhang, L. (2002). Essays on the Cross-Section of Returns. (Doctoral dissertation, University of Pennsylvania, USA).
  34. Zhou, Q., & Faff, R. (2017). The complementary role of cross-sectional and time-series information in forecasting stock returns. Australian Journal of Management42(1), 113-139.