主讲人：沈建成
地点：太阳集团tcy8722松江校区1号学院楼240室
组织单位：太阳集团tcy8722
报告人简介：
沈建成，苏州大学商学院金融系特聘教授，博士生导师，瑞士苏黎世大学访问教授。曾分别在美国Regent University, Taylor University担任金融教职。研究领域为实证行为金融，理论资产定价，人工智能大数据，神经实验经济学。近年来发表了 20多篇国际高水平期刊论 文， 诸 如 Computer Networks, Journal of Banking and Finance，Journal of Financial Research, Journal of Futures Market, Journal of Real Estate Finance and Economics, Journal of Behavioral Finance, Information and Management 等。此外，担任了 Journal of Asset Management, Journal of Business Research, Scientific Reports, 瑞士国家自然基金的审稿人。
报告摘要：
Investor psychology provides an important avenue for modeling non-fundamental behaviors in financial analysis. Yet, whether market psychological information has a practical application in predicting asset returns is still under debate. Thus, a burgeoning number of machine learning algorithms have been developed to test the effectiveness of investor psychology in financial predictions. With all the merits of machine learning approach, the drawbacks are prediction biases, data overfitting issues and poor performance. To address these issues, we developed a DeepPsych system to harness the power of high frequency TRMI psychology data for market prediction. In a “hybridization–generalization–dual-channel-fusion” three-stage experiment, we evaluate each proposed module and the entire framework against the state-of-art machine learning benchmarks on investor psychology and trading data of the SPY (SP500 ETF). Results demonstrate that our deep learning framework can automatically identify features that are more effective than fundamental factors and support profitable trading.
投资者心理学为金融分析中的非基本行为建模提供了一条重要途径。然而，市场心理信息在预测资产回报方面是否具有实际应用仍存在争议。因此，已经开发了大量机器学习算法来测试投资者心理在财务预测中的有效性。尽管机器学习方法具有所有优点，但缺点是预测偏差、数据过度拟合问题和性能不佳。为了解决这些问题，我们开发了一个 DeepPsych 系统，利用高频 TRMI 心理数据的力量进行市场预测。在“混合-泛化-双通道融合”三阶段实验中，我们根据最先进的机器学习基准评估了每个提出的模块和整个框架，这些基准涉及投资者心理和SPY（SP500 ETF）的交易数据。结果表明，我们的深度学习框架可以自动识别特征数据并达成盈利的交易。
关键词：投资者心理，深度学习，高频交易系统
主持人：陶然