Abstract
This article studies the relationship between popular technical analysis indicators and the returns of the Italian S&P MIB, active as the index of the Borsa Italiana until 2009. The study innovates by applying a mixed analysis that combines classic signals offered by the indicators with the power of neural networks. The neural algorithms are applied to study the target technical analysis indicators with more accurate regression power and efficient error minimization. Through a simple hidden-layer, back-propagation algorithm, regressions give interesting results in terms of the forecasting potential of the analyzed indicators. The final step of the study is the conclusions section, commenting on results and summarizing the indications from the multivariate stage analysis. The article also comments on the power of the indicators to reveal potential investment opportunities. The results of the study are backdated but still very significant considering the revived interest of the financial community toward the application of adaptive systems to the solution of financial issues.
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