Hybrid Approach: Neural Networks and Japanese Candlestick Method for Financial Forecast
This paper shows the experimental study of the cnadlestick method in the hybrid financial forecasting models. A committee machine with the Generalized Regression Neural Netowrok (GRNN) experts is the primary tool that handles the input data, and the candlestick method is introduced to the model using gating networks. This introduction of the candlestick method into stock quotes of Exxon Moblie, General Electrics, General Motors, Google, Microsoft, and Wells Fargo are used as input data sets. The output of the model is the forecast of the next day's closing proce. For the purpose of comparison, the performance of a sinple GRNN-based forecasting model is shown. The results of their forecasts are evaluated on the basis of the mean squared error.
T. Kamo and C. H. Dagli, "Hybrid Approach: Neural Networks and Japanese Candlestick Method for Financial Forecast," Intelligent Systems Through Artificial Neural Networks Smart Engineering Systems Design, American Society of Mechanical Engineers (ASME), Jan 2006.
Engineering Management and Systems Engineering
Keywords and Phrases
Candlestick Method; Data Sets; Generalized Regression Neural Network (GRNN); Hybird Financial Forcasting Models
Article - Conference proceedings
© 2006 American Society of Mechanical Engineers (ASME), All rights reserved.