Hybrid Approach to the Japanese Candlestick Method for Financial Forecasting


This paper discusses an experimental study of the Japanese candlestick method as used in hybrid stock market forecasting models. Two models are presented in this paper. Model 1 is a committee machine with simple generalized regression neural networks (GRNN) experts. This model also has a simple gating network. Model 2 has a similar committee machine along with a hybrid type gating network that contains fuzzy logic. Model 1 was developed to introduce the candlestick method and examine whether using the candlestick method improves performance. Model 2 is developed to determine whether the application of fuzzy logic could improve the former model. This model uses standard IF-THEN rules based fuzzy logic. In the experiment, a few simple Japanese candlestick patterns are integrated into the models. Both models use the same simple candlestick patterns to provide a basis for comparison. The Japanese candlestick method is implemented in the gating network. Model 1 uses features of candlestick patterns in the gating network. Model 2 uses candlestick patterns for recognizing the strength of market conditions. To investigate the performance of these models, the daily stock quotes of Hewlett-Packard, Bank of America, Ford, DuPont, and Yahoo are used as input data sets. The performance of the models was satisfactory based on the mean squared error.


Engineering Management and Systems Engineering

Keywords and Phrases

Committee Machine; Financial Forecasting; Gating Networks; Neural networks (Computer science)

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Document Type

Article - Journal

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