Nonparametric model test by using adaptive group LASSO method to identify the effective features in predicting the expected returns of stock portfolios

Subject Areas : Journal of Investment Knowledge

Raheleh ossadat  Mortazavi ¹ (PhD student of financial management Department of Management, Kish International Branch, Islamic Azad University, Kish Island, Iran.)
Hamidreza  Vakilifard ² (Faculty member of Azad University)
Ghodratallah  Talebnia ³ (A faculty member of Azad University)
seyedeh mahboobeh  jafari ⁴ (A faculty member of Azad University)

Received: 2019-03-07 Accepted : 2019-07-17 Published : 2022-12-22

Keywords: Effective features, Adaptive group LASSO, Expected Return, Nonparametric method,

Abstract :

In this paper, a new nonparametric method is applied using the adaptive group LASSO for selecting features and studying which of the features provide incremental information for predicting the cross-sectional expected return. Out of many features mentioned in previous studies, the effect of 36 characteristics on the expected returns of stock portfolios in Tehran Stock Exchange (1396-1387) was investigated.The result of this study shows that only three to five features provide incremental information to predict the expected return on stock portfolios. Therefore, only the return characteristics of 2 to 1 month before the forecast, total fluctuations, beta, maximum daily returns, and the ratio of price to the highest price have the power to predict the expected return on stock portfolios. The rest of the studied features do not have the power to predict expected returns.

References:

• امیری، مقصود؛ شریعت پناهی، مجید و محمدهادی بناکار (1389)" انتخاب سبد سهام بهینه با استفاده ازتصمیم گیری چندمعیاره"، فصلنامه بورس اوراق بهادار، شماره 11، صص 24-5. 


• 2- باقرزاده، سعید. (1384). عوامل مؤثر بر بازده سهام در بورس اوراق بهادار تهران، تحقیقات مالی، 19، 25-64.


• 3-بشکوه و افشاری (1391) "انتخاب نمونه کارائی مطلوب سرمایه گذاری در بازار سهام با استفاده از روش ترکیبی تحلیل تجزیه و تحلیل سلسله مراتبی(AHP) و تجزیه و تحلیل تئوری خاکستری (GRA) "، مجله علمی و کاربردی تحقیقات علمی، جلد. 2، شماره 11، صص . 11218-11225


• 4- دستگیر، محسن و ظفری، فاطمه(1388)،"نقش اطلاعات حسابداری در پیش بینی بازده سهام" مجله بورس، 85، 48-55.


• Ang, A., R. J. Hodrick, Y. Xing, and X. Zhang (2006). The cross-section of volatility and expected returns. The Journal of Finance 61 (1), 259-


• Balakrishnan, K., E. Bartov, and L. Faurel (2010). Post loss/profit announcement drift.Journal of Accounting and Economics 50 (1), 20-41.


• Ball, R., J. Gerakos, J. T. Linnainmaa, and V. V. Nikolaev (2015). Deating pro_tability.Journal of Financial Economics 117 (2), 225-


• Bali, T. G., N. Cakici, and R. F. Whitelaw (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics 99 (2), 427 -


• Bandyopadhyay, S. P., A. G. Huang, and T. S. Wirjanto (2010). The accrual volatility anomaly. Unpublished Manuscript, University of Waterloo.


• Basu, S. (1983). The relationship between earnings' yield, market value and return for NYSE common stocks: Further evidence. Journal of Financial Economics 12 (1), 129-


• Bhandari, L. C. (1988). Debt/equity ratio and expected common stock returns: Empirical evidence. The Journal of Finance 43 (2), 507-


• Chordia, T., A. Subrahmanyam, and V. R. Anshuman (2001). Trading activity and expected stock returns. Journal of Financial Economics 59 (1), 3{32.


