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Stock Return Prediction using Transfer Learning on Textual Data

Authors:


Eduardo Peynetti

,

Rohit Beri

,

Jessica Wijaya

,

Stuart Neilson

This article was produced as part of the final project for



Harvard’s AC295 Fall 2020



course.

Introduction

Transfer Learning, combined with open-sourced state-of-the-art (SOTA) neural network models has democratized and hastened the adoption of machine learning in a variety of fields ranging from medical science to astronomy. In this article, we explore the applicability of SOTA Natural Language Processing models for sentiment analysis in the financial domain (news, press releases, 10-Ks) to build profitable trading strategies.

We explore three different models, Loughran-McDonald, BERT, and Fin-BERT. We fine-tune Fin-BERT and take a look…