Fluctuation prediction of stock market index by adaptive evolutionary higher order neural networks

2016 ◽  
Vol 2 (2/3/4) ◽  
pp. 229 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Higher Order ◽  
Stock Market Index ◽  
Market Index ◽  
Higher Order Neural Networks

Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior

2016 ◽  
pp. 174-191 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Learning Process ◽  
Real Life ◽  
Network Models ◽  
Higher Order ◽  
Market Behavior ◽  
Neural Network Models ◽  
Efficient Prediction ◽  
Higher Order Neural Networks

This chapter presents two higher order neural networks (HONN) for efficient prediction of stock market behavior. The models include Pi-Sigma, and Sigma-Pi higher order neural network models. Along with the traditional gradient descent learning, how the evolutionary computation technique such as genetic algorithm (GA) can be used effectively for the learning process is also discussed here. The learning process is made adaptive to handle the noise and uncertainties associated with stock market data. Further, different prediction approaches are discussed here and application of HONN for time series forecasting is illustrated with real life data taken from a number of stock markets across the globe.

COA-HONN: cooperative optimization algorithm based higher order neural networks for stock fore casting

2019 ◽  
Vol 13 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Mohd Dilshad Ansari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Market ◽  
Optimization Algorithm ◽  
Time Budget ◽  
Statistical Significance ◽  
Higher Order ◽  
Optimization Techniques ◽  
Functional Link ◽  
Higher Order Neural Networks

A broad range of nature inspired optimization techniques are proposed and applied to forecast stock market. They performed notably differently across the stock market datasets. This article attempts to construct a cooperative optimization algorithm (COA) framework as an alternative of employing solitary algorithm. The COA considers genetic algorithm and chemical reaction optimization as constituent techniques. The framework executes each constituent algorithm with a fraction of the whole computation time budget and encourages interaction between them so that they can be benefited from each other. A component technique compares its best result so far obtained with the best established result from the other at regular interval. If the quality of the established result is better than its own best result, it replaces its solution by the received one. The COA is used to adjust the weight and bias vectors of two higher order neural networks such as Pi-Sigma neural network (PSNN) and functional link artificial neural network (FLANN) separately hence, forming two COA-HONN hybrid models. The models are evaluated on forecasting daily closing prices of five real stock datasets. The experimental results confirm that the COA approach enhances the prediction accuracy over individual algorithm. We conducted the Deibold-Mariano test to check the statistical significance of the proposed models, and it was found to be significant. Hence, the proposed approach can be used as a promising tool for stock market forecasting.

Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior

2016 ◽  
pp. 553-570
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Learning Process ◽  
Real Life ◽  
Network Models ◽  
Higher Order ◽  
Market Behavior ◽  
Neural Network Models ◽  
Efficient Prediction ◽  
Higher Order Neural Networks

This chapter presents two higher order neural networks (HONN) for efficient prediction of stock market behavior. The models include Pi-Sigma, and Sigma-Pi higher order neural network models. Along with the traditional gradient descent learning, how the evolutionary computation technique such as genetic algorithm (GA) can be used effectively for the learning process is also discussed here. The learning process is made adaptive to handle the noise and uncertainties associated with stock market data. Further, different prediction approaches are discussed here and application of HONN for time series forecasting is illustrated with real life data taken from a number of stock markets across the globe.

Stock market index prediction using neural networks

1994 ◽  
Author(s):  
Darmadi Komo ◽  
Chein-I Chang ◽  
Hanseok Ko
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Stock Market Index ◽  
Market Index
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