Application of machine learning in algorithmic investment strategies on global stock markets - Grudniewicz, Jan; Ślepaczuk, Robert (Research in international business and finance, 2023)
The research undertakes the subject of machine learning based algorithmic investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models, and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500, and selected CEE indices in the period between 2002-01–01 and 2023–03–31. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Sensitivity analysis was used to assess the quality of the estimation on independent subsets. The findings of the study showed that algorithmic strategies outperformed passive strategies in terms of risk-adjusted returns and that for the analyzed indices, Linear Support Vector Machine and Bayesian Generalized Linear Model were the best-performing models. The Linear Support Vector Machine was chosen as the model that, on average, produced the best results using a more thorough rank approach based on the outcomes for all examined models and indices. [Display omitted] ●Research undertakes the subject of algorithmic investment strategies (AIS) based on ML models such as NN, KNN, RT, RF, Naïve Bayes, GLM, and SVM.●Models were used to generate trading signals on WIG20, DAX, S&P500, and selected CEE indices for the last 20 y.●Quality of estimation was evaluated on independent subsets and with the use of sensitivity analysis.●Results indicated that AIS generated better risk-adjusted returns than benchmarks and that the best performing models were predominantly Linear Support Vector Machine and Bayesian GLM.●More comprehensive rank approach allowed to select Linear Support Vector Machine as the best one.