Deep Learning techniques for stock market forecasting: Recent trends and challenges

Manali Patel, K. Jariwala, C. Chattopadhyay
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Abstract

Stock market forecasting has been a very intensive area of research in recent years due to the highly uncertain and volatile nature of stock data which makes this task challenging. By accurately predicting a particular stock's price investors can gain maximum profit out of their investment. With the great success of Deep Learning methods in various domains, it has attracted the research community to apply these models for financial domain also. These DL methods have been proven to achieve better accuracy and predictions compared to econometric and traditional ML methods. This work reviews recent papers according to various Deep Learning models which included: Artificial Neural Networks, Convolution Neural Networks, Sequence to Sequence models, Generative Adversarial Networks, Graph Neural Networks and Transformers applied for stock market forecasting. Furthermore this work also reviews datasets, features, evaluation parameters and results of various methods. From the analysis done on various DL models we found that Graph Neural Networks and Transformer models have potential to interpret dynamic and non-linear patterns of financial time series data with greater accuracy. In addition to this, correlation among various stock indices and investors sentiment along with historical data has great influence on the prediction accuracy. We also identified the benchmark datasets for stock market forecasting based on market capitalization value of an economy. The aim of this paper is to provide insight into most recent work done in the finance domain and identify future directions for more accurate predictions.
股票市场预测的深度学习技术:最近的趋势和挑战
近年来,由于股票数据的高度不确定性和波动性,股票市场预测一直是一个非常密集的研究领域,这使得这项任务具有挑战性。通过准确预测某只股票的价格,投资者可以从他们的投资中获得最大的利润。随着深度学习方法在各个领域的巨大成功,它也吸引了研究界将这些模型应用于金融领域。与计量经济学和传统ML方法相比,这些DL方法已被证明具有更好的准确性和预测能力。这项工作根据各种深度学习模型回顾了最近的论文,包括:人工神经网络,卷积神经网络,序列到序列模型,生成对抗网络,图神经网络和变压器应用于股票市场预测。此外,本文还对各种方法的数据集、特征、评价参数和结果进行了综述。从对各种深度学习模型的分析中,我们发现图神经网络和变压器模型有潜力以更高的精度解释金融时间序列数据的动态和非线性模式。除此之外,各种股票指数和投资者情绪以及历史数据之间的相关性对预测的准确性有很大的影响。我们还确定了基于经济体市值的股票市场预测的基准数据集。本文的目的是提供对金融领域最新工作的见解,并为更准确的预测确定未来的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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