Estimation of short-term market trend by Soft-Computing techniques. Practical application over IBEX 35’ values
Keywords:
Stock market prediction, Trend detection, Estimation of behaviors, Pattern recognition, ClusteringAbstract
Predicting stock market behavior is one of the major challenges for any investor of variable income. However, predict the behavior of such a fluctuating market as the determined by the set of values of the IBEX 35 index, where many factors can influence on it, it is a problem with difficult solution.Currently there are a wide variety of available methods to help investors perform this task. However, most of them make their estimations based solely on their previous behaviors, so the occurrence of unforeseen external factors, can rendered invalid any initial estimation.Faced with such problems, the use of Soft-Computing techniques, due to its ability to work with inaccurate and incomplete data, results a versatile tool in many environments. Therefore, in this paper is proposed a method based on the use of soft-computing techniques to help predict the stock market behavior.
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References
[1] P. M. Alexander, J. J. Van Loggerenber, : “The repertory grid: discovering a 50-yearold research technique”. Proceedings of SAICSIT, pp 192–199, 2005.
[2] T. Anbalagan, Uma S. «Classification and Prediction of Stock Market Index Based on Fuzzy Metagraph». Procedia Computer Science, nº 47, pp 214-221, 2015.
[3] Anish C. M., Babita M. «Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis». Journal of the Korean Statistical Society, nº 45, pp 64-76, 2015.
[4] G. Armando. !A hybrid genetic-neural architecture for stock indexes forecas¬ting”. Information Sciences, Vol. 170. Pg. 3-33, 2005.
[5] Chavarnakul T., Enke D. “Intelligent technical analysis based equivolume charting for stock trading using neural networks”. Expert Systems with Applications, nº34, pp. 1004-1017, 2008.
[6] Chavent M., Lechevallier Y., Olivier B. “DIVCLUS-T: A monothetic divisive hierarchical clustering method”. Comput. Stat. Data Anal, pp 687-701. DOI=10.1016/j.csda.2007.03.01, 2007.
[7] Díaz-Hermida F., Bugarín A. “Semi-fuzzy Quantifiers as a Tool for Building Linguistic Summaries of Data Patterns”. IEEE Symposium on Foundations of Computational Intelligence, pp 45-52, 2011.
[8] Evansa C., Pappasa K., Xhafab F. “Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation”. Mathematical and Computer Modelling, nº58, pp. 1249–1266, 2013.
[9] Grubbs, F. “Procedures for Detecting Outlying Observations in Samples”. Technometrics, V.11, N.1, pp 1-21, 1969.
[10] Kacprzyk J. “Computing With Words Is An Implementable Paradigm: Fuzzy Queries, Linguistic Data Summaries, and Natural-Language Generation”. IEEE Trans. Fuzzy Systems, nº 18, pp. 451-472, 2010.
[11] Karen E. “Automatic extraction and identification of chart patterns towards financial forecast”. Applied Soft Computing, nº 7, pp. 1197–1208, 2007.
[12] Korenius T, Laurikkala J., Järvelin K., and Juhola M. “Stemming and lemmatization in the clustering of finish text documents” in CIKM ’04: Proceedings of the thirteenth ACM international conference on Information and knowledge management. New York, NY, USA: ACM, pp. 625–633, 2004.
[13] Korfhage R. R. “Information storage and retrieval”. John Wiley & Sons, Inc.
[14] Lam M. (2004): “Neural network techniques for financial performance prediction: integrating fundamental and technical analysis”. Decision Support Systems, nº 37, pp. 567–581, 1997.
[15] Li D., Edwards E. “Automatic Estimation of Dixon’s Test for Extreme Values Using a SAS Macro Driven Program”. PharmaSug, Toronto, 2001.
[16] Mabua S., Hirasawab K., Obayashia M., Kuremotoa T. “Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals”. Expert Systems with Applications, nº 40, pp. 6311–6320, 2013.
[17] Madrigal J. A. “Invertir en bolsa con sentido común”. Anaya Multimedia. ISBN: 9788441537194, 2015.
