Prediction of Stock Index Movement Directions Using Machine Learning Methods: An Application on Developing Countries
Keywords:
Machine Learning, Classification Algorithms, Stock Index Prediction, Developing CountriesAbstract
The aim of this study is to compare the performances of the methods by predicting the movement directions of the stock market indices of developing countries with machine learning classification methods and to determine the best estimation method. For this purpose, the main stock market indices of E-7 countries, known as developing countries; Application was made on SSE (China), BOVESPA (Brazil), RTS (Russia), NIFTY 50 (India), IDX (Indonesia), IPC (Mexico) and BIST 100 (Turkey). k-nearest neighbors, decision trees, random forests, naive Bayes, support vector machines, artificial neural networks and logistic regression.methods, which are among the machine learning classification methods, were used to predict the movement directions of Stock Indices. In the analyses, the daily data for the period 01.01.2012-31.12.2021 and the technical indicators calculated based on the aforementioned data set were used as the data set. According to the results of the analysis, it has been determined that the best method for estimating the movement directions of the stock market indices of developing countries is logistic regression. Along with the logistic regression method, artificial neural networks and support vector machine methods were also determined to predict the direction of movement of all indices with an accuracy of over 70%.
It has also been determined that the best method, logistic regression, is not valid for all indices, in other words, it is not the method with the highest accuracy performance in all indices.
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