Leveraging Vision-Language Models for Granular Market Change Prediction

TL;DR

This paper introduces a novel approach for stock market prediction by using vision-language models on image and byte-based representations of stock data, outperforming traditional time-series models.

Contribution

The work is the first to apply vision-language models to stock data using image and byte representations, demonstrating superior prediction performance.

Findings

Outperforms traditional deep learning baselines

Effective use of image and byte-based data representations

Significant improvement in stock prediction accuracy

Abstract

Predicting future direction of stock markets using the historical data has been a fundamental component in financial forecasting. This historical data contains the information of a stock in each specific time span, such as the opening, closing, lowest, and highest price. Leveraging this data, the future direction of the market is commonly predicted using various time-series models such as Long-Short Term Memory networks. This work proposes modeling and predicting market movements with a fundamentally new approach, namely by utilizing image and byte-based number representation of the stock data processed with the recently introduced Vision-Language models. We conduct a large set of experiments on the hourly stock data of the German share index and evaluate various architectures on stock price prediction using historical stock data. We conduct a comprehensive evaluation of the results with various metrics to accurately depict the actual performance of various approaches. Our evaluation results show that our novel approach based on representation of stock data as text (bytes) and image significantly outperforms strong deep learning-based baselines.