Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction

Hussain, Walayat; Merigó, José M.; Raza, Muhammad Raheel

doi:10.1002/int.22732

Cited by 40 publications

(10 citation statements)

References 57 publications

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“…By obtaining a minimum MAPE value of 0.01398, the proposed methods outperformed various existing studies (e.g. Ahmar and Del Val, 2020;Hussain et al, 2021;Albahli et al, 2022;Bathla et al, 2022). The results reveal that the MAPE value is dependent on the variations in sector stock prices over the testing period.…”

Section: Resultsmentioning

confidence: 73%

Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

İpek

2022

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PurposeCoronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction.Design/methodology/approachIn this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results.FindingsThe main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task.Originality/valueThe novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.

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Section: Resultsmentioning

confidence: 73%

Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

İpek

2022

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“…Moreover, Chan and Chong ( 134 ) and Teo et al ( 135 ) reported that the ANN is utilized to identify complex linear and non-linear relationships. Although recent studies suggested that the ANN is not enough to handle a complex nonlinear relationship ( 136 , 137 ). Nevertheless, the ANN approach give better results than conventional prediction methods ( 137 ) and is more precise than the usual regression strategy in terms of prediction ( 82 , 138 ).…”

Section: Methodsmentioning

confidence: 99%

Examining the Prediction of COVID-19 Contact-Tracing App Adoption Using an Integrated Model and Hybrid Approach Analysis

Alkhalifah

Bukar

2022

Front. Public Health

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COVID-19 contact-tracing applications (CTAs) offer enormous potential to mitigate the surge of positive coronavirus cases, thus helping stakeholders to monitor high-risk areas. The Kingdom of Saudi Arabia (KSA) is among the countries that have developed a CTA known as the Tawakkalna application, to manage the spread of COVID-19. Thus, this study aimed to examine and predict the factors affecting the adoption of Tawakkalna CTA. An integrated model which comprises the technology acceptance model (TAM), privacy calculus theory (PCT), and task-technology fit (TTF) model was hypothesized. The model is used to understand better behavioral intention toward using the Tawakkalna mobile CTA. This study performed structural equation modeling (SEM) analysis as well as artificial neural network (ANN) analysis to validate the model, using survey data from 309 users of CTAs in the Kingdom of Saudi Arabia. The findings revealed that perceived ease of use and usefulness has positively and significantly impacted the behavioral intention of Tawakkalna mobile CTA. Similarly, task features and mobility positively and significantly influence task-technology fit, and significantly affect the behavioral intention of the CTA. However, the privacy risk, social concerns, and perceived benefits of social interaction are not significant factors. The findings provide adequate knowledge of the relative impact of key predictors of the behavioral intention of the Tawakkalna contact-tracing app.

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“…So, the stock market analysis model should address that we should elect the suitable algorithms which are less prone to the more generic conditions and end up with some 60% to 75% predictions. Some of the researchers [31]- [39] employed the techniques of SVM and KNN the authors provided the accuracy ranging from 65% to 81% and in the work [40]. While these works are providing some direct results and some useful insights through various ML and DL approaches with the observed predictions and accuracy levels but still, we can revise these models with additional parameter tuning in the context of features for specific recommendations and predictions on the stock market data.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock market analysis with the usage of machine learning and deep learning algorithms

Sarma

Venkatasekhar²,

Murali³

2023

Bulletin EEI

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In this work we are focusing on listing out various works in the understanding of various parameters and context to get the overview of stock market analysis in the context of machine learning (ML) and deep learning (DL) models. The work focusses on the stock market analysis along with methodologies and algorithms used to understand the trends and the corresponding results as part of those studies. The importance of this work is to summarize and analyse the parameters which are highly influenced the understandingof the stock market trends. The outcome of the work is understanding the important factors which directly and indirectly influences the stock value raise and drop. The work highlights the methodologies and the algorithms used to stock market data analysis and efficient and effective recommendation of stable stocks to the customers. Further we are listing out the research gaps and future enhancements of the studies which are left over in the earlier works. The work pops up the limitations of some of the works in the existing works along with significance of hyper parameter techniques to clearly identify the features through which we can get more possibilities of better analysis of the data.

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Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction

Cited by 40 publications

References 57 publications

Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

Examining the Prediction of COVID-19 Contact-Tracing App Adoption Using an Integrated Model and Hybrid Approach Analysis

Stock market analysis with the usage of machine learning and deep learning algorithms

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