Dynamic Time Warping: S&P 500 Sector ETF Pattern Matching Trading Strategy
Dynamic Time Warping: S&P 500 Sector ETF Pattern Matching Trading Strategy
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;
DOI:
Abstract
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.