Leveraging Vision-Language Models for Granular Market Change Prediction
- URL: http://arxiv.org/abs/2301.10166v1
- Date: Tue, 17 Jan 2023 19:37:19 GMT
- Title: Leveraging Vision-Language Models for Granular Market Change Prediction
- Authors: Christopher Wimmer, Navid Rekabsaz
- Abstract summary: 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.
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.
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.
- Score: 5.54780083433538
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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