A Backtesting Protocol in the Era of Machine Learning
A Backtesting Protocol in the Era of Machine Learning
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DOI：
10.3905/jfds.2019.1.064
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
R. Arnott;Campbell R. Harvey;H. Markowitz
中科院分区：
文献类型：
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作者：
R. Arnott;Campbell R. Harvey;H. Markowitz
机器学习提供了一套强大的工具，为投资管理带来了相当大的希望。与金融中的大多数定量应用一样，误用这些技术的危险可能会导致失望。一个关键的限制是数据的可获得性。机器学习的许多早期成功都起源于物理和生物科学，这些科学中有大量的数据可用。机器学习应用程序通常需要比金融领域更多的数据，这在长期投资中尤其令人担忧。因此，在应用工具之前选择正确的应用程序非常重要。此外，资本市场反映了人们的行为，而人们的行为可能会受到他人行为和过去研究结果的影响。在许多方面，影响机器学习的挑战只是研究人员在定量金融中一直面临的长期问题的延续。尽管投资者需要谨慎--事实上，比过去应用定量方法时更加谨慎--这些新工具在金融领域提供了许多潜在的应用。在这篇文章中，作者开发了一个研究协议，涉及机器学习技术的应用和一般的量化金融。
Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, the danger of misapplying these techniques can lead to disappointment. One crucial limitation involves data availability. Many of machine learning’s early successes originated in the physical and biological sciences, in which truly vast amounts of data are available. Machine learning applications often require far more data than are available in finance, which is of particular concern in longer-horizon investing. Hence, choosing the right applications before applying the tools is important. In addition, capital markets reflect the actions of people, who may be influenced by the actions of others and by the findings of past research. In many ways, the challenges that affect machine learning are merely a continuation of the long-standing issues researchers have always faced in quantitative finance. Although investors need to be cautious—indeed, more cautious than in past applications of quantitative methods—these new tools offer many potential applications in finance. In this article, the authors develop a research protocol that pertains both to the application of machine learning techniques and to quantitative finance in general.