Application of machine learning in algorithmic investment strategies on global stock markets
Study on machine learning-based algorithmic investment strategies using technical indicators across global and CEE stock indices.
What it examines
This study investigates the profitability of machine learning-based quantitative investment strategies using technical analysis indicators. It aims to compare these strategies with the buy-and-hold benchmark across various global and CEE stock indices from 2002 to 2023.
What it concludes
The research suggests that machine learning-based strategies can significantly enhance investment performance. Potential applications include automated trading systems for hedge funds and other financial institutions. Future research could explore other asset classes and machine learning models.
Evidence objects
The research suggests that machine learning-based strategies can significantly enhance investment performance. Potential applications include automated trading systems for hedge funds and other financial institutions. Future research could explore other asset classes and machine learning models.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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.
Source row: 211 · abstract type: unknown