Journal ArticleDOI
Expert system for predicting stock market timing using a candlestick chart
Kyung-Ho Lee,Geun-Sik Jo +1 more
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TLDR
Wang et al. as mentioned in this paper developed a candlestick chart analysis expert system, or a chart interpreter, for predicting the best stock market timing, which has patterns and rules which can predict future stock price movements.Abstract:
It has been one of the greatest challenges to predict the stock market. Since stock prices vary dramatically, it is important to determine when to buy and sell stocks in order to get high returns from stock investment. In this study, we have developed a candlestick chart analysis expert system, or a chart interpreter, for predicting the best stock market timing. The expert system has patterns and rules which can predict future stock price movements. Defined patterns are classified into five groups with respect to their meanings: falling, rising, neutral, trend-continuation and trend-reversal patterns. The experimental results revealed that the developed knowledge base could provide excellent indicators with an average hit ratio of 72% to help investors get high returns from their stock investment. Through experiments from January 1992 to June 1997, it was proven that the developed knowledge base was time- and field-independent.read more
Citations
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Journal ArticleDOI
Predicting stock price using fuzzy grey prediction system
TL;DR: A data mart is constructed to reduce the size of stock data and fuzzification techniques with the grey theory is combined to develop a fuzzy grey prediction as one of predicting functions in the system to predict the possible answer immediately.
Journal ArticleDOI
Forecasting the volatility of stock price index
TL;DR: This model demonstrates the utility of the neural network forecasting combined with time series analysis for the financial goods in forecasting the volatility of stock price index in two view points: deviation and direction.
Journal ArticleDOI
Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data.
Taewook Kim,Hayoung Kim +1 more
TL;DR: It is found that a candlestick chart is the most appropriate stock chart image to use to forecast stock prices, and prediction error can be efficiently reduced by using a combination of temporal and image features from the same data rather than using these features separately.
Journal ArticleDOI
Artificial neural networks with evolutionary instance selection for financial forecasting
TL;DR: The GA optimizes simultaneously the connection weights between layers and a selection task for relevant instances in artificial neural networks for financial data mining and applies the proposed model to stock market analysis.
Journal ArticleDOI
Mining stock price using fuzzy rough set system
TL;DR: This work proposed an effective method, a fuzzy rough set system to predict a stock price at any given time, and used it to predict the stronger rules of stock price and achieved at least 93% accuracy after 180 trials.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Proceedings ArticleDOI
Stock price pattern recognition-a recurrent neural network approach
K. Kamijo,T. Tanigawa +1 more
TL;DR: Recurrent neural networks were applied to the recognition of stock patterns, and a method for evaluating the networks was developed that is applicable to reducing mismatching patterns.
Journal ArticleDOI
Predicting stock market behavior through rule induction: an application of the learning‐from‐example approach*
Helmut Braun,John S. Chandler +1 more
TL;DR: An artificial intelligence-based rule-induction approach to the analysis of stock market prediction is presented, and rules predicting actual market movement performed better than rules predicting the analyst's calls and better than the analyst himself.
Book
Knowledge-Based Systems for Engineers and Scientists
TL;DR: Tools and languages ruled-based systems dealing with uncertainty object-oriented programming (OOP) machine learning systems for interpretation and diagnosis design and selection systems for planning systems for control future directions.
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