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.
Abstract (translated)
使用历史数据预测股票市场的未来方向已经成为金融预测的一个基本组成部分。历史数据包含了每个特定时间范围内的股票的信息和价格,例如开市、收盘价、最低和最高价格。利用这些数据,通常使用各种时间序列模型如LSTM网络来预测市场的未来方向。本工作提出了一种根本性的新的方法来建模和预测市场运动,即使用最近引入的视觉语言模型处理的股票数据的图像和字节表示。我们在德国股票指数的每日股票数据上进行了大规模实验,并使用历史股票数据评估了各种架构对股票价格预测的性能。我们使用各种指标进行全面评估,以准确描述各种方法的实际表现。我们的评估结果显示,我们基于股票数据文本(字节)和图像的表示方法 significantly outperforms strong deep learning-based baselines.
URL
https://arxiv.org/abs/2301.10166
