EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices

Tang, Lin; Pan, Ping He; Yao, Yong

doi:10.15837/ijccc.2018.2.3187

Cited by 4 publications

(8 citation statements)

References 16 publications

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“…For the datasets in Botunac et al (2020); Kumar et al 2016;Yuan et al 2020;Labiad et al 2016;Haq et al 2021), RF provides good performance in terms of high accuracy and low error values. Meanwhile, PCA provides satisfactory results in Nabi et al (2019); Shen and Shafiq 2020;Siddique and Panda 2019;Singh and Khushi 2021;Ampomah et al 2020;Qolipour et al 2021;Iacomin 2015;Ampomah et al 2021;Das et al 2019;Tang et al 2018). Neural network-based models, and AEs have also been successfully applied for feature extraction (Chong et al 2017;Bhanja and Das 2022;Xie and Yu 2021;Dami and Esterabi 2021;Gunduz 2021).…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…Kumar et al ( 2021b ) Basic features, Technical indicators PCA ANN 3 stock indices 25. Tang et al ( 2018 ) historical relative re-turns PCA KNN CSI 300 index 26. Barak et al ( 2017 ) Fundamental indica-tors GA Multiple classifiers 400 stocks 27.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…Subsequently, we excluded 83 articles that did not mention the feature selection methods applied. Therefore, we obtained 32 relevant papers (27 in journals (Alsubaie et al 2019;Aloraini 2015;Li et al 2022;Kumar et al 2016Nabi et al 2019;Yuan et al 2020;Shen and Shafiq 2020;Haq et al 2021;Sun et al 2019;Hao 2017, 2020;Gunduz et al 2017;Siddique and Panda 2019;Singh and Khushi 2021;Ampomah et al 2020Ampomah et al , 2021Qolipour et al 2021;Das et al 2019;Tang et al 2018;Chong et al 2017;Bhanja and Das 2022;Xie and Yu 2021;Dami and Esterabi 2021;Gunduz 2021;Barak et al 2017;Farahani and Hajiagha 2021), and 5 in conference proceedings (Botunac et al 2020;Cai et al 2012;Labiad et al 2016;Rana et al 2019;Iacomin 2015). Figure 2 illustrates the article selection method.…”

Section: Survey Methodsmentioning

confidence: 99%

“…According to the ex- perimental findings, PCA reduced the complexity and computational cost of the prediction model from the original 20 feature sets to 9 features to predict the clos- ing prices of the Nifty 50, Sensex, and S&P 500 stock indices. The study in Tang et al ( 2018 ) applied PCA for dimensionality reduction to provide information-rich features for a KNN model to forecast the relative returns of 10 indices from the Chinese CSI 300 market. For the Telecom Svc index, the method achieved the highest hit rate of 79.60%.…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

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Survey of feature selection and extraction techniques for stock market prediction

2023

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In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Analysis and Discussionmentioning

confidence: 99%

Section: Survey Methodsmentioning

confidence: 99%

Section: Feature Extraction Techniquesmentioning

confidence: 99%

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Survey of feature selection and extraction techniques for stock market prediction

2023

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“…Literature [7] used the vector autoregressive (VaR) model, error correction model (ECM), and Kalman filter model (KFM) to predict the UK stock market in 1996. Literature [8] uses the Bayesian vector autoregressive model (BVaR) to predict the portfolio return of some large German companies, but the prediction effect is poor. Based on the stock data of the New York Stock Exchange and Nigeria stock exchange, literature [9] attempts to use the ARIMA model to predict stock prices.…”

Section: Stock Forecasting Based On Regressionmentioning

confidence: 99%

A Novel Stock Index Intelligent Prediction Algorithm Based on Attention‐Guided Deep Neural Network

Yuan

2021

Wireless Communications and Mobile Computing

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The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.

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An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting

et al. 2021

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Stock index prediction plays an important role in the creation of better investment strategies. However, prediction can be difficult due to the random fluctuation of financial time series. In pursuit of improved stock index prediction, a hybrid prediction model is proposed in this paper, which contains two-step data pretreatment, double prediction models, and smart optimization. In the data pretreatment stage, in order to carry more information about the prediction target, multidimensional explanatory variables are selected by the Granger causality test, and to eliminate data redundancy, feature extraction is inserted with the help of principal component analysis; both of these can provide a higher-quality dataset. Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. In the last stage, the proposed model integrates the weight optimization of the cuckoo search of the two prediction results to take advantage of both. For the model performance test, four main global stock indices are used. The experimental results show that our model performs better than other benchmark models, which indicates the potential of the proposed model for wide application.

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EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices

Cited by 4 publications

References 16 publications

Survey of feature selection and extraction techniques for stock market prediction

Survey of feature selection and extraction techniques for stock market prediction

A Novel Stock Index Intelligent Prediction Algorithm Based on Attention‐Guided Deep Neural Network

An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting

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