Dynamic Time Warping: S&P 500 Sector ETF Pattern Matching Trading Strategy
Alexander Fleiss, Che Liu, Gihyen Eom, Serena Yu, Wo Zhang · The Journal of Financial Data Science · 2021
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 methods Key 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.