Previsão do mercado acionário por meio de redes neurais mlp e redes neurais kohonen em período de crise econômica
MIRANDA, André Pacheco. Stock market forecast through ann MLP and Kohonen ann at time of economic crisis. 2013. 105 f. Dissertação (Mestrado em Administração) - Universidade Federal de Santa Maria, Santa Maria, 2013.
Miranda, André Pacheco
Fluctuations in the stock market through economic crises, risks of deflation and liquidity traps are critical in the analysis of risk, which cause discrepancies in the execution of a particular scope in the equities market. The crisis in subprime insolvency in 2007/2008 which had a major impact on financial markets founded further discussions in relation to risk control in the decision making of investors. In the stock market risk analysis seeks to assist the investor in making decisions, for it makes use of statistical methods and tools to try to predict market movements. Based on these and previous statements in order to assist investors in making decisions through an economic crisis, this is an exploratory study aimed to develop and train two neural networks with differentiated learning without the problem of "black box" methods to compare which of the two has better forecast in periods of economic crisis. As input variables for the neural networks used the return of the volume of weekly Ibovespa in the period 12/08/2002 to 30/05/2011 and a setup developed from the Elliott Wave Theory. That is, these two neural networks were developed, trained and validated to predict market movements when it presents oscillations from an economic crisis. As mentioned earlier to validate the study compared the power of explanation of two methods before a point of probable attack. We conclude, therefore, that the analogy made for the creation of the theory of Elliott wave theory of psychological behavior of the masses and the Fibonacci sequence proved unable to provide for oscillations of the market in a series corresponding to an economic crisis. It was concluded, too, that neural networks with unsupervised learning using temporal variables as input variables has a higher prediction in training, but lower than most crucial step in the validation of systems.