Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment ...Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.展开更多
优质电力园区是针对区域性高品质供电问题的一种有效解决方案,其中定制电力设备配置方案的确定是园区规划建设的重要内容。针对园区中动态电压恢复器(dynamic voltage restorer,DVR)的配置问题,提出一种考虑用户短时电压质量定量需求的...优质电力园区是针对区域性高品质供电问题的一种有效解决方案,其中定制电力设备配置方案的确定是园区规划建设的重要内容。针对园区中动态电压恢复器(dynamic voltage restorer,DVR)的配置问题,提出一种考虑用户短时电压质量定量需求的DVR优化配置策略。基于过程抗扰时间和物理参数变化曲线,将用户对过程运行状态需求映射为短时电压质量需求,实现用户需求量化;以DVR投资最少为目标,满足用户需求为约束,建立DVR优化配置模型,用Tabu搜索算法求解,得到优化配置方案。对典型园区的仿真结果表明,该方法所得配置方案经济、可行,能在满足用户短时电压质量定量需求的同时,保证DVR投资最少。展开更多
文摘Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.
文摘优质电力园区是针对区域性高品质供电问题的一种有效解决方案,其中定制电力设备配置方案的确定是园区规划建设的重要内容。针对园区中动态电压恢复器(dynamic voltage restorer,DVR)的配置问题,提出一种考虑用户短时电压质量定量需求的DVR优化配置策略。基于过程抗扰时间和物理参数变化曲线,将用户对过程运行状态需求映射为短时电压质量需求,实现用户需求量化;以DVR投资最少为目标,满足用户需求为约束,建立DVR优化配置模型,用Tabu搜索算法求解,得到优化配置方案。对典型园区的仿真结果表明,该方法所得配置方案经济、可行,能在满足用户短时电压质量定量需求的同时,保证DVR投资最少。