Predicting Stock Market Trends for Japanese Candlestick Using Cloud Model

Abstract

Cloud model covers the randomness gap in fuzzy logic model and represents the uncertainty transformation between two different concepts. First concept is the linguistic term that represent the qualitative mean. While the second is the crisp term which represent the quantitative mean. The proposed work presents promising model which combines cloud model, fuzzy time series, and Heikin-Ashi candlestick to predict and confirm accurate stock trend. The model solves several challenging such as: nonlinearity, uncertainty and noises in stock market trend. Heikin-Ashi Candlesticks are an extended branch of Japanese candlesticks, such candlestick filters out stock noise and effort to highlight the trend. Heikin-Ashi Candlestick is constructed by calculating averages of the previous and current period prices. Cloud model handle the ambiguous and uncertainty in the Japanese candlestick definitions (qualitative information) and actual stock prices (quantitative data). It is applied to build membership functions by handling the uncertainty and vagueness of the stock historical data. Then the suggested model constructs dynamic weighted fuzzy logical relationships based on the membership functions to predict the next open and close prices of the stock as well as the high and low values. Finally it constructs the next Heikin-Ashi Japanese candlestick pattern that clarify the trend direction based on the patterns sequence. The imperial evaluation proves that the proposed model has high forecasting accuracy and is feasible to be implemented.