We are pleased to announce the publication of Rebellion Research's piece on Dynamic Time Warping by the Journal of Financial Data Science!
Dynamic Time Warping: S&P 500 Sector ETF Pattern Matching Trading Strategy
Written by Alexander Fleiss, Che Liu, Gihyen Eom, Serena Yu and Wo Zhang
The Journal of Financial Data Science Winter 2021, jfds.2021.1.055;
The authors examine an optimized Markowitz efficient portfolio by applying a quantitative trading strategy to the S&P 500 sector exchanged-traded funds (ETFs). First, they implement a pattern-matching trading system, which extracts the underlying trends based on dynamic time warping. They then estimate a decision-making dictionary from the windows of ETF prices to identify the entry points for trading. Finally, they construct a Markowitz efficient portfolio on the ETFs’ net asset values on the validation set. The results demonstrate that the strategy can be modified to improve performance.