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Market Analysis Background and Related Work

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Investment Strategies Optimization based on a SAX-GA Methodology

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Abstract

In this chapter some fundamental concepts, necessary to understand the developed work, are addressed, particularly the domain relative to financial markets and time series analysis. Furthermore several methodologies applied to market investment and especially to pattern detection are presented. Finally an introduction to the SAX representation method will be presented and previous works using this methodology will be discussed.

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References

  1. B. Baumohl, The Secrets of Economic Indicators: Hidden Clues to Future Economic Trends and Investment Opportunities, 2nd edn. (Wharton School Publishing, Pennsylvania, 2007)

    Google Scholar 

  2. S.B. Achelis, Technical Analysis From A-To-Z. (Vision Books, New Delhi, 2000)

    Google Scholar 

  3. T.N. Bulkowski, Encyclopedia of Chart Patterns, 2nd edn. (Wiley, New Jersey, 2005)

    Google Scholar 

  4. R. Fischer, J. Fischer, Candlesticks, Fibonacci, and Chart Pattern Trading Tools: A Synergistic Strategy to Enhance Profits and Reduce Risk. (Wiley, New Jersey, 2003)

    Google Scholar 

  5. R.W. Colby, The Encyclopedia of Technical Market Indicators, 2nd edn. (McGraw-Hill, New York, 2003)

    Google Scholar 

  6. E. Ponsi, Forex Patterns & Probabilities—Trading Strategies for Trending and Range-Bound Markets. (Wiley Trading, Wiley, New Jersey, 2007)

    Google Scholar 

  7. P. Parracho, R. Neves, N. Horta, Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel. IEEE Congr. Evol. Comput. 1895–1901 (2011). doi:10.1109/CEC.2011.5949846

  8. T.-C. Fu, A review on time series data mining. Int. J Eng. Appl. Artif. Intell. 24(3), 164–181 (2011). ISSN 0952-1976. doi:10.1016/j.engappai.2010.09.007

  9. F.-L. Chung, T.-C. Fu, R. Luk, V. Ng, Flexible time series pattern matching based on perceptually important points. Int. Jt. Conf. Artif. Intell. Workshop on Learn from Temporal and Spatial Data, 1–7 (2001)

    Google Scholar 

  10. T.-C. Fu, F.-L. Chung, R. Luk, C.M. Ng, Representing financial time series based on data point importance. Eng. Appl. Artif. Intell. 21(2):277–300 (2008). ISSN 0952-1976. doi:10.1016/j.engappai.2007.04.009

    Google Scholar 

  11. F.-L. Chung, T.-C Fu, V. Ng, R.W.P Luk, An evolutionary approach to pattern-based time series segmentation. IEEE Trans. Evol. Comput. 8(5), 471–489 (2004). doi:10.1109/TEVC.2004.832863

    Google Scholar 

  12. T.-C. Fu, F.-L. Chung, R. Luk, V. Ng, Pattern discovery from stock time series using self-organizing maps. The 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)

    Google Scholar 

  13. W. Leigh, , N. Modani, R. Purvis, T. Roberts, Stock market trading rule discovery using technical charting heuristics. Expert Sys. Appl. 23(2), 155–159 (2002). ISSN 0957-4174. doi:10.1016/S0957-4174(02)00034-9

  14. W. Leigh, N. Paz, R. Purvis, Market timing: a test of a charting heuristic. Econ. Lett. 77(1), 55–63 (2002). ISSN 0165-1765. doi:10.1016/S0165-1765(02)00110-6

  15. W. Leigh, C.J. Frohlich, S. Hornik, , R.L. Purvis, , T.L. Roberts, Trading with a stock chart heuristic. IEEE Trans. Part A: Sys. Hum. Sys. Man Cybern. 38(1), 93–104 (2008). doi:10.1109/TSMCA.2007.909508

    Google Scholar 

  16. W. Leigh, M. Paz, R. Purvis, An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index. Omega, Elsevier 30, 69–76 (2002)

    Google Scholar 

  17. W. Leigh, R. Purvis, J.M. Ragusa, Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. J Decis. Support Sys. 32(4), 361–377 (2002). doi:10.1016/S0167-9236(01)00121-X

    Article  Google Scholar 

  18. K. Matsui, H. Sato, Neighborhood evaluation in acquiring stock trading strategy using genetic algorithms. The International Conference on Soft Computing and Pattern Recognition (SoCPaR), pp. 369–372 (2010). doi:10.1109/SOCPAR.2010.5686733

  19. J. Pinto, R. Neves, N. Horta, Fitness function evaluation for MA trading strategies based on genetic algorithms. ed. by N. Krasnogor. Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO ‘11). ACM, New York, NY, USA, pp. 819–820 (2011). doi:10.1145/2001858.2002105

  20. L. Wang, Q. Wang, Stock market prediction using artificial neural networks based on HLP. Int. Conf. Intel. Hum. Mach. Sys. Cybern. 1, 116–119 (2011). doi:10.1109/IHMSC.2011.34

  21. L.J. Cao, F.E.H. Tay, Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Netw. 14(6), 1506–1518 (2003). doi:10.1109/TNN.2003.820556

    Article  Google Scholar 

  22. D. Zhang, H. Song, P. Chen, Stock market forecasting model based on a hybrid ARMA and support vector machines. Proceedings of the 15th International Conference on Management Science and Engineering, Long Beach, USA (2008)

    Google Scholar 

  23. J. Lin, E. Keogh, S. Lonardi, B. Chiu, A symbolic representation of time series, with implications for streaming algorithms. Proceedings of the 8th ACM SIGMOD International Conference on Management of Data, Workshop on Res. Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003). doi:10.1145/882082.882086

