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Machine Learning Algorithms in Forecasting the Behavior of Financial Assets Prices

Student: Sosnov Denis

Supervisor: Svetlana A. Lapinova

Faculty: Faculty of Economics

Educational Programme: Economics (Bachelor)

Year of Graduation: 2019

The prediction of financial assets prices using either classification or regression models, is a challenge that has been growing in the recent years, despite the large number of publications of forecasting models for this task. Basically, the non-linear tendency of the series and the unexpected behavior of assets (compared to forecasts generated in studies of fundamental analysis or technical analysis) make this problem very hard to solve. In the first part of the work, a comparative analysis of machine learning methods, including AdaBoost, Bootstrap Aggregating, k – nearest neighbours, boosted regression trees and random forests, is presented. At the broadest level, it is expected that machine learning offers an improved description of financial assets prices behavior. The second part of the work includes empirical implementation of machine learning algorithms. We use an evaluation set based on stocks of ExxonMobil company as a training sample to train our models on and check the forecasting possibility on test sample. As a result of learning, a good prediction quality was obtained on the test sample using algorithms based on the Gradient Boosting and Random Forest method, while the rest of the algorithms showed poor results due to overfitting.

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