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Probability distributions, trading strategies and leverage: an application of Gaussian mixture models

Author

Listed:
  • Paulo Lisboa

    (Liverpool John Moores University, UK)

  • Christian L. Dunis

    (Liverpool John Moores University, UK)

  • Andreas Lindemann

    (Liverpool John Moores University, UK)

Abstract

The purpose of this paper is twofold. Firstly, to assess the merit of estimating probability density functions rather than level or classification estimations on a one-day-ahead forecasting task of the EUR|USD time series. This is implemented using a Gaussian mixture model neural network, benchmarking the results against standard forecasting models, namely a naïve model, a moving average convergence divergence technical model (MACD), an autoregressive moving average model (ARMA), a logistic regression model (LOGIT) and a multi-layer perceptron network (MLP). Secondly, to examine the possibilities of improving the trading performance of those models with confirmation filters and leverage. While the benchmark models perform best without confirmation filters and leverage, the Gaussian mixture model outperforms all of the benchmarks when taking advantage of the possibilities offered by a combination of more sophisticated trading strategies and leverage. This might be due to the ability of the Gaussian mixture model to identify successfully trades with a high Sharpe ratio. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • Paulo Lisboa & Christian L. Dunis & Andreas Lindemann, 2004. "Probability distributions, trading strategies and leverage: an application of Gaussian mixture models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(8), pages 559-585.
    • Handle: RePEc:jof:jforec:v:23:y:2004:i:8:p:559-585
    DOI: 10.1002/for.935
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    Cited by:

    1. Steven B. Caudill & Daniel M. Gropper & Valentina Hartarska, 2009. "Which Microfinance Institutions Are Becoming More Cost Effective with Time? Evidence from a Mixture Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(4), pages 651-672, June.
    2. Andreas Lindemann & Christian Dunis & Paulo Lisboa, 2005. "Probability distributions and leveraged trading strategies: an application of Gaussian mixture models to the Morgan Stanley Technology Index Tracking Fund," Quantitative Finance, Taylor & Francis Journals, vol. 5(5), pages 459-474.

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