Comparing Moving Average Rules with Mis-Specified Trend Models

Summary

This paper compares a model-based optimal trading strategy with a technical rule when the asset’s trend is latent and follows an Ornstein–Uhlenbeck process. It focuses on how each strategy performs when the model parameters used to design it are incorrectly specified.

The analysis derives asymptotic expected logarithmic returns for both approaches as functions of the model parameters. Numerical examples indicate that a cross-moving-average strategy is more robust to parameter errors than the model-based optimum. The result is specific to the stochastic price and trend assumptions studied; the excerpt gives no details about the parameter ranges, market data, transaction costs, or implementation effects, so it does not establish that moving averages will be more robust in other settings.

Key ideas

• The asset’s trend is modeled as an unobservable Ornstein–Uhlenbeck process.
• The study compares a model-based optimal strategy with a cross-moving-average trading rule.
• It derives asymptotic expected logarithmic returns as functions of model parameters.
• Numerical examples suggest the moving-average rule is more robust to parameter misspecification.

Tags

# Robustness of mathematical models and technical analysis strategies


# Robustness of mathematical models and technical analysis strategies









The aim of this paper is to compare the performances of the optimal strategy under parameters mis-specification and of a technical analysis trading strategy. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. For both strategies, we provide the asymptotic expectation of the logarithmic return as a function of the model parameters. Finally, numerical examples find that an investment strategy using the cross moving averages rule is more robust than the optimal strategy under parameters mis-specification.

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