Following up on the topic of our forex prediction quality measurements, I've decided to conduct the same analysis on the simulated data, unpredictable by construction. As before, I am tracing the dependence of the Pearson correlation coefficients between predicted and actual logarithmic returns in day close value on the magnitude of the forecasting parameter nicknamed Fred. Fig.1. Pearson correlation of predicted and actual day-scale logarithmic returns as a function of the forecasting parameter nicknamed Fred. The vertical bands indicate a measure of uncertainty, their boundaries are plus/minus one standard deviation to the points. Back-testing simulations give the forecasing engine no access to the future data, direct or indirect. Significantly positive (and ideally, large) values correspond to quality forecasting. Shown in red is the standard deviation band associated with simulated data (see text). Note that the quantities at different Fred are not quite statistically independent, therefore the error bands should be understood as having to do with the curve at large rather than with individual points.
Forecasting optimization: an overlooked parameter fix improves quality while "efficient market" Monte Carlo supports the results.
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