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Related Experiment Videos

Financial volatility trading using recurrent neural networks.

P Tino1, C Schittenkopf, G Dorffner

  • 1Neural Computing Research Group, Aston University, Birmingham B4 7ET, UK.

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary

Trading straddles using recurrent neural networks (RNNs) or Markov models can yield profits, outperforming GARCH models. However, simple Markov models are as effective as complex RNNs for financial time-series volatility prediction.

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Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Econometrics

Background:

  • Daily trading of financial index straddles relies on volatility predictions.
  • Recurrent Neural Networks (RNNs) are often used but struggle with non-stationary financial time-series data.
  • Existing models may overestimate noise or have limited memory, hindering performance.

Purpose of the Study:

  • To evaluate the profitability of trading straddles using RNNs and other predictive models.
  • To compare the performance of RNNs against classical Markov models and econometric GARCH models.
  • To investigate methods for overcoming data nonstationarity in financial time-series analysis.

Main Methods:

  • Simulated daily trading of straddles based on predicted volatility differences.

Related Experiment Videos

  • Utilized Recurrent Neural Networks (RNNs) and fixed-order Markov models for predictions.
  • Employed a hybrid technique combining sophisticated models with simple symbolic predictors for non-stationary data.
  • Compared performance against GARCH family models.

    • Main Results:

    • GARCH models failed to generate significant profits.
    • Both RNNs and Markov models, when used carefully, achieved statistically significant excess profits.
    • No significant performance advantage was found for RNNs over simpler Markov models.

    Conclusions:

    • Simple Markov models are a viable alternative to complex RNNs for financial volatility prediction.
    • Any study reporting RNN performance on financial tasks should include comparisons with simpler models and non-stationarity handling techniques.
    • Effective strategies for trading straddles can be developed using appropriate volatility prediction models.