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Trading in Markovian Price Models

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Learning Theory (COLT 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3559))

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Abstract

We examine a Markovian model for the price evolution of a stock, in which the probability of local upward or downward movement is arbitrarily dependent on the current price itself (and perhaps some auxiliary state information). This model directly and considerably generalizes many of the most well-studied price evolution models in classical finance, including a variety of random walk, drift and diffusion models. Our main result is a “universally profitable” trading strategy — a single fixed strategy whose profitability competes with the optimal strategy (which knows all of the underlying parameters of the infinite and possibly nonstationary Markov process).

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References

  • Blum, A., Kalai, A.: Universal portfolios with and without transaction costs. Machine Learning 35(3), 193–205 (1999)

    Article  MATH  Google Scholar 

  • Brock, A., Lakonishok, J., Lebaron, B.: Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance (47), 1731–1764 (1992)

    Google Scholar 

  • Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D.P., Schapire, R.E., Warmuth, M.K.: How to use expert advice. J. ACM 44(3), 427–485 (1997) ISSN 0004-5411

    Article  MATH  MathSciNet  Google Scholar 

  • Cover, T., Ordentlich, E.: Universal portfolios with side information. IEEE Transactions on Information Theory 42(2) (1996)

    Google Scholar 

  • El-Yaniv, R., Fiat, A., Karp, R.M., Turpin, G.: Optimal search and one-way trading online algorithms. Algorithmica 30, 101–139 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  • Helmbold, D.P., Schapire, R.E., Singer, Y., Warmuth, M.K.: On-line portfolio selection using multiplicative updates. In: International Conference on Machine Learning, pp. 243–251 (1996), citeseer.ist.psu.edu/article/helmbold98line.html

  • Hull, J.: Options, Futures, and Other Derivative Securities. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  • Lo, A., MacKinlay, A.C.: A Non-Random Walk Down Wall Street. Princeton University Press, Princeton (1999)

    Google Scholar 

  • Murphy, J.: Technical Analysis of the Financial Markets. New York Institute of Finance (1999)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Kakade, S.M., Kearns, M. (2005). Trading in Markovian Price Models. In: Auer, P., Meir, R. (eds) Learning Theory. COLT 2005. Lecture Notes in Computer Science(), vol 3559. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11503415_41

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  • DOI: https://doi.org/10.1007/11503415_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26556-6

  • Online ISBN: 978-3-540-31892-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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