Unsupervised Text Mining Techniques for Forecasting Crude Oil

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Creator: 

Hazen, Jacob

Date: 

2022

Abstract: 

While it has been shown that news articles can influence the rationality of investors' decisions, the effect that news may have on commodity prices such as crude oil is uncertain. I explored Natural Language Processing (NLP) techniques to extract textual features from news articles and then constructed a "horse-race" among economic and tree-based machine learning methods to forecast weekly crude oil prices. I obtained two types of textual features, latent topics and sentiment probabilities, using two state-of-the-art NLP models: Latent Dirichlet Allocation (LDA) and a pre-trained version of Bidirectional Encoder Representations from Transformers (BERT) on a financial corpus. This paper introduced a novel forecasting strategy to calculate the out-of-sample (OoS) performance metrics of competing models. The evidence I found shows that textual features can improve forecasts of oil prices, however, textual features from news on their own are not sufficient for high forecasting accuracy.

Subject: 

Finance
Artificial Intelligence
Computer Science

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Arts: 
M.A.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Economics

Parent Collection: 

Theses and Dissertations

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