Comparison of Stock Selection Methods: An Empirical Research On The Borsa Istanbul

  • Ali Sezin Ozdemir Faculty of Management, Institute of Social Sciences, Istanbul Technical University, Turkey
  • Kaya Tokmakcioglu Faculty of Management, Department of Management Engineering, Istanbul Technical University, Turkey
Keywords: Stock selection, portfolio diversification, Borsa Istanbul, artificial neural network, second order stochastic dominance

Abstract

This paper compares the performances of stock selection methods developed by artificial neural network (ANN), second order stochastic dominance (SSD), and Markowitz portfolio optimization by generating annual portfolios whose stocks are selected from several types of indexes traded in the Borsa Istanbul. Daily returns in SSD and Markowitz, and annual ratios in ANN models, are taken as inputs, with the following annual returns as outputs. By the perspective of stock selection literature, this study carries unique value for including comparisons of these methods with the purpose of generating portfolios with higher returns. Thus, two questions emerge: "Are these methods able to overcome losses during financial crises and bear or bull periods, and can they provide positive alpha?" Results indicate that average returns of portfolios generated by ANN are relatively higher than SSD and Markowitz, but all three models provide positive alpha over indexes. However, none of the models could overcome negative returns during economic crises.

References

Bagci, S. A. (2014). Kasım 2000 ve Şubat 2001 Ekonomik Krizlerinin Dış Ticarete Etkileri [Effects of November 2000 and February 2001 economic crises on foreign trade of Turkey]. Aksaray Üniversitesi İktisadi ve İdariBilimler Fakültesi Dergisi, 8(3), 46-54.

Berleant, D., Andrieu, L., Argaud, J. P., Barjon, F., Cheong, M. P., Dancre, M., Sheble, G., & Teoh, C. C. (2008). Portfolio management under epistemic uncertainty using stochastic dominance and information-gap theory. International Journal of Approximate Reasoning, 49(1), 101-116. https://doi.org/10.1016/j.ijar.2007.07.011

Chang, Y. H., & Lee, M. S. (2017). Incorporating Markov decision process on genetic algorithms to formulate trading strategies for stock markets. Applied Soft Computing, 52(C), 1143-1153. https://doi.org/10.1016/j.asoc.2016.09.016

Chen, B., Zhong, J., & Chen, Y. (2020). A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange. Expert Systems with Applications, 145, 113104. https://doi.org/10.1016/j.eswa.2019.113104

Cuthbertson, K., & Nitzche, D. (2013). Performance, stock selection and market timing of the German equity mutual fund industry. Journal of Empirical Finance, 21(C), 86-101. https://doi.org/10.1016/j.jempfin.2012.12.002

Eakins, S. G., & Stansell, S. R. (2003). Can value-based stock selection criteria yield superior risk-adjusted returns: An application of neural networks. International Review of Financial Analysis, 12(1), 83-97. https://doi.org/10.1016/S1057-5219(02)00124-2

Gillam, R., Guerard, J., & Cahan, R. (2015). News volume information: Beyond earnings forecasting in a global stock selection model. International Journal of Forecasting, 31(2), 575-581. https://doi.org/10.1016/j.ijforecast.2014.12.007

Goumatianos, N., Christou, I., & Lindgren, P. (2013). Stock selection system: Building long/short portfolios using intraday patterns. Procedia Economics and Finance, 5, 298-307. https://doi.org/10.1016/S2212-5671(13)00036-1

Guerard, J. B. J., Markowitz, H., & Xu, G. (2015). Earnings forecasting in a global stock selection model and efficient portfolio construction and management. International Journal of Forecasting, 31, 550-560. https://doi.org/10.1016/j.ijforecast.2014.10.003

Guran, C. B., & Tas, O. (2015). Making second order stochastic dominance inefficient mean variance portfolio efficient: Application in Turkish bist-30 index. Iktisat Isletme ve Finans Dergisi, 30(348), 69-94. https://doi.org/10.3848/iif.2015.348.4338

Hill, T., Marquez, L., O'Connor, M., & Remus, W. (1994). Artificial neural network models for forecasting and decision making. International Journal of Forecasting, 10(1), 5-15. https://doi.org/10.1016/0169-2070(94)90045-0

Horton, M. J. (2009). Stars, crows, and doji: The use of candlesticks in stock selection. The Quarterly Review of Economics and Finance, 49(2), 283-294. https://doi.org/10.1016/j.qref.2007.10.005

Ince, H. (2014). Short term stock selection with case-based reasoning technique. Applied Soft Computing, 22, 205-212. https://doi.org/10.1016/j.asoc.2014.05.017

Kopa, M., & Chovanec, P. (2008). A second-order stochastic dominance portfolio efficiency measure. Kybernetika, 44(3), 488-500.

