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Related Experiment Videos

Improved option pricing using artificial neural networks and bootstrap methods

P R Lajbcygier1, J T Connor

  • 1School of Business Systems, Monash University, Clayton, Victoria, Australia. PLAJBCYG@fcit-m1.fcit.monash.edu.au

International Journal of Neural Systems
|August 1, 1997
PubMed
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A novel hybrid neural network improves stock index option pricing predictions. A modified bootstrap method reduces pricing bias and enhances trading strategy performance, especially with limited data.

Area of Science:

  • Quantitative Finance
  • Computational Finance
  • Machine Learning in Finance

Background:

  • Conventional option-pricing models often fail to capture intraday price dynamics.
  • Hybrid neural networks offer potential improvements but can introduce bias.
  • Bootstrap methods are crucial for estimating confidence intervals and mitigating bias.

Purpose of the Study:

  • To develop and evaluate a hybrid neural network model for predicting differences in stock index option futures pricing.
  • To enhance a trading strategy by incorporating confidence intervals from bootstrap methods.
  • To investigate a modified bootstrap predictor for reducing hybrid model bias and improving performance.

Main Methods:

  • Utilizing a hybrid neural network for option price difference prediction.

Related Experiment Videos

  • Implementing bootstrap methods to derive confidence intervals for trading decisions.
  • Developing and testing a modified bootstrap predictor with a tunable parameter.
  • Comparing the modified bootstrap predictor against pure bootstrap and bagging predictors.

    • Main Results:

    • The hybrid neural network model shows improved prediction capabilities.
    • Bootstrap methods effectively reduce bias in hybrid option-pricing models.
    • The modified bootstrap predictor outperforms standard hybrid and bagging approaches.
    • Significant performance gains were observed at the boundaries of the training set and with sparse data.

    Conclusions:

    • Hybrid neural networks, when combined with bootstrap bias reduction techniques, offer a superior approach to option pricing.
    • The modified bootstrap predictor demonstrates robust performance, particularly in data-scarce scenarios.
    • This enhanced methodology improves the reliability and effectiveness of option pricing models and associated trading strategies.