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Predicting stock price movements from financial news using deep neural networks
Kramoliš, Richard ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Financial media are an important source of information and many articles about companies and stocks are released every day. This thesis assesses the informa- tion value of the articles and utilizes these articles for the stock price move- ment prediction task. For this purpose, models with transformer architecture are used, specifically Bidirectional Encoder Representations from Transform- ers. These models are able to process the text data and create the contextual representation of the text sequence. After adding the classification layer, the models are applied for the stock price movement predictions. The thesis evalu- ates multiple models including different techniques and parameters to find the best performing model. It focuses on two data filters that are expected to de- crease the noise in the data. Moreover, it introduces a new method to recognize the company of interest. As a result of the hyperparameter optimization, the final model is constructed. JEL Classification C45, C51, C52, C53, G11, G14, G17 Keywords BERT, Transformer, Financial Articles, Stock Trading Title Predicting stock price movements from financial news using deep neural networks
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