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Related Work

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Identifying Patterns in Financial Markets

Abstract

This chapter presents background information and reviews the existing literature that is relevant to the development of this project. The first part of this chapter presents a brief description of the two existing approaches to analyze the market, in Sect. 2.1 will be described in detail the fundamental and the technical analysis and its tools. A formal definition of an optimization methodology is given in Sect. 2.2. A review of the existence literature about pattern recognition/detection and its techniques to invest in the market is detailed in Sect. 2.3.

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References

  1. Helfert, E.: Financial Analysis Tools and Techniques: A Guide for Managers. McGraw-Hill Education (2001)

    Google Scholar 

  2. Kirkpatrick II, C.D., Dahlquist, J.R.: Technical Analysis: The Complete Resource for Financial Market Technicians, 2nd edn. (2010)

    Google Scholar 

  3. Murphy, J.J.: Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications (1999)

    Google Scholar 

  4. Bulkowski, T.N.: Encyclopedia of Chart Patterns, 2nd edn. Wiley (2005)

    Google Scholar 

  5. Colby, R.W.: The Encyclopedia of Technical Market Indicators. McGraw-Hill (2003)

    Google Scholar 

  6. Lin, L., Cao, L., Wang, J., Zhang, C.: The Applications of Genetic Algorithms in Stock Market Data Mining Optimization (2000)

    Google Scholar 

  7. Chen, S.H.: Genetic Algorithms and Genetic Programming in Computational Finance. Springer, Boston (2002)

    Book  Google Scholar 

  8. Pinto, J., Neves, R., Horta, N.: Fitness function evaluation for MA trading strategies based on genetic algorithms. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 819–820 (2011)

    Google Scholar 

  9. Leigh, W., Modani, N., Purvis, R., Roberts, T.: Stock market trading rule discovery using technical charting heuristics. Expert Syst. Appl. 23(2), 155–159 (2002)

    Article  Google Scholar 

  10. Leigh, W., Paz, N., Purvis, R.: Market timing: a test of a charting heuristic. Econ. Lett. 77(1), 55–63 (2002)

    Article  MATH  Google Scholar 

  11. Leigh, W., Frohlich, C.J., Hornik, S., Purvis, R., Roberts, T.: Trading with a stock chart heuristic. Syst. Man Cybern. Part A Syst. Hum. 38(1), 93–104 (2008)

    Article  Google Scholar 

  12. Leigh, W., Purvis, R., Ragusa, J.M.: Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network and genetic algorithm: a case study in romantic decision support. Decis. Support Syst. 32(4), 361–377 (2002)

    Article  Google Scholar 

  13. Leigh, W., Paz, M., Purvis, R.: An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index. Omega 30(2), 69–76 (2002)

    Article  Google Scholar 

  14. Wang, J., Chan, S.: Trading rule discovery in the US stock market: an empirical study. Expert Syst. Appl. 36(2), 5450–5455 (2009)

    Article  Google Scholar 

  15. Wang, J., Chan, S.: Stock market trading rule discovery using pattern recognition and technical analysis. Expert Syst. Appl. 33(2), 304–315 (2007)

    Article  Google Scholar 

  16. Parracho, P., Neves, R., Horta, N.: Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel. In: IEEE Congress on Evolutionary Computation, pp. 1895–1901 (2011)

    Google Scholar 

  17. Cervelló-Royo, R., Guijarro, F., Michniuk, K.: Stock market trading rule based on pattern recognition and technical analysis: forecasting the DJIA index with intraday data. Expert Syst. Appl. 42(14), 5963–5975 (2015)

    Article  Google Scholar 

  18. Fu, T., Chung, F., Luk, R., Ng, C.: Representing financial time series based on data point importance. Eng. Appl. Artif. Intell. 21(2), 277–300 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Tsinaslanidis, P.E., Kugiumtzis, D.: A prediction scheme using perceptually important points and dynamic time warping. Expert Syst. Appl. 41(15), 6848–6860 (2014)

    Article  Google Scholar 

  21. Ni, H.: Profitability of technical chart pattern trading on FX rates: analyzed by wavelet transform. In: Third International Symposium on Intelligent Information Technology Application, pp. 138–141. Nanchang (2009)

    Google Scholar 

  22. Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Disc. 15(2), 107–144 (2007)

    Article  MathSciNet  Google Scholar 

  23. Canelas, A., Neves, R., Horta, N.: A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques. Expert Syst. Appl. 40(5), 1579–1590 (2013)

    Article  Google Scholar 

  24. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. J. Knowl. Inf. Syst. 3(3), 263–286 (2000)

    Article  MATH  Google Scholar 

  25. Leitão, J., Neves, R.F., Horta, N.: Combining rules between PIPs and SAX to identify patterns in financial markets. Expert Syst. Appl. 65, 242–254 (2016) (Reprinted with permission from Elsevier)

    Google Scholar 

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Leitão, J., Neves, R.F., Horta, N.C.G. (2018). Related Work. In: Identifying Patterns in Financial Markets. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-70160-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-70160-8_2

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