Stock markets are complex. Their dramatic movements, and unexpected booms and crashes, dull all traditional tools. The major concern of the study is to develop a system that can predict future prices in the stock markets by taking samples of past prices. The model elicits, from historical data price, some of the rules which govern the market, and shows that rules which are drawn from a particular stock are to some extent independent of that stock, and can be generalized and applied to other stocks regardless of specific time or industrial field. The experimental results of this study in the duration of 3 months reveal that the model can correctly predict the direction of the market with an average hit ratio of 87%. In addition to daily prediction, this model is also capable of predicting the open, high, low, and close prices of desired stock weekly and monthly.
"The results indicate the superiority of neural networks, liner method , and then the multiple regression method. YANG  has proposed a Fuzzy Logic based method for the short term and long term stock price predictions. The system proceeds through two steps 1) Clustering of Input data 2) Specification of output. "
[Show abstract][Hide abstract] ABSTRACT: Stock market analysis and prediction has been one of the widely studied and most interesting time series analysis problems till date. Many researchers have employed many different models, some of them are linear statistic based while some non linear regression, rule, ANN, GA and fuzzy logic based. In this paper we have proposed a novel model that tries to predict short term price fluctuation, using candlestick analysis. This is a proven technique used for short term prediction of stock price fluctuation and market timing since many years. Our approach has been hybrid that combines self organizing map with case based reasoning to indemnify profitable patterns (candlestick) and predicting stock price fluctuation based on the pattern consequences.
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