Abstract
In this work we perform an automatic data survey to draw up an optimum portfolio, and to automate the one year forecast of a portfolio’s payoff and risk, showing the advantages of using formally grounded models in portfolio management and adopting a strategy that ensures, a high rate of return at a minimum risk. The use of neural networks provides an interesting alternative to the statistical classifier. We can take a decision on the purchase or sale of a given asset, using a neural network to classify the process into three decisions: buy, sell or do nothing.
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López, V.F., Alonso, L., Moreno, M.N., Segrera, S., Belloso, A. (2007). A System for Efficient Portfolio Management. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_98
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DOI: https://doi.org/10.1007/978-3-540-77226-2_98
Publisher Name: Springer, Berlin, Heidelberg
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