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Financial prediction using neural networks by Joseph S. Zirilli

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Published by International Thomson Computer Press in New York .
Written in English

Subjects:

  • Speculation,
  • Neural networks (Computer science),
  • Futures market,
  • Investment analysis

Book details:

Edition Notes

Includes bibliographical references and index.

StatementJoseph S. Zirilli.
Classifications
LC ClassificationsHG6015 .Z57 1996
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL1000714M
ISBN 101850322341
LC Control Number96039640

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PDF | Approaches for performing trend analysis through the use of neural networks. This book compares the results of experiments on various types of | Find, read and cite all the research you /_Financial_Prediction_Using_Neural_Networks. Exchange Rate Movement Year Book , Henley Centre for Forecasting, London Economic prediction using neural networks: The case of IBM daily stock returns, IEEE Int. Conf. on Neural Liu X. () Financial Prediction Using Neural Networks. In: Neural Networks for Identification, Prediction and Control. Springer, London. https Neural Networks and the Financial Markets It seems that you're in USA. We have a Financial Prediction Modelling: Summary and Future Avenues. Neural Networks and the Financial Markets Book Subtitle Predicting, Combining and Portfolio Optimisation  › Computer Science › Artificial Intelligence. Neural Networks and the Financial Markets Predicting, Combining and Portfolio Optimisation. Editors Search within book. Front Matter. Pages i-xiii. PDF. Introduction to Prediction in the Financial Markets. Front Matter. Pages PDF. Introduction to the Financial Markets. John G. Taylor. Pages

From the selected asset, the technique of artificial neural networks with a multilayer perceptron with regression configured with 3 layers (21,85,2) was used, using a logistic activation function /_Financial_forecasting_with_neural_networks.   Neural Networks For Financial Forecasting Siew Lan Loo Results of forecasting using financial data are particularly good [LapFar87, Schöne9O, ChaMeh92]. In contrast, traditional statistical Prediction Accuracy Of RuleD Networks Prediction Accuracy Of RuleT Networks sNI Networks And Relative Threshold Prediction Index   Get Book. Book Description: Neural Networks And The Financial Markets by Jimmy Shadbolt, Neural Networks And The Financial Markets Book available in PDF, EPUB, Mobi Format. Download Neural Networks And The Financial Markets books, This volume looks at financial prediction from a broad range of perspectives. It covers: the economic arguments   Stock Market Prediction/Stock Market Index Prediction. Predictions for stock market indices and stock values are handled by the neural networks using the historic data and predicting based on different parameters. The prediction accuracy is enhanced by the choice of variables and the information used for ://

Abstract: The use of neural networks in financial market prediction presents a major challenge to the design of effective neural network predictors and classifiers. In this paper, the author examines several neural networks to evaluate their capability in prediction and in trend estimation which is treated as a classification ://   Financial Time Series Prediction using Spiking Neural Networks David Reid*, Abir Jaafar Hussain, Hissam Tawfik. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, L16 9JD, UK School of Computing and Mathematical Sciences Liverpool John Moores University Liverpool, L3 3AF, UK Review of neural networks -- 2. Introduction to the futures markets -- 3. Introduction to technical analysis -- 4. Survey of neural network literature on financial prediction -- 5. Basic strategy for trend prediction -- 6. A mechanical neural net position trading system -- 7. Advanced feature extraction techniques for trend prediction   Abstract. Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community for their superior predictive properties including robustness to over ://?abstract_id=