• Cooper, M. J., H. Gulen, and M. J. Schill (2008). Asset growth and the cross-section of stock returns. The Journal of Finance 63 (4), 1609-


• De Bondt, W. F. and R. Thaler (1985). Does the stock market overreact? The Journal of Finance 40 (3), 793-


• Erdos, P., Ormos, M., & Zibriczky, D. (2011). Nonparametric and Semiparametric asset pricing. Journal of Economic Modelling, (28)3, 1150-1162.


• Fama, E. F. and K. R. French (1996). Multifactor explanations of asset pricing anomalies.Journal of Finance 51 (1), 55-


• Frazzini, A. and L. H. Pedersen (2014). Betting against beta. Journal of Financial Economics 111 (1), 1-


• Gandhi, P. and H. Lustig (2015). Size anomalies in US bank stock returns. The Journal of Finance 70 (2), 733-


• George, T. J. and C.-Y. Hwang (2004). The 52-week high and momentum investing. The Journal of Finance 59 (5), 2145-


• Gomez & Sanabria,(2014), Non-Parametric and Semi-Parametric Asset Pricing: An Application to the Colombian Stock Exchange, Economic Systems, Vol. 38, No. 2, 2014


• Green, J., J. R. Hand, and F. Zhang (2016). The characteristics that provide independent information about average US monthly stock returns. Review of Financial Studies


• Hahn, J. and H. Lee (2009). Financial constraints, debt capacity, and the cross-section of stock returns. The Journal of Finance 64 (2), 891-


• Harvey, C. R., Y. Liu, and H. Zhu (2016). ... and the cross-section of expected returns.Review of Financial Studies 29 (1), 5-68.


• Haugen, R. A. and N. L. Baker (1996). Commonality in determinants of expected stock returns. Journal of Financial Economics 41 (3), 401-


• Huang, J.-Z. and Z. Shi (2016). Determinants of bond risk premia. Unpublished Manuscript, Penn State University.


• Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of Finance 45 (3), 881-


• Lakonishok,J.,A.Shleifer, & R.W.Vishny (1994).Contrarian investment, extrapolation, and risk. The Journal of Finance 49 (5),1541-


• Lewellen, J. (2015). The cross section of expected stock returns. Critical Finance Review 4 (1), 1-


• Lyandres, E., L. Sun, and L. Zhang (2008). The new issues puzzle: Testing the investmentbased Review of Financial Studies 21 (6), 2825-2855.


• McMillan, D.G. (2117). Non-linear Forecasting of Stock Returns: Does Volume Help? International Journal of forecasting, 23 (1 ): 111–121.


• Novy-Marx, R. (2011). Operating leverage. Review of Finance 15 (1), 103-


• Novy-Marx, R. (2012). Is momentum really momentum? Journal of Financial Economics 103 (3), 429-


• Olsen, Dennis and Charles Mossman (2003). "Neural Network Forecast of Canadian Stock Returns" International Journal of Forecasting, No.19, PP: 453-465.


• Ou, J. A. and S. H. Penman (1989). Financial statement analysis and the prediction of stock returns. Journal of Accounting and Economics 11 (4), 295-


• Palazzo, B. (2012). Cash holdings, risk, and expected returns. Journal of Financial Economics 104 (1), 162-185


• Pontiff, J. and A. Woodgate (2008). Share issuance and cross-sectional returns. The Journal of Finance 63 (2), 921-


• Richardson, S. A., R. G. Sloan, M. T. Soliman, and I. Tuna (2005). Accrual reliability,earnings persistence and stock prices. Journal of Accounting and Economics 39 (3),437-


• Rosenberg, B., Reid, K., Lanstein, R., (1985). "Persuasive Evidence of Market Inefficiency. "Journal of Portfolio Management 11. pp. 9-17.


• Sloan, R. (1996). Do stock prices fully reect information in accruals and cash ows about future earnings? Accounting Review 71 (3), 289-


• Soliman, M. T. (2008). The use of DuPont analysis by market participants. The Accounting Review 83 (3), 823-


• Scholz, M., Nielsen, J. P., & Sperlich, S. (2012). Nonparametric Prediction of Stock Returns Guided by Prior Knowledge. University of Graz, Department of Economics