Evaluation of Deep Learning Algorithms for KASE Index Prediction

Abstract

Security indexes are the primary instruments that are used to analyze the circumstances of the financial market. A considerable proportion of a country’s total wealth is held in the form of investments in its stock market. If future stock market patterns can be accurately predicted, it may be possible for investors to optimize the returns they receive on their investments. Yet, as a result of nonlinearity and nonstationarity, it is difficult to accurately forecast financial data. The purpose of this investigation is to determine how well predictive machine learning models perform in relation to the stock market. This analysis makes use of daily close price data for the KASE index on the Kazakhstan Stock Exchange from March 2018 all the way through March 2023. In order to make accurate predictions, we use a total of four different machine learning algorithms. According to the findings, the deep learning model fares far better than the competition in terms of the accuracy of its predictions. In addition, the neural network approach and the random forest method are considered to be the best, while the support vector regression method is placed second. Criteria for evaluation include mean absolute error (also known as MAE), mean square error (also known as MSE), and root mean square error (RMSE).