Recurrent artificial neural networks for forecasting of forward interest rates

Bouqata, Bouchra; Bensaid, A.; Palliam, Ralph

doi:10.1109/ijcnn.1999.830777

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…ANNs have been applied in a number of areas in the financial markets, including fore casting forward interest rates (Bouqata, Bensaid & Palliam, 1999), estimating general insurance reserves (Braun & Lai, 2005), evaluating credit risk (Fei & Zhigang, 2008), detecting credit card fraud (Sahin & Duman, 2011), analysing stock exchange movements (Abhishek et al, 2012;O'Connor & Madden, 2006), forecasting commodity prices (Kohzadi et al, 1996) and foreign exchange rates (Tan, undated), and predicting credit union distress and bankruptcy (Korol, 2013;Tsai & Wu, 2008;Saha, 2009;Lacher et al, 1995;Tan, undated).…”

Section: 5mentioning

confidence: 99%

A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets

2016

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No analytic procedures currently exist for determining optimal artificial neural network structures and parameters for any given application. Traditionally, when artificial neural networks have been applied to financial modelling problems, structure and parameter choices are often made a priori without sufficient consideration of the effect of such choices. A key aim of this study is to develop a general method that could be used to construct artificial neural networks by exploring the model structure and parameter space so that informed decisions could be made relating to the model design. In this study, a formal approach is followed to determine suitable structures and parameters for a Feed Forward Multi-layered Perceptron artificial neural network with a Resilient Propagation learning algorithm with a single hidden layer. This approach is demonstrated through the modelling of four South African economic variables, namely the average monthly returns on the money, bond and equity markets as well as monthly inflation. Artificial neural networks can be constructed on the aforementioned variables in isolation or, jointly, in an integrated model. The performance of a range of more traditional time series models is compared with that of the artificial neural network models. The results suggest that, on a statistical level, artificial neural networks perform as well as time series models at forecasting the returns for financial markets. Hybrid models, combining artificial neural networks with the time series models, are constructed, trained and tested for the money market and for the rate of inflation. They appear to add value to the time series models when forecasting inflation, but not for the money market.

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Section: 5mentioning

confidence: 99%

A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets

2016

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Prediction Model of Stock Market Returns Based on Wavelet Neural Network

Zhao

Zhang

2008

2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application

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Financial forecasting is one of the domineering fields of research, where investor's money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann's development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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Recurrent artificial neural networks for forecasting of forward interest rates

Cited by 2 publications

References 8 publications

A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets

A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets

Prediction Model of Stock Market Returns Based on Wavelet Neural Network

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