RT Journal Article
SR Electronic
T1 Derivation of a Dynamic Market Risk Signal Using Kernel PCA and Machine Learning
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP jfds.2020.1.037
DO 10.3905/jfds.2020.1.037
A1 Alireza Yazdani
YR 2020
UL https://pm-research.com/content/early/2020/06/23/jfds.2020.1.037.abstract
AB Kernel principal component analysis (PCA) is an extension of the conventional PCA method that employs a kernel transformation whereby hidden patterns in possibly multidimensional data may be detected and extracted more explicitly. In this article, the author applies the method of kernel PCA to a currency prediction case study and derives an aggregate market signal. It is observed that this signal has desirable information-compression properties and may be used as a predictive risk indicator in the return prediction models. Used alongside common drivers of exchange rates, a kernel PCA signal enhances in-sample and out-of-sample risk-adjusted performance across a range of machine learning strategies. In particular, the author observes that a kernel PCA signal remains robust and predictive during volatile market conditions. The kernel PCA signal may be used as a machine learning feature to inform and support data-driven risk management strategies. TOPICS: Big data/machine learning, derivativesKey Findings• This article develops a risk signal based on the method of kernel PCA.• Kernel PCA conveys desirable information on market dynamics.• Currency machine learning strategies benefit from this kernel PCA feature.