A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques

Authors:
AntóNio Canelas;Rui Neves;Nuno Horta
Affiliations:
Instituto de Telecomunicaçíes, Instituto Superior Técnico, Torre Norte, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal;Instituto de Telecomunicaçíes, Instituto Superior Técnico, Torre Norte, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal;Instituto de Telecomunicaçíes, Instituto Superior Técnico, Torre Norte, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 8
Cited 1

Efficient Similarity Search In Sequence Databases

FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation

CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
A symbolic representation of time series, with implications for streaming algorithms

DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Stock time series visualization based on data point importance

Engineering Applications of Artificial Intelligence
Stock Forecast Method Based on Wavelet Modulus Maxima and Kalman Filter

ICMECG '10 Proceedings of the 2010 International Conference on Management of e-Commerce and e-Government
Fitness function evaluation for MA trading strategies based on genetic algorithms

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Forecasting Stock Exchange Movements Using Neural Networks: A Case Study

ICFCSA '11 Proceedings of the 2011 International Conference on Future Computer Sciences and Application
Stock Market Prediction Using Artificial Neural Networks Based on HLP

IHMSC '11 Proceedings of the 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01

Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Quantified Score

Hi-index	12.05

Visualization

Abstract

This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.