Alternative Neural Network Approaches for Enhancing Stock Picking Using Earnings Forecasts

  • Giuseppe Galloppo
  • Mauro Aliano
Part of the Palgrave Macmillan Studies in Banking and Financial Institutions book series (SBFI)


Interest in financial markets has increased in the last couple of decades, among fund managers, policy makers, investors, borrowers, corporate treasurers and specialized traders. Forecasting the future returns has always been a major concern for the players in stock markets and one of the most challenging applications studied by researchers and practitioners extensively. Predicting the financial market is a very complex task, because the financial time series are inherently noisy and non-stationary and more it is often argued that the financial market is very efficient. Fama (1970) defined efficient market hypothesis (EMH) where the idea is a market in which security prices at any time ‘fully reflect’ all available information both for firms’ production—investment decisions, and investors’ securities selection. Furthermore, in EMH context no investor is in a position to make unexploited profit opportunities by forecasting futures prices on the basis of past prices. On the other hand, a large number of researchers, investors, analysts, practitioners etc. use different techniques to forecast the stock index and prices. In the last decade, applications associated with artificial neural network (ANN) have drawn noticeable attention in both academic and corporate research.


Root Mean Square Error Stock Market Neural Network Model Stock Return Back Propagation Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Giuseppe Galloppo and Mauro Aliano 2013

Authors and Affiliations

  • Giuseppe Galloppo
  • Mauro Aliano

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