Predicting the Russian Oil Market with News Analysis

Student: Varvara Furik

Educational Programme: Joint HSE-NES Undergraduate Program in Economics (Bachelor)

Year of Graduation: 2023

This study focuses on the analysis of implementation of novel text-based variables in models predicting stock returns of major Russian oil companies. Methodology is based on NLP algorithms that allow to compress information from numerous articles about the energy sector into quantitative variables and used them with financial and macroeconomic variables in a predictive model. The algorithm was tested on Interfax data from 2013-2022. The developed model predicts stock return of Russian oil companies in-sample well. However, it works much worse out-of-sample than simpler benchmark model, despite the fact that for some stocks a combination of complex models shows better results than a simple benchmark model. The main problem most likely is overfitting of a complex model due to a lack of data.

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