Trading ebook Finance - Neural Prediction of Weekly Stock Market Index

Recurrent neural network approach kamijo and tanigawa

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Unformatted text preview: s or small fluctuations. Recurrent neural network approach Kamijo and Tanigawa [11] propose the use of an Elman recurrent net for predicting the future stock prices using extracted features from past daily high, low and closing stock prices. The method used tries to extract triangle patterns in stock prices which are seen graphically by plotting the daily high, low and closing prices. A triangle refers to the beginning of a sudden stock price rise after which the high and low prices appear and the price oscillates for some period before the lines converge. The neural network is trained to recognize such triangle patterns in the stock prices. As such, it is mainly a categorization approach to recognize whether a pattern is a triangle or not. Such knowledge can be useful in judging whether a price rise is permanent. Prediction based on past stock values and other fundamental indicators The second category of research assumes that other fundamental factors such as present interest rates, bond rate and foreign exchange rates affect...
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This note was uploaded on 07/20/2012 for the course ECON 203 taught by Professor Girishdev during the Spring '12 term at Indian Institute of Technology, Kharagpur.

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