Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
This work addresses the challenge of identifying profitable stock opportunities for investors, but it is incremental as it builds on existing case-based methods with a new metric.
The paper tackles the problem of predicting stock market returns by developing a novel similarity metric for comparing historical pricing data within a case-based reasoning approach, demonstrating benefits over conventional benchmarks in a real-world application.
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. We argue that one of the impediments for case-based stock prediction has been the lack of a suitable similarity metric when it comes to identifying similar pricing histories as the basis for a future prediction -- traditional Euclidean and correlation based approaches are not effective for a variety of reasons -- and in this regard, a key contribution of this work is the development of a novel similarity metric for comparing historical pricing data. We demonstrate the benefits of this metric and the case-based approach in a real-world application in comparison to a variety of conventional benchmarks.