Efficient prediction of exchange rates with low complexity artificial neural network models

Authors:
Ritanjali Majhi;G. Panda;G. Sahoo
Affiliations:
Centre of Management Studies, National Institute of Technology, Warangal, India;Department of Electrical and Ceramic Engineering, National Institute of Technology, Rourkela 769008, India;Department of Computer Science and Engineering BIT, Mesra, Ranchi, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 7
Cited 8

Bankruptcy prediction using neural networks

Decision Support Systems - Special issue on neural networks for decision support
Financial Prediction Using Neural Networks

Financial Prediction Using Neural Networks
Intelligent Systems and Financial Forecasting

Intelligent Systems and Financial Forecasting
Forecasting stock market movement direction with support vector machine

Computers and Operations Research
A fusion model of HMM, ANN and GA for stock market forecasting

Expert Systems with Applications: An International Journal
Identification of nonlinear dynamic systems using functional linkartificial neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A globally convergent adaptive predictor

Automatica (Journal of IFAC)

An Effective Hybrid GA---PP Strategy for Artificial Neural Network Ensemble and Its Application Stock Market Forecasting

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Expert Systems with Applications: An International Journal
A prediction interval-based approach to determine optimal structures of neural network metamodels

Expert Systems with Applications: An International Journal
A systematic design for coping with model risk

Expert Systems with Applications: An International Journal
Using artificial neural network models in stock market index prediction

Expert Systems with Applications: An International Journal
Fluctuation prediction of stock market index by Legendre neural network with random time strength function

Neurocomputing
Semi-parametric smoothing regression model based on GA for financial time series forecasting

ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
New robust forecasting models for exchange rates prediction

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

In recent years forecasting of financial data such as interest rate, exchange rate, stock market and bankruptcy has been observed to be a potential field of research due to its importance in financial and managerial decision making. Survey of existing literature reveals that there is a need to develop efficient forecasting models involving less computational load and fast forecasting capability. The present paper aims to fulfill this objective by developing two novel ANN models involving nonlinear inputs and simple ANN structure with one or two neurons. These are: functional link artificial neural network (FLANN) and cascaded functional link artificial neural network (CFLANN). These have been employed to predict currency exchange rate between US$ to British Pound, Indian Rupees and Japanese Yen. The performance of the proposed models have been evaluated through simulation and have been compared with those obtained from standard LMS based forecasting model. It is observed that the CFLANN model performs the best followed by the FLANN and the LMS models.