Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified Differential Harmony Search Technique

Dash, Rajashree; Dash, P. K.

doi:10.1016/j.eswa.2016.01.016

Cited by 55 publications

(20 citation statements)

References 28 publications

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“…Dash and Dash (2016) developed a self‐evolving recurrent neuro‐FIS with modified differential harmony search for stock price prediction. The recurrent structure has two types of feedback loops in the temporal strength firing and output layers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The last category is based on fuzzy systems (Anbalagan & Maheswar, 2015; Dash & Dash, 2016; Enke, Grauer, & Mehdiyev, 2011; George, Dimitrakakis, & Zopounidis, 2011). The main advantage of these methods is that they have low prediction error and better prediction accuracy than other methods do (Enke et al, 2011); however, they are prone to a high misclassification percentage rate (Anbalagan & Maheswar, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Mohamed

Ahmed

Mehdi

et al. 2021

Intell Sys Acc Fin Mgmt

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Predicting stock price remains one of the challenges for investors' investment strategies. This study helps with accurate prediction and the main factors affecting variations in stock prices. It applies an adaptive neuro-fuzzy model on 58 listed firms from both the Abu Dhabi Securities Exchange and the Dubai Financial Market for the period 2014-2018 to estimate the predictive power of corporate performance measures and their significance. After examining four performance predictors-return on asset (ROA), return on equity (ROE), earning per share (EPS), and profit margin (PM)-the study finds that ROE is the most significant predictor and ROA is the least. EPS is the most influential profitability measure and PM the least.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Mohamed

Ahmed

Mehdi

et al. 2021

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

show abstract

“…In this section, we focus the review of methods and technical indicators utilized for forecasting of direction movement of stock index. As shown in Table 1 , an ANN model was used in some of the studies [ 18 , 25 , 29 , 30 ], whilst hybrid models were preferred in other studies [ 17 , 21 , 31 , 32 , 33 ] as displayed in Table 2 . In Table 1 .…”

Section: Literature Reviewmentioning

confidence: 99%

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Ecer

Ardabili

Band

et al. 2020

Entropy

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Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

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“…Fuzzy systems make decisions of the basis of human reasoning and ANNs are trained to learn from the previous experiences or data. Researchers have successfully implemented ANFIS to deal fitting, forecasting, regression and classification problems [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Online Estimation of Wind Turbine Tip Speed Ratio by Adaptive Neuro-Fuzzy Algorithm

Asghar¹,

Liu²

2018

ijacsa

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The efficiency of a wind turbine highly depends on the value of tip speed ratio during its operation. The power coefficient of a wind turbine varies with tip speed ratio. For maximum power extraction, it is very important to hold the tip speed ratio at optimum value and operate the variable-speed wind turbine at its maximum power coefficient. In this paper, an intelligent learning based adaptive neuro-fuzzy inference system (ANFIS) is proposed for online estimation of tip speed ratio (TSR) as a function of wind speed and rotor speed. The system is developed by assigning fuzzy membership functions (MFs) to the input-output variables and artificial neural network (ANN) is applied to train the system using back propagation gradient descent algorithm and least square method. During the training process, the ANN adjusts the shape of MFs by analyzing training data set and automatically generates the decision making fuzzy rules. The simulations are done in MATLAB for standard offshore 5 MW baseline wind turbine developed by national renewable energy laboratory (NREL). The performance of proposed neuro-fuzzy algorithm is compared with conventional multilayer perceptron feed-forward neural network (MLPFFNN). The results show the effectiveness of proposed model. The proposed system is more reliable for accurate estimation of tip speed ratio.

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Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified Differential Harmony Search Technique

Cited by 55 publications

References 28 publications

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Online Estimation of Wind Turbine Tip Speed Ratio by Adaptive Neuro-Fuzzy Algorithm

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