PT  - JOURNAL ARTICLE
AU  - Alexander Fleiss
AU  - Che Liu
AU  - Gihyen Eom
AU  - Serena Yu
AU  - Wo Zhang
TI  - Dynamic Time Warping: S&P 500 Sector ETF Pattern Matching Trading Strategy
AID  - 10.3905/jfds.2021.1.055
DP  - 2021 Jan 31
TA  - The Journal of Financial Data Science
PG  - 93--110
VI  - 3
IP  - 1
4099  - https://pm-research.com/content/3/1/93.short
4100  - https://pm-research.com/content/3/1/93.full
AB  - 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.TOPICS: Exchange-traded funds and applications, portfolio construction, statistical methodsKey Findings▪ The authors explore the applicability of dynamic time warping to financial time series in the context of quantitative trading strategies.▪ They construct a portfolio that minimizes the expected volatility by estimating the optimal weights for each component to explore profitable quantitative strategies.▪ They demonstrate the flexibility of the pattern-matching trading strategy by modifying the strategies to adapt to both the pre–COVID-19 period and the post–COVID-19 period.