Comparison of the Effectiveness of Econometric Models and Machine Learning Algorithms for Predicting Stock Market Dynamics

Student: Kiliba Svetlana

Faculty: Faculty of World Economy and International Affairs

Educational Programme: World Economy (Bachelor)

Year of Graduation: 2024

The task of forecasting stock market dynamics has always been relevant both for investors, seeking to form an optimal portfolio based on the most effective prediction strategy, and for financial regulators interested in timely identification of preconditions for financial crises. With the development of data science, a large number of machine learning algorithms have been developed that can be applied to the problem of stock market prediction instead of the conventional econometric methods. The work provides a theoretical analysis of the predictive capabilities of econometric methods and machine learning models and also compares their effectiveness using empirical data.

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