[18] Matsumoto, S., Kamei, Y., Monden, A., Matsumoto, K. “Comparison of Outlier Detection Methods in Faultproneness Models”. Proceedings of the First international Symposium on Empirical Software Engineering and Measurement ESEM. IEEE Computer Society, Washington, USA, 2007.
[19] Nayal S. C., Misra B. B., Behera H. S. «Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices». Ain Shams Engineering Journal, in press, 2015.
Olivas J.A. “Contribución al Estudio Experimental de la Predicción basada en Categorías Deformables Borrosas”, Universidad de Castilla La Mancha, España. Tesis Doctoral, 2000.
[20] Oliveira F. A. “Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4”. Expert Systems with Applications, nº 40, pp. 7596–7606, 2014.
[21] Peralta A., Romero F. P., Polo M., Olivas J. A. “Knowledge extraction of the behaviour of software developers by the analysis of time recording logs”. FUZZIEEE. pp. 1-8, 2010.
[22] Quilan, J.R. “Induction of decision trees”. Machine Learning 1, nº 81-106, 1986.
[23] Rodríguez-González A., García-Crespo A, et al. “CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator”. Expert Systems with Applications. Elsevier, 2011.
[24] Romero F. P., Peralta A., Soto A., Olivas J. A., Serrano-Guerrero J. “Fuzzy optimized self-organizing maps and their application to document clustering”, Soft- Computing. DOI 10.1007/s00500-009-0468-3, Springer, 2009.
[25] Rousseeuw P., Leroy A.: “Robust Regression and Outlier Detection”. John Wiley & Sons. New York, USA, 1996.
[26] Svalina I., Galzina V., et al. “An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices”. Expert Systems with Applications, nº 40, pp. 6055–6063, 2013.
[27] Wang L., Wang Z., Zhao S., Tan S. «Stock market trend prediction using dynamical Bayesian factor graph». Expert Systems with Applications, nº 42, pp 6267-6275, 2015.
[28] Zadeh, L. A. “A note on prototype set theory and fuzzy sets”. Cognition, nº 12, pp. 291- 297, 1982.
[29] Zhanga W-G., Xiaob W-L., Konga W-T, Zhang Y. “Fuzzy pricing of geometric Asian options and its algorithm”. Applied Soft Computing, nº 28, pp. 360–367, 2015.
[2] T. Anbalagan, Uma S. «Classification and Prediction of Stock Market Index Based on Fuzzy Metagraph». Procedia Computer Science, nº 47, pp 214-221, 2015.
[3] Anish C. M., Babita M. «Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis». Journal of the Korean Statistical Society, nº 45, pp 64-76, 2015.
[4] G. Armando. !A hybrid genetic-neural architecture for stock indexes forecas¬ting”. Information Sciences, Vol. 170. Pg. 3-33, 2005.
[5] Chavarnakul T., Enke D. “Intelligent technical analysis based equivolume charting for stock trading using neural networks”. Expert Systems with Applications, nº34, pp. 1004-1017, 2008.
[6] Chavent M., Lechevallier Y., Olivier B. “DIVCLUS-T: A monothetic divisive hierarchical clustering method”. Comput. Stat. Data Anal, pp 687-701. DOI=10.1016/j.csda.2007.03.01, 2007.
[7] Díaz-Hermida F., Bugarín A. “Semi-fuzzy Quantifiers as a Tool for Building Linguistic Summaries of Data Patterns”. IEEE Symposium on Foundations of Computational Intelligence, pp 45-52, 2011.
[8] Evansa C., Pappasa K., Xhafab F. “Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation”. Mathematical and Computer Modelling, nº58, pp. 1249–1266, 2013.
[9] Grubbs, F. “Procedures for Detecting Outlying Observations in Samples”. Technometrics, V.11, N.1, pp 1-21, 1969.
[10] Kacprzyk J. “Computing With Words Is An Implementable Paradigm: Fuzzy Queries, Linguistic Data Summaries, and Natural-Language Generation”. IEEE Trans. Fuzzy Systems, nº 18, pp. 451-472, 2010.
