A Neural Network Approach to Predicting Stock Exchange Movements using External Factors

O’Connor, Niall; Madden, Michael G.

doi:10.1007/1-84628-224-1_6

Cited by 19 publications

(17 citation statements)

References 5 publications

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“…The empirical analysis shows that out-of-simple volatility forecasts of the SNN are superior to forecasts of traditional linear methods (GARCH) and also better than merely assuming a conditional Gaussian distribution. O'Connor and Madden [12] evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates. In the experiments presented, basing trading decisions on a neural network trained on a range of external indicators result in a return on investment of 23.5% per annum, during a period when the DJIA index grew by 13.03% per annum.…”

Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

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Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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show abstract

“…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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“…1 there is a way such as (3-4-3-1) which is used in this paper and some other researches (O'Connor & Madden, 2006). This way of representation says that the neural network has 3 inputs and two hidden layers with 4 and 3 nodes and 1 output node.…”

Section: Figmentioning

confidence: 99%

“…This evaluation method is widely used in the literature of exchange rate prediction (Yu et al, 2005;Yu et al, 2009;Yao, & Tan, 2000;O'Connor, & Madden, 2006). This method is called directional success or directional status.…”

Section: Forecast Evaluation In Financial Time Seriesmentioning

confidence: 99%

Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

Fathian¹,

Kia²

2012

MSL

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In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

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A Neural Network Approach to Predicting Stock Exchange Movements using External Factors

Cited by 19 publications

References 5 publications

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

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

Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

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