Lee, W. S., Tzeng, G. H., Guan, J. L., Chien, K. T., & Huang, J. M. (2009). Combined MCDM techniques for exploring stock selection based on Gordon model. Expert Systems with Applications, 36(3), 6421-6430. https://doi.org/10.1016/j.eswa.2008.07.084

Liesiö, J., Xu, P., & Kuosmanen, T. (2020). Portfolio diversification based on stochastic dominance under incomplete probability information. European Journal of Operational Research, 286(2), 755-768. https://doi.org/10.1016/j.ejor.2020.03.042

Lucas, A., van Dijk, R., & Kloek, T. (2002). Stock selection, style rotation, and risk. Journal of Empirical Finance, 9(1), 1-34. https://doi.org/10.1016/S0927-5398(01)00043-3

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x

Mitchell, T. (1990). Machine Learning. McGraw-Hill. https://doi.org/10.1146/annurev.cs.04.060190.002221

O'Shaughnessy, J. (1997). What Works on Wall Street. McGraw-Hill.

Ogryczak, W. L., & Ruszczynski, A. (2002). Dual stochastic dominance and related mean-risk models. SIAM Journal of Optimization, 13(1), 60-78. https://doi.org/10.1137/S1052623400375075

Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465. https://doi.org/10.1016/S0169-2070(02)00058-4

Post, T., Karabatı, S., & Arvanitis, S. (2018). Portfolio optimization based on stochastic dominance and empirical likelihood. Journal of Econometrics, 206(1), 167-186. https://doi.org/10.1016/j.jeconom.2018.01.011

Quah, T.-S., & Srinivasan, B. (1999). Improving returns on stock investment through neural network selection. Expert Systems with Applications, 17(4), 295-301. https://doi.org/10.1016/S0957-4174(99)00041-X

Ratanabanchuen, R., & Saengchote, K. (2020). Institutional capital allocation and equity returns: Evidence from Thai mutual funds' holdings. Finance Research Letters, 32(C), 101085. https://doi.org/10.1016/j.frl.2018.12.033

Song, Q., Lui, A., & Yang, S. Y. (2017). Stock portfolio selection using learning-to-rank algorithms with news sentiment. Neurocomputing, 264(C), 20-28. https://doi.org/10.1016/j.neucom.2017.02.097

Sun, B., Li, H., An, P., & Wang, Z. (2020). Dynamic energy stock selection based on shareholders' coholding network. Physica A: Statistical Mechanics and its Applications, 542, 122243. https://doi.org/10.1016/j.physa.2019.122243

Suzuki, T., & Okhura, Y. (2016). Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets. Physica A: Statistical Mechanics and its Applications, 442(C), 50-66. https://doi.org/10.1016/j.physa.2015.08.042

Tas, O., Barijough, F. M., & Ugurlu, U. (2015). A test of second order stochastic dominance with different weighting methods: Evidence from BIST-30 and DJIA. Journal of Business, Economics and Finance, 4(4), 723-731. https://doi.org/10.17261/Pressacademia.2015414538

Tas, O., Ozdemir, A. S., & Tokmakcioglu, K. (2016). Portfolio analysis with second order stochastic dominance: An implementation on BIST-100 index. PressAcademia Procedia, 2(1), 10-18. https://doi.org/10.17261/Pressacademia.2016118623

Tiryaki, F., & Ahlatcioglu, M. (2005). Fuzzy stock selection using a new fuzzy ranking and weighting algorithm. Applied Mathematics and Computation, 170(1), 144-157. https://doi.org/10.1016/j.amc.2004.10.092

van der Hart, J., de Zwart, G., & van Dick, D. (2005). The success of stock selection strategies in emerging markets: Is it risk or behavioural bias? Emerging Markets Review, 6(3), 238-262. https://doi.org/10.1016/j.ememar.2005.05.002

van der Hart, J., Slagter, E., & van Dijk, D. (2003). Stock selection strategies in emerging markets. Journal of Empirical Finance, 10(1-2), 105-132. https://doi.org/10.1016/S0927-5398(02)00022-1

Xia, H., Min, X., & Deng, S. (2015). Effectiveness of earnings forecasts in efficient global portfolio construction. International Journal of Forecasting, 31(2), 568-574. https://doi.org/10.1016/j.ijforecast.2014.10.004

Yang, F., Chen, Z., Li, J., & Tang, L. (2019). A novel hybrid stock selection method with stock prediction. Applied Soft Computing, 80(2), 820-831. https://doi.org/10.1016/j.asoc.2019.03.028

Yildiz, B., & Yezegel, A. (2010). Fundamental analysis with artificial neural network. The International Journal of Business and Finance Research, 4(1), 149-158.

Published
2022-08-08
How to Cite
Ali Sezin Ozdemir, & Kaya Tokmakcioglu. (2022). Comparison of Stock Selection Methods: An Empirical Research On The Borsa Istanbul. International Journal of Business and Society, 23(2), 834-854. https://doi.org/10.33736/ijbs.4841.2022