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
