Stock Price Prediction Based on Fuzzy Logic
Zhejiang Univ., Hangzhou
DOI: 10.1109/ICMLC.2007.4370347 Conference: Machine Learning and Cybernetics, 2007 International Conference on, Volume: 3
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.
Available from: Mukesh M Goswami
- "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. "
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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.
Available from: dspace.ist.utl.pt
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ABSTRACT: Financial forecasting is an area of research which has been attracting a lot of attention recently from practitioners in the field of artificial intelligence. Apart from the economic benefits of accurate financial prediction, the inherent nonlinearities in financial data make the task of analyzing and forecasting an extremely challenging task. This paper presents a survey of more than 100 articles published over two centuries (from 1933 up to 2013) in an attempt to identify the developments and trends in the field of financial forecasting with focus on application of artificial intelligence for the purpose. The findings from the survey indicate that artificial intelligence and signal processing based techniques are more efficient when compared to traditional financial forecasting techniques and these techniques appear well suited for the task of financial forecasting. Some of the issues that need addressing are discussed in brief. A novel technique for selection of the input dataset size for ensuring best possible forecast accuracy is also presented. The results confirm the effectiveness of the proposed technique in improving the accuracy of forecasts.
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