Stock market trend prediction using AHP and weighted kernel LS-SVM

Marković, Ivana; Stojanović, Miloš B.; Stanković, Jelena; Stanković, Milena

doi:10.1007/s00500-016-2123-0

Cited by 23 publications

(10 citation statements)

References 34 publications

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“…Weng et al, [13] also follow up on research results [53] and also the results of their previous research [55] about stock price predictions. In its development, Weng et al In more detail, the preprocessor data includes cleaning data for existing missing and outliers and transformation data.…”

Section: Weng Et Al's Frameworkmentioning

confidence: 95%

“…Using boost algorithm (Boosting Method) [50], [51], [13], [34], [52]. Added a feature selection method [53], [54], [55], [36], [56]. By using hyper-parameter optimization (tuning method of hyper-parameter) for several learners [57], [58], [10], [59], [60], [34], [61], [5].…”

Section: Proposed Methods Improvements and Modification For Stock Predictionsmentioning

confidence: 99%

“…The results of their research [53] reveal that the Support vector Machine classifier has an average predictive performance of 60 percent for the three stock exchange markets (BELEX15, S&P500, FTSE100), after selecting attributes using the AHP method. The feature weighting results generated by the Analytical Hierarchy Process method are used for ranking and feature selection, and used with LS-SVM through a weighted kernel.…”

Section: Markovic Et Al's Frameworkmentioning

confidence: 99%

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The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

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This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.

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Section: Weng Et Al's Frameworkmentioning

confidence: 95%

Section: Proposed Methods Improvements and Modification For Stock Predictionsmentioning

confidence: 99%

Section: Markovic Et Al's Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

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“…Moreover, several hybrid based approaches have been proposed for stock trend prediction, which have achieved excellent performance. For example, Marković et al [50] proposed a new hybrid method that integrates the analytic hierarchy process and weighted kernel least squares SVM. Lei [51] developed a hybrid method by combining the rough set and wavelet neural network.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

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Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

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“…It has been applied to stock market analysis and has been verified to be effective when it is being compared with other algorithms, such as the Random Walk Model (RW), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Elman Backpropagation Neural Networks (EBNN) (Huang et al 2005). It has been used for stock market daily price prediction (Henrique et al 2018;Marković et al 2017) and Producer Price Index (PPI) prediction (Tang et al 2018). Although the feasibility was proved, the research also pointed out the limitations for solving such problem as a regression task (Henrique et al 2018).…”

Section: Stock Forecasting Based On Stock Pricementioning

confidence: 99%

Learning-Based Stock Market Trending Analysis by Incorporating Social Media Sentiment Analysis

Wang

et al. 2021

Preprint

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Stock market trending analysis is one of the key research topics in financial analysis. Various theories once highlighted the non-viability of stock market prediction. With the advent of machine learning and Artificial Intelligence (AI), more and more efforts have been devoted to this research area, and predicting the stock market has been demonstrated to be possible. Learning-based methods have been popularly studied for stock price prediction. However, due to the dynamic nature of the stock market and its non-linearity, stock market prediction is still one of the most dificult tasks. With the rise of social networks, huge amount of data is being generated every day and there is a gaining in popularity of incorporating these data into prediction model in the effort to enhance the prediction performance. Therefore, this paper explores the possibilities of the viability of learning-based stock market trending prediction by incorporating social media sentiment analysis. Six machine learning methods including Multi-Layer Perception, Support Vector Machine, Naïve Bayes, Random Forest, Logistic Regression and Extreme Gradient Boosting are selected as the baseline model. The result indicates the possibilities of successful stock market trending prediction and the performance of different learning-based methods is discussed. It is discovered that the distribution of the value of stocks may affect the prediction performance of the methods involved. This research not only demonstrates the merits and weaknesses of different learning-based methods, but also points out that incorporating social opinion is a right direction for improving the performance of stock market trending prediction.

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Stock market trend prediction using AHP and weighted kernel LS-SVM

Cited by 23 publications

References 34 publications

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Learning-Based Stock Market Trending Analysis by Incorporating Social Media Sentiment Analysis

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