Big Data is an emerging paradigm in almost all industries. Finance big data (FBD) is becoming one of the most promising areas of management and governance in the financial sector. It is significantly changing business models in financial companies. Many researchers argue that Big Data is fueling the transformation of finance and business at-large in the ways that we cannot as yet assess. A new research area is evolving to study quantitative models and econometric approaches for financial studies that can bridge the gap between empirical finance research and data science. In this fascinating area, experts and scientists can propose novel finance business models by using the Big Data methods, present sophisticated methods for risk control with machine learning tools, provide visualization tools for financial markets analysis, create new finance sentiment indexes by mining public feelings from the massive textual data from social networks, and deploy the information-based tools in other creative ways. Due to the 4V characteristics of Big Data—volume (large data scale), velocity (real-time data streaming), variety (different data formats), and veracity (data uncertainty)—a long list of challenges for FBD management, analytics, and applications exists. These challenges include (1) to organize and manage FBD in effective and efficient ways; (2) to find novel business models from FBD analytics; (3) to handle traditional finance issues like high-frequency trading, sentiments, credit risk, financial analysis, risk management and regulation, and others, in creative Big Data–driven ways; (4) to integrate the variety of heterogeneous data from different sources; and (5) to ensure the security and safety of finance systems and to protect the individual privacy in view of the availability of Big Data. To meet these challenges, we need fundamental research on both data analytics technology and finance business. This special issue, “Finance Big Data: Management, Analysis, and Applications,” of International Journal of Electronic Commerce, is motivated by the need to meet the challenges of the fast development of finance big data. The papers brought together in this special issue highlight research efforts focused on the development of methods, tools, and techniques for the handling of various aspects of FBD from academia and industries. Viktor Manahov and Hanxiong Zhang, in “Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming,” develop an artificial futures market populated with high-frequency (HF) traders and institutional traders using Strongly Typed Genetic Programming trading algorithm. The authors simulate real-life futures trading at the millisecond time frame by applying Strongly Typed

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金融大数据：管理、分析和应用

大数据是几乎所有行业的新兴范式。金融大数据（FBD）正在成为金融领域最有前途的管理和治理领域之一。它正在显着改变金融公司的商业模式。许多研究人员认为，大数据正在以我们尚无法评估的方式推动金融和整体业务的转型。一个新的研究领域正在发展，以研究金融研究的定量模型和计量经济学方法，可以弥合实证金融研究和数据科学之间的差距。在这个迷人的领域，专家和科学家可以使用大数据方法提出新的金融业务模型，使用机器学习工具展示复杂的风险控制方法，为金融市场分析提供可视化工具，通过从社交网络的海量文本数据中挖掘公众情绪，创建新的金融情绪指数，并以其他创造性的方式部署信息化工具。由于大数据的 4V 特性——容量（大数据规模）、速度（实时数据流）、多样性（不同数据格式）和准确性（数据不确定性）——FBD 管理、分析、和应用程序存在。这些挑战包括 (1) 以有效和高效的方式组织和管理 FBD；(2) 从 FBD 分析中寻找新的商业模式；(3) 以创新的大数据驱动方式处理高频交易、情绪、信用风险、财务分析、风险管理和监管等传统金融问题；(4) 整合来自不同来源的各种异构数据；(5) 鉴于大数据的可用性，确保金融系统的安全和安全，保护个人隐私。为了应对这些挑战，我们需要对数据分析技术和金融业务进行基础研究。国际电子商务杂志特刊“金融大数据：管理、分析和应用”是为了迎接金融大数据快速发展的挑战的需要。本期特刊中汇集的论文重点研究了学术界和工业界处理 FBD 各个方面的方法、工具和技术的开发。Viktor Manahov 和 Hanxiong Zhang，在“使用高频交易数据预测金融市场：使用强类型遗传编程进行检查”中，”使用强类型遗传编程交易算法开发一个由高频 (HF) 交易员和机构交易员组成的人工期货市场。作者通过应用强类型在毫秒时间范围内模拟现实生活中的期货交易