By Jason E. Kutsurelis
This examine examines and analyzes using neural networks as a forecasting device. in particular a neural network's skill to foretell destiny tendencies of inventory marketplace Indices is validated. Accuracy is in comparison opposed to a conventional forecasting strategy, a number of linear regression research. eventually, the chance of the model's forecast being right is calculated utilizing conditional chances. whereas purely in brief discussing neural community thought, this study determines the feasibility and practicality of utilizing neural networks as a forecasting software for the person investor. This learn builds upon the paintings performed by means of Edward Gately in his publication Neural Networks for monetary Forecasting. This study validates the paintings of Gately and describes the advance of a neural community that completed a 93.3 percentage chance of predicting a marketplace upward push, and an 88.07 percentage likelihood of predicting a industry drop within the S&P500. It was once concluded that neural networks do have the aptitude to forecast monetary markets and, if thoroughly proficient, the person investor may gain advantage from using this forecasting software.
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Additional info for Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy
The actual and predicted values from both the Close and Percent networks were placed into one spreadsheet. The predicted closing value of the S&P500 index was calculated using the predicted % change provided by the Percent Network. This allowed comparison of a single type of data between both networks. The two predicted closing values where 25 compared graphically and statistically. This completed the neural network portion of the research. A more traditional statistical forecasting tool is regression analysis.
If the dates were not equal, Excel placed a 1 in a row. This function was copied down the entire data set and then summed at the bottom. A sum of zero represented no date integrity discrepancies. A sum of anything greater than zero indicated that a date discrepancy existed. The sum was zero for all indexes 19 with the exception of *CRB. Within *CRB it was found that one trading day was missing. Instead of deleting the index, the trading day was added. The average closing value of the day proceeding and following the missing day was chosen to represent the missing day.
Bayes’ Theorem states that a probability depends upon the environment in which it is based. A conditional probability is stated as given X what is the probability of Y or P Y X . First the researcher had to find something that could easily be identified in the environment. It would be tedious to try and calculate probabilities for individual S&P500 closing values or individual percent changes. However, one could calculate the number of times the market rose or fell; this is potentially enough information for the investor.
Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy by Jason E. Kutsurelis