Dynamic Time Warping: S&P 500 Sector ETF Pattern Matching Trading Strategy

January 7, 2021 · Academic Research

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:

https://doi.org/10.3905/jfds.2021.1.055

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.

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https://www.rebellionresearch.com/blog/dynamic-time-warping-s-p-500-sector-etf-pattern-matching-trading-strategy

Alexander Fleiss is the CEO of Rebellion Research, A Scientist, Teacher & Ai Researcher