A Backtesting Protocol in the Era of Machine Learning
| Autor: | Harry M. Markowitz, Robert D. Arnott, Campbell R. Harvey |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Disappointment
Information Systems and Management Computer science business.industry Strategy and Management Mathematical finance Machine learning computer.software_genre Data availability Investment management Computational Theory and Mathematics Artificial Intelligence medicine Business Management and Accounting (miscellaneous) Artificial intelligence Business and International Management medicine.symptom Set (psychology) business Capital market Biological sciences Protocol (object-oriented programming) computer Finance Information Systems |
| Zdroj: | The Journal of Financial Data Science. 1:64-74 |
| ISSN: | 2640-3943 |
| DOI: | 10.3905/jfds.2019.1.064 |
| Popis: | 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 ref lect the actions of people, who may be inf luenced 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. |
| Databáze: | OpenAIRE |
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