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Forecasting Directions of Metal Prices Changes with Respect to News Articles (Copper Price Case)

Student: Vsevolod Danielian

Supervisor: Ivan Stankevich

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Year of Graduation: 2020

In this paper, I have applied machine learning techniques to predict the price movement of copper futures based on relevant news. I used GloVe and Longformer models to get a vector representation of texts, which I used to make predictions using logistic regression and SVC. I also applied a bidirectional LSTM-based neural network to build predictions directly from the text. The results showed that the best predictor is the vector representation of the text, obtained using the Longformer and mapped into a space of lower dimensions by the principal component method.

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