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Prediction of short-term stock price response to news

ФИО студента: Bogdan Pepelyaev

Руководитель: Sofya Kulikova

Кампус/факультет: Faculty of Economics, Management, and Business Informatics

Программа: Finance (Master)

Оценка: 7

Год защиты: 2020

This work is dedicated to the study of financial markets in terms of machine learning and models of portfolio’s theory. The work consists of several parts as the event-study, the main models of portfolio’s theory and machine learning methods, additionally, the research has Natural Language Processing model, which is an innovation in area's approaches. The role of event-study in the work is to select the news background. In the course of the work we will demonstrate standard approaches to predicting stock prices, which were investigated earlier, also we will show models that are descriptive and necessary for the final part of the study and compare them to each other. The literature review provides a detailed review of the models and their origin. The review of models begins with linearly dependent models and ends with models, which has a large space of hypotheses. We will create a new model in the results of the work, which includes all parts of the study and shows relevant results. The hypotheses of the importance of language models and the importance of news on stock behavior were confirmed.

Full text (added May 15, 2020)

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