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

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.

Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

“…The choice of the method was based on the advantages it confers compared to other procedures. Thus, it was observed that NNs contribute to an increase in the predictability of stock prices compared to conventional methods (Sahiner et al, 2021), capture information in a more comprehensive manner (Chang et al, 2022), have a high error tolerance (Mijwel, 2018), accurately process sets of homogeneous data (Castello & Resta, 2022;Tripathi et al, 2022), demonstrate a better long-term predictive power compared to statistical methods (Jan & Ayub, 2019), and are superior to regression models or those based on the approach technique (Ozdemir & Tokmakcioglu, 2022). Compared to linear models, NNs can provide solutions to complex relationships without being reprogrammed (Caliskan Cavdar & Aydin, 2020;Talwar et al, 2022).…”

“…The methods recently used in the study of the volatility of financial assets identified by the authors included logistic regression (Chang et al, 2022), stochastic dominance of the second order (Ozdemir & Tokmakcioglu, 2022), multivariate regression based on a deep neural network with backpropagation algorithm and Bayesian network (Naveed et al, 2023), traditional econometric models Neuro Fuzzy, ANFIS and CANFIS, EGARCH and VaR (Sahiner et al, 2021), Analytic Hierarchy Process (AHP) method, ANN based on FF5F model factors (Jan & Ayub, 2019), and ANN based on the multilayer perceptron model as a machine learning algorithm (Khansari et al, 2022).…”

“…The use of variables in ANN processing by various researchers includes historical values (Chang et al, 2022), stock returns (Jan & Ayub, 2019), stock selection methods and portfolio optimisation (Ozdemir & Tokmakcioglu, 2022), traded derivative contracts (TFDCs), exchange rate, daily COVID-19 cases and deaths (Naveed et al, 2023), bond markets of BRICS countries (Castello & Resta, 2022), and capital markets indices (Sahiner et al, 2021). ANN modelling is preferred over GARCH and EGARCH models as neural network prediction models demonstrate improved forecast accuracy (Sahiner et al, 2021).…”