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A TEMPORAL PATTERN APPROACH FOR PREDICTING WEEKLY FINANCIAL TIME SERIES
| Content Provider | CiteSeerX |
|---|---|
| Author | Diggs, David H. Povinelli, Richard J. |
| Abstract | 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Hidden Pattern Bull Market Condition Stock Market Stock Market Price Time Series Weekly Financial Data Data Set Data Mining Approach Major Stock Index Transaction Cost Many Year |
| Content Type | Text |
