Assessment of the applicability of large language models for quantitative stock price prediction

Abstract

In accordance with the findings presented in [34], this study examines the applicability of Machine Learning (ML) models and training strategies from the Natural Language Processing (NLP) domain in addressing time series problems, emphasizing the structural and operational aspects of these models and strategies. Recognizing the structural congruence within the data, we opt for Stock Price Prediction (SPP) as the designated domain to assess the transferability of NLP models and strategies. Building upon initial positive outcomes derived from quantitative SPP models in our ongoing research endeavors, we provide a rationale for exploring a range of additional methods and conducting subsequent research experiments. The outlined research aims to elucidate the efficacy of leveraging NLP models and techniques for addressing time series problems exemplified as SPP.