发现掘金:金融应用中的数据挖掘

Dongsong Zhang, Lina Zhou
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引用次数: 254

摘要

随着经济全球化的加剧和信息技术的发展,金融数据以前所未有的速度产生和积累。因此,迫切需要自动化方法来有效和高效地利用大量财务数据,以支持公司和个人进行战略规划和投资决策。数据挖掘技术已被用于发现隐藏的模式,并预测金融市场的未来趋势和行为。通过数据挖掘获得的竞争优势包括增加收入、降低成本以及大大提高市场响应能力和意识。已经有大量的研究和实践集中在探索数据挖掘技术来解决金融问题。在本文中,我们从技术和应用的角度描述了金融应用背景下的数据挖掘。此外,我们比较了不同的数据挖掘技术,并讨论了具体金融应用中涉及的重要数据挖掘问题。最后,我们强调了该领域未来研究的一些挑战和趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovering golden nuggets: data mining in financial application
With the increase of economic globalization and evolution of information technology, financial data are being generated and accumulated at an unprecedented pace. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision-making. Data mining techniques have been used to uncover hidden patterns and predict future trends and behaviors in financial markets. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. In this paper, we describe data mining in the context of financial application from both technical and application perspectives. In addition, we compare different data mining techniques and discuss important data mining issues involved in specific financial applications. Finally, we highlight a number of challenges and trends for future research in this area.
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