Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting

This research presents a study of intelligent stock price forecasting systems using interval type-2 fuzzy logic for analyzing Japanese candlestick techniques. Many intelligent financial forecasting models have been developed to predict stock prices, but many of them do not perform well under unstable market conditions. One reason for poor performance is that stock price forecasting is very complex, and many factors are involved in stock price movement. In this environment, two kinds of information exist, including quantitative data, such as actual stock prices, and qualitative data, such as stock traders\u27 opinions and expertise. Japanese candlestick techniques have been proven to be effective methods for describing the market psychology. This study is motivated by the challenges of implementing Japanese candlestick techniques to computational intelligent systems to forecast stock prices. The quantitative information, Japanese candlestick definitions, is managed by type-2 fuzzy logic systems. The qualitative data sets for the stock market are handled by a hybrid type of dynamic committee machine architecture. Inside this committee machine, generalized regression neural network-based experts handle actual stock prices for monitoring price movements. Neural network architecture is an effective tool for function approximation problems such as forecasting. Few studies have explored integrating intelligent systems and Japanese candlestick methods for stock price forecasting. The proposed model shows promising results. This research, derived from the interval type-2 fuzzy logic system, contributes to the understanding of Japanese candlestick techniques and becomes a potential resource for future financial market forecasting studies --Abstract, page iii

Fuzzy Logic-Based Japanese Candlestick Pattern Recognition and Financial Forecast

This paper discusses fuzzy-logic based Japanese candlestick pattern recognition and financial forecasting. Fuzzy logic is used to recognize candlestick patterns and is applied to finanical forecasting. Candlestick patterns recognized by fuzzy logic give impact to the new model presented in this paper, and the results of stock market forecasting. In this paper, the daily stock quote of Exxon Mobile, General Electric, General Motors, Google, Microsoft, and Wells Farge are used as input data sets. The output of the model is the forecast of the next days closing price. For the new model, IF-THEN rule-based fuzzy logic is used for recognizing basic candlestick patterns. The performance of the previous study (Kamo and Dagli, 2006) is used for comparison of the data. The results of the models are evaluated on the basis of mean squared error. Graphs and charts bisually illustrate the trend generated by the new model and the actual stock market movement

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\u27s 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

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