Stock Market Trend Prediction Using Support Vector Machines and Variable Selection Methods
Open accessAbstract
In this paper, a prediction model integrating machine learning and statistical analysis tools is presented to predict the trend of stock market.The proposed approach consists of three stages, as follows.In the first stage, the system uses technical analysis to calculate useful indicators based on historical data.Then, two different variable selection methods are applied to select the most important variables that describe the given data set.Finally, support vector machines (SVM ) has been used to construct the forecasting model.The hybridized approach was tested to solve the prediction task of directional changes in Dow Jones industrial average (DJIA) index.To evaluate the effectiveness of the use of variable selection techniques in construction of prediction models, this paper compares the performance of the proposed model with the standard SVMbased method.The study concludes that the use of a successful feature extraction technique can improve the forecasting accuracy of the prediction model.
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SVM has excellent memory efficiency and effectiveness, making it an ideal estimating technique in high-dimensional space, but it might experience overfitting; it performs exceptionally well on tiny datasets.
“SVM's excellent memory efficiency and effectiveness make it an ideal estimating technique in high-dimensional space.The shortcoming of SVM is that it might experience overfitting, but it performs exceptionally well on tiny datasets (Pathak and Pathak 2020;Grigoryan 2017).”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Grigoryan 2017
The Introduction (page not supplied) says SVMs have better generalization performance, supporting the claim of effectiveness. It does not establish memory efficiency, suitability for high-dimensional data, or exceptional performance on tiny datasets. It also describes SVMs as tending to resist overfitting, rather than presenting overfitting as a shortcoming. (LLM, conf=0.86)
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References 19 · Cited by 17
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- Authors
- Grigoryan, H.
- Affiliations
- Bucharest University of Economic Studies (RO)
- Corresponding author
- Grigoryan, Hakob
- ORCID
- Grigoryan, Hakob
- Venue
- Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)
- Publisher
- Atlantis Press
- Published
- 2017
- OpenAlex
- W2735833784
- Cited by
- 17
- References
- 19
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 1 · 2020: 1 · 2019: 1
- FWCI
- 0.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA) (publishedVersion, cc-by-nc)
- Funders
- Tallinna Tehnikaülikool
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Keywords
- stock trend prediction, support vector machine, feature selection, technical indicators, feature extraction, Dow Jones Industrial Average, hybrid forecasting model
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