Searching Financial Patterns with Self-Organizing Maps

Abstract

Using Self-organizing maps (SOM), tins paper formalizes chartists’ behavior in searching of patterns (charts). By applying a 6 by 6 two-dimensional SOM to a time series data of TAIEX (Taiwan Stock Index), 36 patterns are established. To see whether these 36 patterns transmit profitable signals, a “normalized” equity curve is drawn for each pattern up to 20 days after observing the pattern. Many of these equity curves are either monotonically increasing or decreasing, and none of them exhibits random fluctuation. Therefore, it is concluded that the patterns established by SOM can help us foresee the movement of stock index in the near future. We further test profitability performance by trading on these SOM-induced financial patterns. The equity-curve results show that SOM-induced trading strategy is able to outperform the buy-and-hold strategy in quite a significant period of time.