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  • Using Neural Networks and Genetic Algorithms to Predict Stock Market Returns

    About The File:
    File Format | PDF
    File Size | 1.08 MB
    Pages | 166
    Language | English
    Category | Stock market
    Description:  In this study we attempt to predict the daily excess returns of FTSE 500 and S&P 500 indices over the respective Treasury Bill rate returns. Initially, we prove that the excessreturns time series do not fluctuate randomly. Furthermore we apply two different types of prediction models: Autoregressive (AR) and feed forward Neural Networks (NN) to predict the excess returns time series using lagged values. For the NN models a Genetic Algorithm is constructed in order to choose the optimum topology. Finally we evaluate the prediction models on four different metrics and conclude that they do not manage tooutperform significantly the prediction abilities of naï ve predictors.

    It is nowadays a common notion that vast amounts of capital are traded through the Stock Markets all around the world. National economies are strongly linked and heavily influenced of the performance of their Stock Markets. Moreover, recently the Markets have become a more accessible investment tool, not only for strategic investors but for common people as well. Consequently they are not only related to macroeconomic parameters, but they influence everyday life in a more direct way. Therefore they constitute a mechanism which has important and direct social impacts.

    The characteristic that all Stock Markets have in common is the uncertainty, which is related with their short and long-term future state. This feature is undesirable for the investor but it is also unavoidable whenever the Stock Market is selected as the investment tool. The best that one can do is to try to reduce this uncertainty. Stock Market Prediction (or Forecasting) is one of the instruments in this process.
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