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.
Highlights
The global financial crisis of 2007–2008 (GFC) was caused by many factors but one of the main causes was the excessive expansion of financial assets including derivatives [1,2,3]
As a single time series data, the index futures, which generate a large amount of data as a result of large-scale transactions, have been widely used for statistical analysis [5,6]
We propose an algorithm trading system that matches the time series pattern of the index futures data with the representative pattern using the naïve dynamic time warping (DTW) algorithm
Summary
The global financial crisis of 2007–2008 (GFC) was caused by many factors but one of the main causes was the excessive expansion of financial assets including derivatives [1,2,3]. In stock price time series data, investors in equity markets show various patterns of investment. They can be categorized as investors who adopt fundamental analysis and technical analysis [11]. On the other hand, assuming that the past behavior of a stock price affects the future price, technical analysts make investment decisions based on historical prices or patterns of price movement using complex indicators. Technical analysts use pattern analysis methods to analyze stock price charts for trading decisions [12]. This pattern analysis is a method of predicting the stock price by examining specific patterns observed in the past stock price chart and confirming the existence of similar patterns in the current stock price [18]
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