Application of Machine Learning algorithms for AIX Index movements predictions

Abstract

There have been many efforts to predict stock market trends by numerous scholars across a wide range of areas utilizing different methods and applications. This thesis examines in detail how advanced machine learning techniques can be used to predict the indices of the Astana International Exchange (AIX). As a relatively new but highly impactful stock exchange in Central Asia, the AIX is important in the regional financial landscape. The accurate forecasting of AIX index trends is critical for stakeholders such as investors, fund managers, and policy makers, as it allows them to take informed decisions and obtain better understanding of market fluctuations. The paper evaluates the performance of different machine learning models in projecting the trend performance of AIX indices and investigates the consequences of these predictions for investment strategies and wider market movements. For the learning and validation of machine learning models, the research uses historical data on AIX index share prices for the period of 2023 year. The study shows that all used models may give sufficient outcomes in forecasting the dynamic of AIX index. The findings demonstrate that in comparison with other models, ARIMA can become useful instrument for forecasting price dynamic on AIX and can be used by investors and traders to take well-grounded conclusions in their investment strategies