Intermarket analysis seems to make no dramatic difference to forex prediction quality on day scale

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Written by Forex Automaton   
Friday, 04 December 2009 16:50

As we are gearing up towards the launch of a real-time day scale forecasting service, I am using the newly developed forecasting figure of merit, Pearson correlation coefficient between the real and predicted logarithmic returns, to make a choice of the operating mode for the test mode of the service. I am taking another look at the dependence of the prediction quality on a day scale on the magnitude of a forecasting parameter nicknamed Fred for two distinct pattern recognition modes, namely that of completely independent analysis of the different exchange rates (so-called Step One), and the one in which the time series for the indvidivual exchange rates are considered jointly in order to utilize potential inter-market patterns (Step Three).

Pearson correlation of predicted and actual day-scale forex logarithmic returns as a function of the forecasting parameter

Fig.1. Pearson correlation of predicted and actual day-scale logarithmic returns as a function of the forecasting parameter nicknamed Fred. The shaded bands indicate a measure of uncertainty, their boundaries are plus/minus one standard deviation to the points. Back-testing simulations give the forecasting engine no access to the future data, direct or indirect. Significantly positive (and ideally, large) values correspond to quality forecasting. 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.


Money management for the individual markets



Pattern analysis for the individual markets


Step One

Step Two



Step Three

Table 1. Various modes of the trading system operation. The present report deals with the configuration denoted as Step One, the simplest of the three.

Day scale data for AUD/USD, EUR/USD, GBP/USD, USD/CAD, USD/CHF and USD/JPY, covering the time span from August 20, 2002 through August 21, 2009 are used in Fig.1. The middle and half-width of the bands around the data points in Fig.1 represent the mean and standard deviation of the set of the individual Pearson correlation coefficients for the 6 major forex exchange rates, treated as independent measurements.

Recall that the Pearson correlation coefficient has a known range from -1 (the quantities being total opposites) to 1 (total correlation) and that way there is a scale for comparison to know what is large and what is small. A realistic expectation for a measurement like that is to lie around 0. If forex is efficient, there is no way to design a system capable of making predictions, since all available information is instantly discounted by the market, therefore yesterday's (and older) data are of no use to predict today's close: all yesterday's information has been discounted yesterday. Therefore, in such a hypothetic situation, the predicted close (or equally, its representative in the analysis, predicted logarithmic return) and the actual one have only one choice -- to yield zero covariance and zero Pearson correlation coefficient after a proper construction of these measures for a long enough chunk of data. Such an outcome indeed takes place for random Monte Carlo simulations of hypothetical efficient markets with volatilities of real ones.

As before, Fig.1 as such is free of bias -- it shows you all the possible Fred values. Absence of the "benefit of hindsight" is thus ensured on the stage of Fig.1 analysis: the statement that it is more likely for an arbitrarily chosen Fred value to result in a positive correlation between reality and forecast is based on no particular Fred value and thus no choice is made with the benefit of hindsight. The benefit of hindsight will enter the game once a single value of Fred is chosen on the basis of Fig.1.

Finally, to the special topic of this post, the comparison of Step One and Step Three results. Since both studies are made on exactly the same data, the results are highly inter-independent. This means that the uncertainty bands shown do not represent independent uncertainties: a change in the input data, such as an extension of the simulated trading range, is likely to move the measurements for the individual Fred values, as well as the Step One and Step Three, in a correlation fashion.

The lack of advantage brought about by introducing a more complex Step Three algorithm is not too unexpected, as my intermarket correlation studies in forex showed the non-trivial correlations to die off within one, at most two hours of time lags, while this study deals with day data. The result may very well be different with other time scales, or with thoughtfully selected combinations of forex exchange rates. For now, I make a choice in favor of Step One as the less computationally complex one.

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Last Updated ( Tuesday, 09 March 2010 12:37 )