电子市场信息令牌驱动的机器学习：行为金融大数据分析中的性能影响
Information Token Driven Machine Learning for Electronic Markets: Performance Effects in Behavioral Financial Big Data Analytics
摘要：
随着信息化进程的不断加快，金融服务业也在信息化的浪潮中不断发展。不仅数据量急剧增加，而且大数据现象的速度、复杂性和不可预测性加剧了金融服务研究人员和从业人员面临的挑战。数学、统计和技术被创造性地利用来创建分析解决方案。鉴于财务投标数据（FBD）的许多独特特征，有必要深入了解可用于创建FBD特定解决方案的策略和模型。行为金融学数据是FBD的一个子集，正在经历指数级增长，这为采用大数据分析方法研究行为金融学提供了前所未有的机会。本研究映射了机器学习（ML）技术和行为金融学类别，以探索使用ML技术解决FBD中行为方面的潜力。提出了这种方法的本体论可行性，并提出了本研究的主要目的-基于ML的行为模型可以有效地评估FBD的性能。一个简单的机器学习算法被成功地用于研究人工股票市场中的行为表现，以验证命题。关键词：信息;大数据;电子市场;分析;行为
英文摘要：
Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of big-data phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned- ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions. Keywords: Information; Big Data; Electronic Markets; Analytics; Behavior
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