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), 2024. Published by Science Publishing Group |
Time series Forecasting, Financial Market, Support Vector Machines, Dhaka Stock Exchange, Machine Learning
| [1] | Abecasis, S. M., Lapenta, E. S., and Pedreira, C. E. (1999). Performance metrics for financial time series forecasting, Journal of computational intelligence in finance, Vol. 7, No. 4: 5-23. |
| [2] | A. Smola and B. Scholkopf. A Tutorial on Support Vector Regression. 1998, Technical Report NeuroCOLT NC-TR-98-030. |
| [3] | Burges, C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery 2, 121-167. |
| [4] | Cao, L. J. & E. H., Tay (2001). Support vector with adaptive parameters in financial time series forecasting, IEEE Trans. Neural Network, Volume 14, pp. 1506-1518. |
| [5] | Das, S. P., and Padhy, S., Support Vector Machines for Prediction of Futures Prices in Indian Stock Market, International Journal of Computer Applications (0975 – 8887), Volume 41 – No. 3, March 2012. |
| [6] | David J. and Sally L. (Eds), (2012); Advances in Computer Science and Information Engineering. New York, Springer. |
| [7] | Dunham, M. H. (2006). Data Mining: Introductory and Advanced Topics. India: Pearson Education. |
| [8] | Edgar Osuma and Robert Freund and Federico Girosi. Support Vector Machines: Training and Applications. AIM-1602, M. I. T., 38, 1997. |
| [9] | Kim, K. J., Han, I.: Genetic Algorithms Approach Feature Discretization in Artificial Neural Networks for the Prediction of Stock Price Index. J. Expert, Syst. Appl. 19, 125–132 (2000). |
| [10] | K. R. Muller, A. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen and V. N. Vapnik. Predicting time series with support vector machines. ICANN, 999-1004, 1997. |
| [11] | Liang, X., Zhang, H. S., Xiao, J. G., Chen, Y.: Improving Option Price Forecasts with Neural Networks and Support Vector Regressions. J. Neural Comput. Appl. 72, 3055–3065 (2009). |
| [12] | Liao, Z., Wang, J.: Forecasting Model of Global Stock Index by Stochastic Time Effective Neural Network. J. Expert, Syst. Appl. 37, 834–841 (2010). |
| [13] | Olson, David L., Delen, Dursun. (2008). Advanced-Data Mining Techniques. New York: Springer. |
| [14] | R. Sharda, R. Pati, A connectionist approach to time series prediction: an empirical test. Neural Networks in Finance and Investing, Chicago: Probus Publishing, 1994, pp. 451-464. |
| [15] | Tay, F. E. H. and Cao, L., “Application of support vector machines in financial time-series forecasting,” Omega, Vol. 29, 2001, pp. 309–317. |
| [16] | V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, pp. 1995. |
| [17] | V. N. Vapnik. Statistical Learning Theory. Wiley, New York, 1998. |
| [18] | V. N. Vapink, S. Golowich, and A. Smola. Support vector method for function approximation, regression estimation, and signal processing. |
| [19] | W. Cheng, L. Wanger, Forecasting the 30-year U.S. Treasury bond with a system of neural networks. Journal of Computational Intelligence in Finance, 4 (1996), pp. 10-16. |
| [20] | Yaser, S. A. M., Atiya, A. F.: Introduction to Financial Forecasting. J. Appl. Intel. 6, 205–213 (1996). |
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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