An inter-market arbitrage trading system based on extended classifier systems

Traditionally, the most popular arbitrage strategy is derived from the cost of carry model or by using the econometrics approach. However, these approaches have difficulty in dealing with intra-day 1-min trading data and capturing inter-market arbitrage opportunity in the real world. In this research, we propose computational intelligence approaches based on the extended classifier system (XCS). First, in order to reduce the amount of data, the original data streams of intra-day 1-min trading data are filtered by the conditions of variant price spread relation. XCS is then adopted for knowledge rule discovery. After analyzing the property with domain-specific knowledge that the price of index futures will get close to that of spot products at the time the futures mature, four important factors related to bias, price spread, expiry date, and intraday trading timing are considered as the conditions of XCS to build the inter-market arbitrage model. The inter-market spread of the Taiwan Stock Index Futures (TX) traded at the Taiwan Futures Exchange (TAIFEX) and the Morgan Stanley Capital International (MSCI) Taiwan Index Futures traded at the Singapore Exchange Limited (SGX) are chosen for an empirical study to verify the accuracy and profitability of the model.

Research highlights
► Capturing inter-market arbitrage opportunity from intra-day 1-min trading data.
► Using association rules to filter high frequency data.
► XCS is adopted for knowledge rule discovery.