Machine Learning for Financial Time Series: Identifying Prediction Patterns in Financial Time Series Using a Genetic Algorithm This work presents a framework based on a self-learning genetic algorithm for discovering prediction patterns in financial time series. In order to justify the in-sample performance, the prediction pa
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| Title | : | Machine Learning for Financial Time Series: Identifying Prediction Patterns in Financial Time Series Using a Genetic Algorithm |
| Author | : | |
| Rating | : | 4.83 (364 Votes) |
| Asin | : | 3639402308 |
| Format Type | : | Paperback |
| Number of Pages | : | 76 Pages |
| Publish Date | : | 2012-05-08 |
| Genre | : |
This work presents a framework based on a self-learning genetic algorithm for discovering prediction patterns in financial time series. By modifying a complex mathematical algorithm for evolutionary optimization in a manner more suitable for financial time series, specifics typical to asset trading were taken into account and were reflected in the solution set. In-sample the genetic algorithm was able to successfully find prediction rules allowing for the creation of successful, feasible and robust trading strategies with high performance. In order to justify the in-sample performance, the prediction patterns were tested on an out—of —sample time period : the results confirmed the ability of the genetic algorithm to find prediction patterns with robust performance. The proposed framework shows great potential when working with high dimensional search spaces and in finding non-linear dependencies in financial data.
Editorial : About the Author Antonio Stoyanov, Master of Mathematical Finance: Master’s degree in Mathematical Finance from the University of Konstanz. Currently Business Analyst at BearingPoint GmbH focusing on risk management for financial institutions.
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