A Temporal Pattern Approach for Predicting Weekly Financial Time Series
The American Society of Mechanical Engineers
Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life: Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2003)
Discovering patterns and relationships in the stock market has been widely researched for many years. The goal of this work is to find hidden patterns within stock market price time series that may be exploited to yield greater than expected returns. A data mining approach provides the framework for this research. The data set is composed of weekly financial data for the stocks in two major stock indexes. Experiments are conducted using a technique designed to discover patterns within the data. Results show that these methods can outperform the market in longer time ranges with bull market conditions. Results include consideration of transaction costs. INTRODUCTION Data mining is the process of discovering hidden patterns in data. Due to the large size of databases, importance of information stored, and valuable information obtained, finding hidden patterns in data has become increasingly significant. The stock market provides an area in which large volumes of data are created and stored on a daily basis, and hence an ideal dataset for applying data mining techniques. Statistical analysis has been widely used for many years to make predictions on the future values of a security price and study its behavior over time. Times series such as the stock market are often seen as non-stationary which present challenges in predicting future values. The focus of this research is in analyzing and predicting weekly financial time series. This work will show the advantage of using a weekly trading strategy, which is an extension of the daily trading strategy, to overcome the transaction cost associated with trading. The proposed method is a data mining approach that uses time-delay embedding and temporal patterns to characterize events. The method is designed to analyze non-stationary time series and provides the basis for this work. The paper is broken into five sections, which describe the goal of this work, overview of the Time Series Data Mining method, financial applications, results, and research conclusions.