  24. J. Lin, E. Keogh, S. Lonardi, P. Patel, Finding motifs in time series. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)

    Google Scholar 

  25. C. Faloutsos, M. Ranganathan, Y. Manolopoulos, Fast subsequence matching in time-series databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, Minneapolis, pp. 419–429 (1994). doi:10.1145/191839.191925

  26. E. Keogh, K. Chakrabarti, M. Pazzani, S. Mehrotra, Dimensionality reduction for fast similarity search in large time series databases. J Knowl. nf. Sys. (2000). doi:10.1145/191839.191925

    Google Scholar 

  27. J. Lin, E. Keogh, L. Wei, S. Lonardi, Experiencing SAX: A novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007). doi:10.1007/s10618-007-0064-z

  28. J. Shieh, K. Keogh, iSAX: Indexing and mining terabyte sized time series. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 623–631 (2008). doi:10.1145/1401890.1401966

  29. A. Camerra, T. Palpanas, J. Shieh, E. Keogh, iSAX 2.0: Indexing and mining one billion time series. Proceedings of the 2010 IEEE International Conference Data Mining (ICDM ‘10). IEEE Computer Society, Washington, DC, USA, pp. 58–67 (2010). doi:10.1109/ICDM.2010.124

  30. B. Lkhagva, Y. Suzuki, K. Kawagoe, New time series data representation ESAX for financial applications. Proceedings of the 22nd International Conference Data Engineering Workshops (ICDEW ‘06), p. 115 (2006). doi:10.1109/ICDEW.2006.99

  31. J.-L. Wang, S.-H. Chan, Stock market trading rule discovery using pattern recognition and technical analysis. Expert Sys. Appl. 33(2), 304–315 (2007). doi:10.1016/j.eswa.2006.05.002

  32. T.-C. Fu, F.-L. Chung, R. Luk, C.M. Ng, Stock time series pattern matching: Template-based vs rule-based approaches. Eng. Appl. Artif. Intell. 20(3), 347–364 (2007)

    Google Scholar 

  33. P.-C. Chang, C.-Y. Fan, C.-H. Liu, Y.-W. Wang, J.-J. Lin, Evolving neural network with dynamic time warping and piecewise linear representation system for stock trading decision making. 2009 WRI World Congr. Comput. Sci. Inf. Eng. 5, 303–307 (2009). doi:10.1109/CSIE.2009.36

  34. L. Junmyung, C. Sungzoon, B. Jinwoo, Trend detection using auto-associative neural networks: Intraday KOSPI 200 futures. Proceedings of the IEEE International conference on Computational Intelligence for Financial Engineering, pp. 417–420. (2003) doi:10.1109/CIFER.2003.1196290

  35. L.C. Martinez, D.N. da Hora, J.R. de M Palotti, W. Meira, G.L. Pappa (2009) From an artificial neural network to a stock market day-trading system: A case study on the BM&F BOVESPA, The International Joint Conference on Neural Networks 2006–2013 (2009). doi:10.1109/IJCNN.2009.5179050

  36. A.U. Khan, T.K. Bandopadhyaya, S. Sharma, Classification and identification of stocks using SOM and genetic algorithm based backpropagation neural network. International Conference on Innovation in Management and Information Technology, pp. 292–296 (2008). doi:10.1109/INNOVATIONS.2008.4781644

  37. P.-C. Ko, P.-C. Lin, C.-S. Shih, Stock valuation and dynamic asset allocation with genetic algorithm and cubic spline. Int. Conf. Mach. Learn. Cybern. 7, 3997–4000 (2008). doi:10.1109/ICMLC.2008.4621101

  38. H.-N. Hao, Short-term forecasting of stock price based on genetic-neural network. 6th Int. Conf. Nat. Comput. 4, 1838–1841 (2010). doi:10.1109/ICNC.2010.5584528

  39. W.W.Y. Ng, X.-L. Liang, P.P.K. Chan, D.S. Yeung, Stock investment decision support for Hong Kong market using RBFNN based candlestick models. Int. Conf. on Mach. Learn. Cybern. 2, 538–543 (2011)

    Google Scholar 

  40. H. Tahersima, M. Tahersima, M. Fesharaki, N. Hamedi, Forecasting stock exchange movements using neural networks: A case study. Proceedings of the International Conference on Future Computer Science and Applications, pp. 123–126 (2011)

    Google Scholar 

  41. Z. Fang, G. Luo, F. Fei, S. Li, Stock forecast method based on wavelet modulus maxima and kalman filter. Proceedings of the 4th International Conference on Management of e- Commerce and e-Government, pp. 50–53 (2010). doi:10.1109/ICMeCG.2010.19

  42. V. Parque, S. Mabu, K. Hirasawa, Enhancing global portfolio optimization using genetic network programming. Proceedings of the SICE Annual Conference, pp. 3078–3083 (2010)

    Google Scholar 

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Authors and Affiliations

  1. Instituto de Telecomunicações/Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Antonio M. L. Canelas, Rui F. M. M. Neves & Nuno C. G. Horta

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  1. Antonio M. L. Canelas
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  2. Rui F. M. M. Neves
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  3. Nuno C. G. Horta
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Correspondence to Antonio M. L. Canelas .

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Canelas, A.M.L., Neves, R.F.M.M., Horta, N.C.G. (2013). Market Analysis Background and Related Work. In: Investment Strategies Optimization based on a SAX-GA Methodology. SpringerBriefs in Applied Sciences and Technology(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33110-7_2

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  • DOI: https://doi.org/10.1007/978-3-642-33110-7_2

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