The effect of financial news on bist stock prices: A machine learning approach

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümü, Türkiye

Tezin Onay Tarihi: 2018

Öğrenci: MEDET KANMAZ

Danışman: SERKAN KÜÇÜKŞENEL

Özet:

This thesis examines the relationship between the price data of companies in different sectors in the Borsa Istanbul (BIST) stock exchange and the verbal data revealed in the financial news related to these companies. In this work, sentiment analysis, natural language processing and the effect of financial news on individual stock performances are studied with a simple and novel method. Sentiment analysis is created by automatically labelling the news for companies publicly traded in BIST as positive or negative on the basis of the daily performance of stocks with different methods in machine learning. These algorithms determine the polarity in financial news with an accuracy of around 70%. As a result of this study, it was seen that positive or negative news had a positive / negative effect on the related stock prices. Whether the outcome of this algorithm provides incentives to make a profit in the market or not is also questioned. On the other hand, it is shown that it is hard to gain profit from this public information unless there is insider information.