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Sequential optimizing investing strategy with neural networks


  • Ryo Adachi
  • Akimichi Takemura


In this paper we propose an investing strategy based on neural network models combined with ideas from game-theoretic probability of Shafer and Vovk. Our proposed strategy uses parameter values of a neural network with the best performance until the previous round (trading day) for deciding the investment in the current round. We compare performance of our proposed strategy with various strategies including a strategy based on supervised neural network models and show that our procedure is competitive with other strategies.

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  • Ryo Adachi & Akimichi Takemura, 2010. "Sequential optimizing investing strategy with neural networks," Papers 1002.2265,
  • Handle: RePEc:arx:papers:1002.2265

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    References listed on IDEAS

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    4. Peter Carr & Anita Mayo, 2007. "On the Numerical Evaluation of Option Prices in Jump Diffusion Processes," The European Journal of Finance, Taylor & Francis Journals, vol. 13(4), pages 353-372.
    5. N. Hilber & N. Reich & C. Schwab & C. Winter, 2009. "Numerical methods for Lévy processes," Finance and Stochastics, Springer, vol. 13(4), pages 471-500, September.
    6. Peter Carr & Helyette Geman, 2002. "The Fine Structure of Asset Returns: An Empirical Investigation," The Journal of Business, University of Chicago Press, vol. 75(2), pages 305-332, April.
    7. Peter Carr & Hélyette Geman & Dilip Madan & Marc Yor, 2005. "Pricing options on realized variance," Finance and Stochastics, Springer, vol. 9(4), pages 453-475, October.
    8. Madan, Dilip B & Seneta, Eugene, 1990. "The Variance Gamma (V.G.) Model for Share Market Returns," The Journal of Business, University of Chicago Press, vol. 63(4), pages 511-524, October.
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