Pattern matching trading system based on the dynamic time warping algorithm

Sang Hyuk Kim, Hee Soo Lee, Han Jun Ko, Seung Hwan Jeong, Hyun Woo Byun, Kyong Joo Oh

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon's clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Financial market investors are able to make more efficient investment strategies by using the PMTS. In this sense, the system developed in this paper contributes the efficiency of the financial markets and helps to achieve sustained economic growth.

Original languageEnglish
Article number4641
JournalSustainability (Switzerland)
Volume10
Issue number12
DOIs
Publication statusPublished - 2018 Dec 6

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All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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