António Canelas
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.
- Agrawal, R., Faloutsos, C., and Swami, A. 1993. Efficient Similarity Search in Sequence Databases. In Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms (FODO '93). 69--84. Google Scholar
Digital Library
- Bernstein, P. L. 1999. A new look at the efficient market hypothesis. The Journal of Portfolio Management 25, 2, 1--2, (Winter 1999).Google ScholarCross Ref
- Bulkowski, T. N. 2005. Encyclopedia of Chart Patterns, 2nd Edition. John Wiley and Sons.Google Scholar
- Fu, T., Chung, K. F., Kwok, K., and Ng, C. 2008. Stock time series visualization based on data point importance. Eng. Appl. Artif. Intell. 21, 8 (Dec. 2008), 1217--1232. DOI= http://dx.doi.org/10.1016/j.engappai.2008.01.005. Google ScholarDigital Library
- Goldin, D. and Kanellakis, P. 1995. On Similarity Queries for Time-Series Data: Constraint Specification and Implementation. In Proceedings of the First International Conference on Principles and Practice of Constraint Programming (CP '95), 137--153. Google ScholarDigital Library
- Hua-Ning, H. 2010. Short-term forecasting of stock price based on genetic-neural network. In Natural Computation (ICNC), 2010 Sixth International Conference on (10-12 Aug. 2010), 4, 1838--1841.Google Scholar
- Keogh, E., Chakrabarti, K., Pazzani, M. and Mehrotra, S. 2001. Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems, 3, 3, 263--286. (Aug. 2001). DOI= http://dx.doi.org/10.1007/PL00011669.Google ScholarCross Ref
- Krause, A. 2011. Performance of evolving trading strategies with different discount factors. In Congress on Evolutionary Computation (CEC), 2011 IEEE, (5-8 Jun. 2011), 186--191. DOI= http://dx.doi.org/10.1109/CEC.2011.5949617.Google ScholarCross Ref
- Lei Wang and Qiang Wang. 2011. Stock Market Prediction Using Artificial Neural Networks Based on HLP. In Proceedings of the 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01 (IHMSC '11), 1, 116--119. DOI=http://dx.doi.org/10.1109/IHMSC.2011.34. Google ScholarDigital Library
- Lin, J., Keogh, E., Lonardi, S. and Chiu, B. 2003. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery (DMKD '03). ACM, New York, NY, USA, 2--11. DOI=http://doi.acm.org/10.1145/882082.882086. Google ScholarDigital Library
- Lin, J., Keogh, E., Lonardi, S. and Patel, P. 2002. Finding Motifs in Time Series, Proceedings of the 2nd Workshop n Temporal Data Mining (2002), 53--68.Google Scholar
- Matsui, K. and Sato, H. 2010. Neighborhood evaluation in acquiring stock trading strategy using genetic algorithms. In Soft Computing and Pattern Recognition (SoCPaR), 2010 International Conference. (7-10 Dec. 2010), 369--372, DOI= 10.1109/SOCPAR.2010.5686733.Google ScholarCross Ref
- Ng, W. W. Y., Xue-Ling Liang, Chan, P. P. K. and Yeung, D. S. 2011. Stock investment decision support for Hong Kong market using RBFNN based candlestick models. In Machine Learning and Cybernetics (ICMLC), 2011 International Conference. 2, (10-13 Jul. 2011), 538--543, DOI= 10.1109/ICMLC.2011.6016839.Google ScholarCross Ref
- Parque, V., Mabu, S. and Hirasawa, K. 2010. Enhancing global portfolio optimization using genetic network programming. In SICE Proceedings of Annual Conference 2010. (18-21 Aug. 2010), 3078--3083.Google Scholar
- Parracho, P., Neves, R. and Horta, N. 2011. Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel, In Congress on Evolutionary Computation (CEC), 2011 IEEE, (5-8 Jun. 2011), 1895--1901. DOI=10.1109/CEC.2011.5949846.Google ScholarCross Ref
- Pinto, J., Neves, R. F. and Horta, N. 2011. Fitness function evaluation for MA trading strategies based on genetic algorithms. In Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (GECCO '11). ACM, New York, NY, USA, 819--820. DOI= http://doi.acm.org/10.1145/2001858.2002105. Google ScholarDigital Library
- Tahersima, H., Tahersima, M., Fesharaki, M. and Hamedi, N. 2011. Forecasting Stock Exchange Movements Using Neural Networks: A Case Study. In Proceedings of the 2011 International Conference on Future Computer Sciences and Application (ICFCSA '11). IEEE Computer Society, Washington, DC, USA, 123--126. DOI= http://dx.doi.org/10.1109/ICFCSA.2011.35. Google ScholarDigital Library
- Zhijun, F., Guihua, L., Fengchang, F. and Shuai Li 2010. Stock Forecast Method Based on Wavelet Modulus Maxima and Kalman Filter. In Proceedings of the 2010 International Conference on Management of e-Commerce and e-Government (ICMECG '10). IEEE Computer Society, Washington, DC, USA, 50--53. DOI= http://dx.doi.org/10.1109/ICMeCG.2010.19. Google ScholarDigital Library
Index Terms
- A new SAX-GA methodology applied to investment strategies optimization
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