Testing Moving Average Crossover Forecasts Against Price Direction

Summary

The article evaluates whether moving-average crossovers are easier to forecast than short-horizon price direction. Using EURUSD 15-minute data, it labels future outcomes based on the relative position of short and longer simple moving averages, then compares those targets with a direct price-direction target. It describes time-ordered train and test splits with a gap tied to the forecast horizon, and lists several candidate classifiers, including linear models, tree methods, and neural networks. The stated predictors include price, tick volume, spread, and both moving averages.

The text reports that direct price forecasts perform poorly, while linear discriminant analysis stands out on crossover prediction. It proposes that moving averages change more slowly than price as one possible reason. The article also discusses backward feature selection and adding other technical indicators. The available excerpt omits detailed crossover scores and much of the results, and predictive accuracy alone does not show that a tradable strategy would be profitable after costs or out of sample.

Key ideas

• The experiment compares forecasts of future moving-average ordering with forecasts of direct price direction.
• Time-series splits and a forecast-horizon gap are used to preserve temporal ordering during evaluation.
• The article reports weak direct price prediction and stronger crossover prediction by linear discriminant analysis.
• Slower changes in moving-average relationships may make crossovers easier to forecast than short-term price moves.
• Feature selection and added indicators are explored, but accuracy does not establish trading profitability.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.