Treand Behavior Research by Pattern Analysis in Financial Big data - A Case Study of Taiwan Index Futures Market

Abstract

Market structure provides concrete information about the market. Price patterns can be imagined as the evidence of a supply and demand states in the market. Price shifts higher as the demands exceed the available supply and vice-versa. These patterns convey precious information about what is going to happen in the market. The purpose of this study is to investigate the underlying relation between price pattern in Taiwan Futures Exchange (TAIFEX) Futures Index Market and its following trend. Forecasting the directions of price shift following the pattern through supervised learning and testing with artificial neural network (ANN). This research implements changepoint-analysis (CPA) under statistics field, and perceptually important points (PIP) theory. CPA finds the locations where the shifts in value occur. Then, PIP algorithm performs the feature extraction of the pattern. Then, the PIP is then fed to ANN to forecast the following trends. To simulate the research concept, a control model is built based on online time segmentation algorithm for comparison. The results of this research shows that robust patterns found by CPA have the ability to forecast market trend direction up to 83.6% accuracy. The result indicates that TAIFEX Futures market directions can be forecasted through its historical price robust patterns. Thus, rejecting that TAIFEX Futures Index Market follows random walk theory. In contrast, the control model which was built based on online time segmentation also has the ability to forecast but not as accurate as using the CPA method. In conclusion, analyzing the patterns reflected in the market effectively provide precious insights about its trends behavior.