A GA-Artificial Neural Network Hybrid System for Financial Time Series Forecasting

Nair, Binoy B.; Sai, S. Gnana; Naveen, A.; Lakshmi, A. M. Sree; Venkatesh, G.; Mohandas, V. P.

doi:10.1007/978-3-642-20573-6_91

Cited by 17 publications

(14 citation statements)

References 18 publications

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“… is a vector for weights between n hidden nodes and output node and  is a vector for weights between m input nodes and hidden node while, , [ (4) IV. PROPOSED COMBINATION METHODS…”

Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

“…Currency exchange rate is outlined as the rate on foreign currency and demonstrates the foreign-currency price of the currency of the country within which value is calculated [2], [3]. In trendy FTS, predicting, exchange ate have been recognized as one of the most difficult applications [4]. Thus, several numbers of models are designed to support the stakeholders for intelligence precise predictions.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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Abstract-Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study is to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated, namely, ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model is examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.

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“… is a vector for weights between n hidden nodes and output node and  is a vector for weights between m input nodes and hidden node while, , [ (4) IV. PROPOSED COMBINATION METHODS…”

Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…More recently, Nair et al . () propose a hybrid genetic algorithm neural network which, when compared with benchmark models, outperforms them, displaying superior accuracy and overall performance. Nair et al .…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and Trading the English and German Stock Markets with Novelty Optimization Techniques

Karathanasopoulos

Skindilias

Lo³

2016

Journal of Forecasting

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The motivation for this paper was the introduction of novel short‐term models to trade the FTSE 100 and DAX 30 exchange‐traded funds (ETF) indices. There are major contributions in this paper which include the introduction of an input selection criterion when utilizing an expansive universe of inputs, a hybrid combination of partial swarm optimizer (PSO) with radial basis function (RBF) neural networks, the application of a PSO algorithm to a traditional autoregressive moving model (ARMA), the application of a PSO algorithm to a higher‐order neural network and, finally, the introduction of a multi‐objective algorithm to optimize statistical and trading performance when trading an index. All the machine learning‐based methodologies and the conventional models are adapted and optimized to model the index. A PSO algorithm is used to optimize the weights in a traditional RBF neural network, in a higher‐order neural network (HONN) and the AR and MA terms of an ARMA model. In terms of checking the statistical and empirical accuracy of the novel models, we benchmark them with a traditional HONN, with an ARMA, with a moving average convergence/divergence model (MACD) and with a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the FTSE 100 and DAX 30 ETF time series over the period January 2004 to December 2015 using the last 3 years for out‐of‐sample testing. Finally, the empirical and statistical results indicate that the PSO‐RBF model outperforms all other examined models in terms of trading accuracy and profitability, even with mixed inputs and with only autoregressive inputs. Copyright © 2016 John Wiley & Sons, Ltd.

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“…The RBF model outperform all the other seven traditional recurrent neural network. Moreover, Nair et al (2011) propose a hybrid GA neural network which, when compared with benchmark models, outperforms displaying superior accuracy and overall performance. Nair et al (2011) forecasts one day ahead and uses closing prices from the FTSE100, BSE Sensex, Nikkei 225, NSE-Nifty and DJIA as inputs for his models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, Nair et al (2011) propose a hybrid GA neural network which, when compared with benchmark models, outperforms displaying superior accuracy and overall performance. Nair et al (2011) forecasts one day ahead and uses closing prices from the FTSE100, BSE Sensex, Nikkei 225, NSE-Nifty and DJIA as inputs for his models. Lastly, Karathanasopoulos et al (2013b) have used a sliding window approach which combines adaptive differential evolution and support vector regression for forecasting and trading the FTSE100.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and trading the English stock market with novelty optimization techniques

Karathanasopoulos

2016

Econom. bus. letters

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The motivation of this paper is to introduce a short term adaptive model (Partial Swarm Optimizer) combined with linear and nonlinear models when applied to the task of forecasting and trading the daily closing returns of the FTSE100 exchange traded funds (ETFs). This is done by benchmarking its results with a higher order neural network (HONN), a recurrent neural network (RNN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD), plus a buy and hold strategy. More specifically, the trading performance of all models is investigated in forecast and trading simulations on the FTSE 100 ETF time series over the period January 2000 to June 2016 using the last two years for out-of-sample testing. As it turns out, the proposed adaptive models do remarkably well and outperform its benchmarks in terms of correct directional change and trading performance.

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A GA-Artificial Neural Network Hybrid System for Financial Time Series Forecasting

Cited by 17 publications

References 18 publications

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Modelling and Trading the English and German Stock Markets with Novelty Optimization Techniques

Modelling and trading the English stock market with novelty optimization techniques

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