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On the Symbolic Analysis of Market Indicators with the Dynamic Programming Approach

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Symbolic analysis of time series of economic indicators offers an advantage of transferring quantitative values into qualitative concepts by indexing a subset of intervals with a set of symbols. In a similar way, computer codes routinely process continuous problems in a discrete manner. This work explains an appealing analogy between the DNA code of life and the symbol series derived from financial markets. In particular, it is shown that similarity scoring schemes and the alignment gap concept known in bioinformatics have even more natural and deeper analogies in the economic systems. The symbolic analysis does not solely mean a loss of information; in also allows us to quantify a similarity degree between various financial time series (and their subsequences) in a rigorous way, which is a novel concept of practical importance in economic applications. Our symbolic analysis concept is illustrated by two types of market indicator series, namely the analysis of Dow Jones vs. NIKKEI 225 indices on one side, and the CZK/EUR exchange rate vs. Prague money market rates on the other side. The present framework may also yield a significantly reduced computational complexity as compared to the neural networks in the class of similarity-comparison algorithms.

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References

  1. Trippi, R., Turban, E.: Neural Networks in Finance and Investing. McGraw-Hill/Irwin Publishing, New York (1996)

    Google Scholar 

  2. Moody, J.: Forecasting the Eonomy with Neural Nets: A Survey of Challenges and Solutions. In: Orr, G.B., Mueller, K.R. (eds.) Neural Networks - Tricks of the Trade. Springer, Berlin (1998)

    Google Scholar 

  3. Franke, J., Neumann, M.H.: Bootstrapping Neural Networks. Neural Computation 12, 1929–1949 (2000)

    Article  Google Scholar 

  4. Kilian, L., Taylor, M.P.: Why Is It So Difficult to Beat the Random Walk Forecast of Exchange Rates? Journal of International Economics 60(1), 85–107 (2003)

    Article  Google Scholar 

  5. Setubal, J., Meidanis, J.: Introduction to Computational Molecular Biology. PWS Publishing Company ITP, Boston (1997)

    Google Scholar 

  6. Bergeron, B.: Bioinformatics Computing. Pearson Education, Prentice Hall PTR, Indiana (2003)

    Google Scholar 

  7. Bufardi, A.: On the Fuzzyfication of the Classical Definition of Preference Structure. Fuzzy Sets and Systems 104(2), 323–332 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  8. Bellman, R.: Dynamic Programming. University Press, New York (1957)

    MATH  Google Scholar 

  9. Myers, E.W., Miller, W.: Optimal Alignments in Linear Space. CABIOS 4, 11–17 (1988)

    Google Scholar 

  10. Huang, X., Hardison, R.C., Miller, W.: A Space-Efficient Algorithm for Local Similarities. CABIOS 6, 373–381 (1990)

    Google Scholar 

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Authors and Affiliations

  1. Division of Natural Sciences, International Christian University,  

    Lukáš Pichl

  2. Division of Social Sciences, International Christian University, Osawa 3-10-2, Mitaka, Tokyo, 181-8585, Japan

    Takuya Yamano & Taisei Kaizoji

Authors
  1. Lukáš Pichl
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  2. Takuya Yamano
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  3. Taisei Kaizoji
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Pichl, L., Yamano, T., Kaizoji, T. (2006). On the Symbolic Analysis of Market Indicators with the Dynamic Programming Approach. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_64

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  • DOI: https://doi.org/10.1007/11760191_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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