[11] Karen E. “Automatic extraction and identification of chart patterns towards financial forecast”. Applied Soft Computing, nº 7, pp. 1197–1208, 2007.
[12] Korenius T, Laurikkala J., Järvelin K., and Juhola M. “Stemming and lemmatization in the clustering of finish text documents” in CIKM ’04: Proceedings of the thirteenth ACM international conference on Information and knowledge management. New York, NY, USA: ACM, pp. 625–633, 2004.
[13] Korfhage R. R. “Information storage and retrieval”. John Wiley & Sons, Inc.
[14] Lam M. (2004): “Neural network techniques for financial performance prediction: integrating fundamental and technical analysis”. Decision Support Systems, nº 37, pp. 567–581, 1997.
[15] Li D., Edwards E. “Automatic Estimation of Dixon’s Test for Extreme Values Using a SAS Macro Driven Program”. PharmaSug, Toronto, 2001.
[16] Mabua S., Hirasawab K., Obayashia M., Kuremotoa T. “Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals”. Expert Systems with Applications, nº 40, pp. 6311–6320, 2013.
[17] Madrigal J. A. “Invertir en bolsa con sentido común”. Anaya Multimedia. ISBN: 9788441537194, 2015.
[18] Matsumoto, S., Kamei, Y., Monden, A., Matsumoto, K. “Comparison of Outlier Detection Methods in Faultproneness Models”. Proceedings of the First international Symposium on Empirical Software Engineering and Measurement ESEM. IEEE Computer Society, Washington, USA, 2007.
[19] Nayal S. C., Misra B. B., Behera H. S. «Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices». Ain Shams Engineering Journal, in press, 2015.
Olivas J.A. “Contribución al Estudio Experimental de la Predicción basada en Categorías Deformables Borrosas”, Universidad de Castilla La Mancha, España. Tesis Doctoral, 2000.
[20] Oliveira F. A. “Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4”. Expert Systems with Applications, nº 40, pp. 7596–7606, 2014.
[21] Peralta A., Romero F. P., Polo M., Olivas J. A. “Knowledge extraction of the behaviour of software developers by the analysis of time recording logs”. FUZZIEEE. pp. 1-8, 2010.
[22] Quilan, J.R. “Induction of decision trees”. Machine Learning 1, nº 81-106, 1986.
[23] Rodríguez-González A., García-Crespo A, et al. “CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator”. Expert Systems with Applications. Elsevier, 2011.
[24] Romero F. P., Peralta A., Soto A., Olivas J. A., Serrano-Guerrero J. “Fuzzy optimized self-organizing maps and their application to document clustering”, Soft- Computing. DOI 10.1007/s00500-009-0468-3, Springer, 2009.
[25] Rousseeuw P., Leroy A.: “Robust Regression and Outlier Detection”. John Wiley & Sons. New York, USA, 1996.
[26] Svalina I., Galzina V., et al. “An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices”. Expert Systems with Applications, nº 40, pp. 6055–6063, 2013.
[27] Wang L., Wang Z., Zhao S., Tan S. «Stock market trend prediction using dynamical Bayesian factor graph». Expert Systems with Applications, nº 42, pp 6267-6275, 2015.
[28] Zadeh, L. A. “A note on prototype set theory and fuzzy sets”. Cognition, nº 12, pp. 291- 297, 1982.
[29] Zhanga W-G., Xiaob W-L., Konga W-T, Zhang Y. “Fuzzy pricing of geometric Asian options and its algorithm”. Applied Soft Computing, nº 28, pp. 360–367, 2015.
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Published
2016-06-30
How to Cite
Peralta, A., & Rejas, R. (2016). Estimation of short-term market trend by Soft-Computing techniques. Practical application over IBEX 35’ values. Revista CINTEX, 21(1), 113–135. Retrieved from https://revistas.pascualbravo.edu.co/index.php/cintex/article/view/12
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RESEARCH PAPERS
