Forecasting Financial Markets Using Neural Networks: An by Jason E. Kutsurelis

By Jason E. Kutsurelis

This study examines and analyzes using neural networks as a forecasting device. particularly a neural network's skill to foretell destiny developments of inventory industry Indices is confirmed. Accuracy is in comparison opposed to a conventional forecasting strategy, a number of linear regression research. ultimately, the likelihood of the model's forecast being right is calculated utilizing conditional percentages. whereas purely in short discussing neural community conception, this examine determines the feasibility and practicality of utilizing neural networks as a forecasting software for the person investor. This examine builds upon the paintings performed through Edward Gately in his booklet Neural Networks for monetary Forecasting. This examine validates the paintings of Gately and describes the improvement of a neural community that accomplished a 93.3 percentage chance of predicting a marketplace upward push, and an 88.07 percentage chance of predicting a industry drop within the S&P500. It used to be concluded that neural networks do have the aptitude to forecast monetary markets and, if thoroughly knowledgeable, the person investor may benefit from using this forecasting device.

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Extra info for Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy

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The number of times the market rose and fell was calculated. Next, the accuracy of each model was calculated. The output of each model for the out of sample data was used. The if-then technique was used to identify when both the model forecast and the actual data agreed or disagreed as to a market rise or fall. This data could be entered into Bayes' theorem to obtain the conditional probability. However the accuracy of the model was adjusted using the historical environmental data as described by Professor Terasawa.

The final test of the validity of the multiple regression model is to verify there is no multicollinearity between the independent variables. According to Levine et al (1997), when two independent variables are highly collinear they can cause the regression coefficients to fluctuate drastically if one or both are included in the model. It is difficult to separate the effect of to two collinear independent variables on the dependant variable. The measure of collinearity is the Variance Inflationary Factor (VIF).

Therefore, the GSM C and Lag of GSM C was added to the Close Network causing the data window to match the Percent Network. All data prior to July 18, 1996 was discarded. In the Extraction Module, every 5th data set was chosen to represent the test or out of sample data. Both models were now training on identical raw data. After training, the network was applied to the out of sample data. The results showed a slight decrease in r 2 value but fully 100 percent of the predictions were within five percent of the actual closing value.

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