Directional Prediction of Financial Time Series Using SVM and Wilson Loop Perceptron

The Wilson loop is indicative of the pathway encompassed within the market cocycle, which carries the coherent gauge field behavior present in the financial time series data. We enhance the capabilities of the support vector machine by integrating supplementary attributes through the incorporation of the knot and link characteristics of the Wilson loop, as derived from market microstructure contexts. This framework was employed to capture the financial market dynamics within time series data, with particular emphasis on the parallel transport of co-state between predictor and predictant transfer along the continuous behavior field’s predictive evolutionary lift path trajectory. We found that the average performance of Wilson loop perceptron in empirical analysis of a sample set of closed price of DE30 and EUR/USD exchange rate is 68.42% when compared with SVM which have 49.17%. Furthermore, we conclude from data analysis of statistical DM-test that has Wilson loop perceptron better performance than SVM in directional prediction of intraday financial time series.