面向金融科技的深度学习技术综述

Survey of Deep Learning Technologies for Financial Technology

近年来,深度学习技术被广泛应用于金融领域,并受到了国内外学术界和商业界的广泛关注。研究人员利用深度学习技术对各种金融数据进行发掘和分析,取得了大量的研究成果。深度学习在多个金融关键应用上的表现超过了传统的统计机器学习模型,包括金融市场预测、交易策略改进和金融文本信息挖掘等。为了更全面地把握深度学习技术在金融领域中的研究和应用趋势,促进它们之间的深层次融合和发展,着重梳理了近年来深度学习技术在金融科技研究中的发展脉络及前沿动态,分析和总结了深度学习模型在金融领域的主要应用和最新算法。根据金融领域中的具体应用场景将现有的深度学习金融研究进行详细分类,分析并总结各个领域的最新研究,并展望了金融科技领域未来的研究热点、技术难点和发展趋势等。

In recent years,deep learning techniques have been widely applied in addressing various problems in financial technology(Fintech)and have attracted increasing attention from both academia and business.Researchers utilize deep learning techniques for mining and analyzing financial data while finding the economic patterns behind tremendous data.Deep learning outperforms traditional statistical machine learning models in a range of crucial financial applications,including market movement prediction,trading strategy improvement,financial text processing,etc.To facilitate the development of Fintech and the deployment of new deep learning techniques,this paper provides a comprehensive survey of the deep learning-based Fintech studies published in recent years.Our survey focuses on the most recent advances in Fintech and provides a roadmap of financial problems as well as corresponding solutions.To this end,we investigate the widely used methodologies in finance data mining and summarize the popular deep models in Fintech data learning.Besides,we propose a taxonomy that categorizes existing Fintech research into ten well-studied applications in the literature.Subsequently,we systematically review the state-of-the-art deep learning methods and provide insights on the improvement for future endeavors.Finally,the pros and cons of existing research are summarized,followed by outlining the trend,open challenges,and opportunities in the Fintech research community.