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Stock Market Prediction Based on Machine Learning Analysis

ФИО студента: Alexander Perepelkin-linhardt

Руководитель: Dmitriy Alexandrovich Kachalov

Кампус/факультет: International College of Economics and Finance

Программа: Double degree programme in Economics of the NRU HSE and the University of London (Bachelor)

Год защиты: 2019

Forecasting stock prices was always in the scope of interest for individual investors. Different analytics construct various complicated models in order to achieve better predictions for future securities prices, in aftermath, receiving extraordinary returns. Most of the prediction models in use were developed from statistic and technology science. These areas were rapidly developing in the recent decades, creating new models. Nowadays, the top topic in finances is Data science that uses machine learning analysis to make predictions about the future prices. This study is aimed to discover, whether machine learning analysis is capable to show sufficient result. In this paper, two popular machine learning techniques will be examined: Support Vector Regression and Gradient Boosting. These models will be implemented on the US and Indian stock market historical data.

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