Stock Market Prediction Using a Combination of Stepwise Regression Analysis, Differential Evolution-based Fuzzy Clustering, and a Fuzzy Inference Neural Network

Enke, David; Mehdiyev, Nijat

doi:10.1080/10798587.2013.839287

Cited by 46 publications

(24 citation statements)

References 32 publications

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“…One approach that can be used to deal with complex real-world problems is to integrate the use of artificial intelligence technologies with neuro-fuzzy techniques in order to combine their different strengths while overcoming a single technology's weakness. Such hybrid models often provide better results than the ones achieved with the use of each technique in isolation [9]. Considering the dynamic, nonstationary, and complex nature of the CPI data, along with its uncertain environment, innovations in forecasting methodologies and improvements in prediction accuracy performance, researchers may prefer soft computing techniques in general, and more recent neuro-fuzzy techniques in particular, over standard qualitative and linear quantitative models in order to achieve more accurate calculations.…”

Section: Research Purposementioning

confidence: 97%

A Hybrid Neuro-fuzzy Model to Forecast Inflation

Enke

Mehdiyev

2014

Procedia Computer Science

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Section: Research Purposementioning

confidence: 97%

A Hybrid Neuro-fuzzy Model to Forecast Inflation

Enke

Mehdiyev

2014

Procedia Computer Science

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“…To screen out optimal wavelengths, a feature extraction method, stepwise regression analysis was performed in this study. Significance level and F-test values, F-to-enter, and F-to-remove are used as the main criteria in variable selection (Enke & Mehdiyev, 2013). In stepwise regression analysis, variables are entered and removed in a stepwise manner based on the computed F-tests, until there is no justifiable reason to enter or remove more.…”

Section: Wavelengths Selectionmentioning

confidence: 99%

Diagnosis of CTV-Infected Leaves Using Hyperspectral Imaging

Xie¹,

Qian²,

Yang³

et al. 2015

Intelligent Automation & Soft Computing

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Hyperspectral reflectance images of healthy and diseased leaves infected with different isolates of Citrus tristeza virus (CTV) including TRL514, CT30, CT32 and CT11A were collected in the visible and near-infrared region of 400 -1000 nm. Average reflectance spectrum was generated from each hyperspectral image individually obtained from 60 healthy and 240 CTV-infected leaves. The spectra were transformed with 15-point Savitzky Golay second derivative. Then principal component analysis was performed on the transformed data in order to reduce the dimension of data. Comparative analysis was performed among supervised classification models, including back-propagation neural network (BPNN), linear discriminant analysis (LDA) and Mahalanobis distance (MD). When the second derivative spectra were analyzed, classifier models including BPNN, LDA and MD can discriminate the healthy and CTV-infected leaves with the highest classification accuracies of 100% in the spectral range of 400-1000 nm and 760 -1000 nm. Nine optimal wavelengths (405, 424, 920, 947, 957, 972, 978, 980, and 998 nm) selected by stepwise regression resulted in 97.33% total classification accuracy for differentiation of healthy and CTV-infected leaves and showed great potential in CTV diagnosis. However, the overall classification accuracy of different CTV isolates infected leaves resulted in 70% based on the MD model using the selected optimal wavelengths. Further study is required to find out whether the method is suitable for CTV detection under field conditions.

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“…The proposed method was employed to make one‐step forward decision. To forecast the direction of S&P 500 stock market prices, Enke and Mehdiyev () proposed the architecture of the fuzzy inference neural network where stepwise regression analysis was first employed to reduce the variable size and then differential evolution‐based fuzzy clustering method was used to generate data clustering groups of the input. The proposed model was also compared with other neural network and regression models.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

Dosdoğru

2019

Manage Decis Econ

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In this study, a new methodology is proposed to automatically determine six parameters of artificial neural network using population-based metaheuristics. We considered following three issues: What is the effect of used metaheuristic on performance? Which parameters are mostly selected? Is there a difference between the forecasting results when using stationary or nonstationary dataset that are selected according to the augmented Dickey-Fuller test statistics? Based upon results of performance measures, proposed method leads to significant opportunities to forecast stock market more effectively. We also expect proposed methodology can provide remarkable advantages for other complex, dynamic, and nonlinear forecasting problems. | INTRODUCTIONStock market is one of the most important investment ways due to the potential of earning higher returns. It is also considered as one of the key indicators of economic growth of countries. Over the years, researchers, practitioners, and investors analyzed the stock market, the stocks, and the relationships. However, stock market has high complexity and nonlinear constraints to grasp. Although anybody can own stocks to perform the aspired profit, many investors find stock market hard to detect which stock to buy, or when to buy and sell stocks. Furthermore, it is so difficult to exactly say when the stock market price will increase, how much it will increase or how long the increase of price will remain (Albeladi & Abdullah, 2018).Due to high volatilities and noises in stock market, unilateral traditional forecasting method and analysis cannot meet investors' need any more. Hence, new methods with recent advances in artificial intelligence have been proposed to extract useful information from stock market data. Depending on the nature of the stock market, a particular method can be used to gain profit. However, there is no single model exist in literature. Each method has its own strengths and limitations in terms of performance and reliability. In this case, artificial intelligence methods can be integrated to combine the strength of methods and overcome a single method's weakness. Especially, artificial neural network (ANN) method can be proposed for stock market forecasting in order to improve investors' decision support quality. In this paper, genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) are integrated with ANN to clarify the dynamic and nonstationary nature of the stock market. GA, DE, and PSO are population-based metaheuristics. The group of populationbased metaheuristics inherently has some significant advantages. First, they ensure information about the "surface" of an objective function.Second, population-based metaheuristics are less sensitive to "improper" pathways of certain individual solutions. Finally, they improve the probability of attaining the global optimum (Feoktistov, 2006). This study involves the following tasks: (a) determine the relevant technical indicators for the stocks, (b) determine the proper transfe...

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Stock Market Prediction Using a Combination of Stepwise Regression Analysis, Differential Evolution-based Fuzzy Clustering, and a Fuzzy Inference Neural Network

Cited by 46 publications

References 32 publications

A Hybrid Neuro-fuzzy Model to Forecast Inflation

A Hybrid Neuro-fuzzy Model to Forecast Inflation

Diagnosis of CTV-Infected Leaves Using Hyperspectral Imaging

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

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