Support Vector Machines (SVM) has been a naval research field in scientific research for forecasting. This study deals with the application of SVM in financial time series predicting. This paper suggests a model of stock market prediction based on SVMs with appropriate parameter values. A data set of daily closing prices of five selected companies such as Alhaj Textiles Limited, Apex Tannery Limited, Jamuna Bank Limited, Padma Oil Company, and Square Pharmaceuticals Limited of the Dhaka Stock Exchange (DSE) from 01 January 2017 to 13 August 2019 was selected and uses these data to train the model and checks the predictive power of the model. The obtained results show that all the companies closing stock prices are non-stationary. Also the number of support vectors and mean square error is decreasing pattern with the increase of kernel parameter. It is also found that original data and predicted data are very much identical. The result shows that in all the cases SVM model has some predictive power it can be used to forecast financial time series. Several methods, such as SVM, ARIMA, single exponential smoothing, and double exponential smoothing, were performed to predict Bangladesh's stock market. Amazingly, the outcome shows the most efficient method to be Support Vector Machine because of its lowest forecasting errors.
| Published in | Internet of Things and Cloud Computing (Volume 8, Issue 4) |
| DOI | 10.11648/j.iotcc.20200804.12 |
| Page(s) | 46-51 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Time series Forecasting, Financial Market, Support Vector Machines, Dhaka Stock Exchange, Machine Learning
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APA Style
Md. Farhad Hossain, Sharmin Islam, Partha Chakraborty, Ajit Kumar Majumder. (2020). Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines. Internet of Things and Cloud Computing, 8(4), 46-51. https://doi.org/10.11648/j.iotcc.20200804.12
ACS Style
Md. Farhad Hossain; Sharmin Islam; Partha Chakraborty; Ajit Kumar Majumder. Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines. Internet Things Cloud Comput. 2020, 8(4), 46-51. doi: 10.11648/j.iotcc.20200804.12
AMA Style
Md. Farhad Hossain, Sharmin Islam, Partha Chakraborty, Ajit Kumar Majumder. Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines. Internet Things Cloud Comput. 2020;8(4):46-51. doi: 10.11648/j.iotcc.20200804.12
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Download@article{10.11648/j.iotcc.20200804.12,
author = {Md. Farhad Hossain and Sharmin Islam and Partha Chakraborty and Ajit Kumar Majumder},
title = {Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines},
journal = {Internet of Things and Cloud Computing},
volume = {8},
number = {4},
pages = {46-51},
doi = {10.11648/j.iotcc.20200804.12},
url = {https://doi.org/10.11648/j.iotcc.20200804.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20200804.12},
abstract = {Support Vector Machines (SVM) has been a naval research field in scientific research for forecasting. This study deals with the application of SVM in financial time series predicting. This paper suggests a model of stock market prediction based on SVMs with appropriate parameter values. A data set of daily closing prices of five selected companies such as Alhaj Textiles Limited, Apex Tannery Limited, Jamuna Bank Limited, Padma Oil Company, and Square Pharmaceuticals Limited of the Dhaka Stock Exchange (DSE) from 01 January 2017 to 13 August 2019 was selected and uses these data to train the model and checks the predictive power of the model. The obtained results show that all the companies closing stock prices are non-stationary. Also the number of support vectors and mean square error is decreasing pattern with the increase of kernel parameter. It is also found that original data and predicted data are very much identical. The result shows that in all the cases SVM model has some predictive power it can be used to forecast financial time series. Several methods, such as SVM, ARIMA, single exponential smoothing, and double exponential smoothing, were performed to predict Bangladesh's stock market. Amazingly, the outcome shows the most efficient method to be Support Vector Machine because of its lowest forecasting errors.},
year = {2020}
}
TY - JOUR T1 - Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines AU - Md. Farhad Hossain AU - Sharmin Islam AU - Partha Chakraborty AU - Ajit Kumar Majumder Y1 - 2020/12/31 PY - 2020 N1 - https://doi.org/10.11648/j.iotcc.20200804.12 DO - 10.11648/j.iotcc.20200804.12 T2 - Internet of Things and Cloud Computing JF - Internet of Things and Cloud Computing JO - Internet of Things and Cloud Computing SP - 46 EP - 51 PB - Science Publishing Group SN - 2376-7731 UR - https://doi.org/10.11648/j.iotcc.20200804.12 AB - Support Vector Machines (SVM) has been a naval research field in scientific research for forecasting. This study deals with the application of SVM in financial time series predicting. This paper suggests a model of stock market prediction based on SVMs with appropriate parameter values. A data set of daily closing prices of five selected companies such as Alhaj Textiles Limited, Apex Tannery Limited, Jamuna Bank Limited, Padma Oil Company, and Square Pharmaceuticals Limited of the Dhaka Stock Exchange (DSE) from 01 January 2017 to 13 August 2019 was selected and uses these data to train the model and checks the predictive power of the model. The obtained results show that all the companies closing stock prices are non-stationary. Also the number of support vectors and mean square error is decreasing pattern with the increase of kernel parameter. It is also found that original data and predicted data are very much identical. The result shows that in all the cases SVM model has some predictive power it can be used to forecast financial time series. Several methods, such as SVM, ARIMA, single exponential smoothing, and double exponential smoothing, were performed to predict Bangladesh's stock market. Amazingly, the outcome shows the most efficient method to be Support Vector Machine because of its lowest forecasting errors. VL - 8 IS - 4 ER -
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