Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation

Rustam, Zuherman; Kintandani, Puteri

doi:10.1155/2019/8962717

Cited by 20 publications

(15 citation statements)

References 13 publications

(20 reference statements)

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“…The prediction of up-to-minute stock price was carried out by Henrique et al [36]; they concluded that the incorporation of a moving window could improve the predictability of the SVR. Recently, the SVM algorithm has been combined with other feature selection techniques (e.g., filter method and wrapper method) to improve the accuracy of trend prediction [37,38].…”

Section: Support Vector Machinementioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.

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Section: Support Vector Machinementioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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“…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%

“…Rustam et al [58] perform the attribute selection method using PSO as has been done by Guo et al [10], after normalization of technical data indicators and support vector regression (SVR) learning algorithms. Feature selection using Particle Swarm Optimization showed superior performance against the experimental results of the study.…”

Section: Rustam Et Al's Frameworkmentioning

confidence: 99%

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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“…These financial indicators can be used directly in prediction models as a dependent or independent variable. Such as Close price is used as a dependent variable or label in prediction models ( Lin, Yang & Song, 2009 ; Rustam & Kintandani, 2019 ). Moreover new features are also derived from the existing one such as gain in ( Garcia-Lopez, Batyrshin & Gelbukh, 2018 ; Mourelatos et al, 2018 ).…”

Section: Key Conceptsmentioning

confidence: 99%

News sensitive stock market prediction: literature review and suggestions

Usmani¹,

Shamsi²

2021

PeerJ Computer Science

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Stock market prediction is a challenging task as it requires deep insights for extraction of news events, analysis of historic data, and impact of news events on stock price trends. The challenge is further exacerbated due to the high volatility of stock price trends. However, a detailed overview that discusses the overall context of stock prediction is elusive in literature. To address this research gap, this paper presents a detailed survey. All key terms and phases of generic stock prediction methodology along with challenges, are described. A detailed literature review that covers data preprocessing techniques, feature extraction techniques, prediction techniques, and future directions is presented for news sensitive stock prediction. This work investigates the significance of using structured text features rather than unstructured and shallow text features. It also discusses the use of opinion extraction techniques. In addition, it emphasizes the use of domain knowledge with both approaches of textual feature extraction. Furthermore, it highlights the significance of deep neural network based prediction techniques to capture the hidden relationship between textual and numerical data. This survey is significant and novel as it elaborates a comprehensive framework for stock market prediction and highlights the strengths and weaknesses of existing approaches. It presents a wide range of open issues and research directions that are beneficial for the research community.

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Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation

Cited by 20 publications

References 13 publications

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

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

News sensitive stock market prediction: literature review and suggestions

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