Predicting stock market price using support vector regression

Meesad, Phayung; Rasel, Risul Islam

doi:10.1109/iciev.2013.6572570

Cited by 53 publications

(18 citation statements)

References 8 publications

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“…The author used second-order polynomial (quadratic) nonlinear regression and found that the k-nearest neighbor algorithm performs better than non-linear regression. Phayung Meesad (Meesad and Rasel 2013) used SVR with different kinds of windowing operators. They showed that SVR with a rectangular window and flatten window is reasonable to predict for 1-day and 5-day ahead stock prices of DSE.…”

Section: Review Of Literaturementioning

confidence: 99%

Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques

et al. 2021

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One of the most challenging topics in financial market analysis is predicting stock prices. Factors like supply and demand in the market, market sentiment, and investor's expectations, economic and political shocks can affect stock prices. All these factors make stock prices volatile and chaotic. Several machine learning models have been developed to make more precise and accurate predictions. Support vector regression (SVR) and K-nearest neighbor (KNN) regression are the most popular machine learning techniques used for stock price prediction. Our study follows the hypothesis that the SVR algorithm is a more precise way of predicting the Dhaka Stock Exchange (DSE) than the KNN regression. This research has made predictions and compared prediction errors between SVR and KNN. The analysis has been conducted on recent years' data of some selected shares listed on the DSE. The performance of the models is measured in terms of their root mean squared error (RMSE), R-squared (R 2), adjusted R-squared score values. We have optimized our model performance by tuning different combinations of hyper-parameters. The best result was found with the linear SVR model in the case of BXPHARMA with the highest R-squared score of about 97.04%, and lowest RMSE of about 1.23, followed by the KNN regression model with an R-squared score of approximately 96.39% and RMSE of about 1.38. SVR has the lowest RMSE and highest R-squared values in all cases.

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Section: Review Of Literaturementioning

confidence: 99%

Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques

et al. 2021

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“…The selection of kernel function is important to the effectiveness of Support Vector Regression. However, there is no mature theory in the selection of kernel function of SVR [24,27].…”

Section: Support Vector Regressionmentioning

confidence: 99%

A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

et al. 2017

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“…In order to undertake the experiments and evaluate the results from the experiments, NASDAQ index was selected as our research domain [2]. The Apple stock (AAPL) was selected to analyze the results and make predictions.…”

Section: Machine Learning Approaches For Stock Prediction a Datamentioning

confidence: 99%

“…80% of data was used for training and the remaining for testing. The original dataset contains 6 attributes: Date, Open, High, Close, Adj Close and Volume [2]. The goal was to predict the Closing price of Apple stock (AAPL) for 35 days.…”

Section: Machine Learning Approaches For Stock Prediction a Datamentioning

confidence: 99%

Survey on Stock Prediction Using Machine Learning Approaches

Borade¹

2018

IJRASET

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Predicting stock market price using support vector regression

Cited by 53 publications

References 8 publications

Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques

Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques

A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

Survey on Stock Prediction Using Machine Learning Approaches

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