Interval Forecasting of Stock Price Changes using the Hybrid of Holt’s Exponential Smoothing and Multi-Output Support Vector Regression

Advances in Mathematical Finance and Applications

مقاله 9، دوره 7، شماره 2، تیر 2022، صفحه 405-421   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2020.1883402.1332

نویسندگان

Sayyed Mohammadreza Davoodi^{* 1}؛ Mahdi Rabiei²

¹Department of Management ,Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.

²Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran

چکیده

Given the importance of investment in stock markets as a major source of income for many investors, there is a strong demand for models that estimate the future behavior of stock prices. Interval forecasting is the process of predicting an interval characterized by two random variables acting as its upper and lower bounds. In this study, a hybrid method consisting of Holt’s exponential smoothing and multi-output least squares support vector regression is used to forecast the interval of the lowest and highest prices in a stock market. First, Holt’s smoothing method is used to smooth the two bounds of the interval and then the residuals of the smoothing process are modeled with multi-output vector support regression. The output of the regression step is the error of the two bounds of the interval. The method is implemented on the weekly data of the overall index of the Tehran Stock Exchange from 1992 to 2016, with the interval defined as the distance between the lowest and highest overall index values. The results demonstrate the high accuracy of the hybrid method in producing in-sample and out-of-sample forecasts for the movement of the two bounds of the interval, that is, the weekly highs and lows of the overall index. Also, the hybrid method has achieved a lower mean squared error than the Holt’s smoothing method, indicating that multi-output vector support regression has improved the performance of the smoothing method

کلیدواژه‌ها

smoothing؛ support vector machine؛ multi-output least-squares vector regression؛ interval forecasting

مراجع

References

 

]1[ Raee, R., Nikahd, A., Habibi, M., The Index Prediction of Tehran Stock Exchange by Combining the Principal Components Analysis, Support Vector Regression and Particle Swarm Optimization, Financial Management Strategy, 2017, 4(4), P.1-23. Doi: 10.22051/jfm.2017.12410.1175

 

]2[ Raei, R., Saeidi, A., Principles of financial engineering and risk management, Tehran University Business School publications and Samt publications, 2004. (in Persian).

 

]3[ Shahriari, H., Aghaie, A., Nezhad Afrasiabi, M., Financial Time series Forecasting using Holt-Winters in H-step Ahead, Financial Research Journal, 2016, 18(3), P. 505-518. Doi: 10.22059/jfr.2016.62453

 

]4[ Mohammadian Amiri, E., Ebrahimi, S., Multiple-Step-Ahead Forecasting of Value at Risk Based on Holt-Winters Exponential Smoothing Multiplicative Method, Financial Management Strategy, 2018, 6(1), P.93-114.

 Doi: 10.22051/jfm.2018.15099.1355.

 

]5[ Mansourfar, G., Ghayour, F., Lotfi, B., The Ability of Support Vector Machine (SVM) in Financial Distress Prediction, Empirical Research in Accounting, 2015, 5(1), P.177-195. Doi: 10.22051/jera.2015.646

 

]6[ Fallahpour, S., Golarzi, G., Fatourechian, N., Predicting Stock Price Movement Using Support Vector Machine Based on Genetic Algorithm in Tehran Stock Exchange Market, Financial Research Journal, 2013, 15(2), P.269-288. Doi: 10.22059/jfr.2013.51081.

 

]7[ Chen, Y., Hao, YA., feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction, Expert Systems with Applications, 2017, 80, P.340 355. ‏Doi:10.1016/j.eswa.2017.02.04.

 

]8[ Falah Shams, M., Rashnoo, M., Investigating the Prices Manipulation in the Tehran Stock Exchange by Using the SVM Model, Financial Research Journal, 2012, 14(1), P. 69-84. Doi: 10.22059/jfr.2012.36633

 

]9[ Xiong, T., Li, C., Bao, Y., Interval-valued time series forecasting using a novel hybrid Holt’I and MSVR model, Economic Modelling, 2017, 60, P.11-23. Doi: 10.1016/j.econmod.2016.08.019

 

]10[ Huang, S. C., Chuang, P. J., Wu, C. F., Lai, H. J, Chaos-based support vector regressions for exchange rate forecasting, Expert Systems with Applications, 2010, 37(12), P.8590-8598. Doi: 10.1016/j.eswa.2010.06.001

 

]11[ Gavrishchaka, V., Banerjee, S., Support vector machine as an efficient framework for stock market volatility forecasting, Computational Management Science, 2006, 3(2), P.147-160. Doi:10.1007/s10287-005-0005-5‏

 

[12] Abdolbaghi Ataabadi, A., Davoodi, S., Salimi Bani, M., The Effectiveness of the Automatic System of Fuzzy Logic-Based Technical Patterns Recognition: Evidence from Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2019, 4(3), P.107-125. Doi: 10.22034/amfa.2019.585179.1185

 

[13] Parmezan, A. R. S., Souza, V. M., Batista, G. E., Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model, Information Sciences, 2019, 484, P.302-337.‏ Doi:10.1016/j.ins.2019.01.076

 

[14] Sun, S., Sun, Y., Wang, S., Wei, Y., Interval decomposition ensemble approach for crude oil price forecasting, Energy Economics, 2018, 76, P.274-287.‏ Doi: 10.1016/j.eneco.2018.10.015.

 

[15] Ehteshami, S., Hamidian, M., Hajiha, Z., Shokrollahi, S., Forecasting Stock Trend by Data Mining Algorithm, Advances in Mathematical Finance and Applications, 2018, 3(1), P.97-105.

 Doi: 0.22034/amfa.2018.539138

 

[16] Aghaeefar, N., Mohammad Pourzarandi, M., Afshar Kazemi, M., Minoie, M., Applying Optimized Mathematical Algorithms to Forecast Stock Price Average Accredited Banks in Tehran Stock Exchange and Iran Fara Bourse, Advances in Mathematical Finance and Applications,2019, 4(3), P.77-94.

 Doi: 0.22034/amfa.2019.580802.1150

                             

[17] Abbasi, E., Samavi, M., Koosha, E., Performance Evaluation of the Technical Analysis Indicators in Comparison whit the Buy and Hold Strategy in Tehran Stock Exchange Indices, Advances in Mathematical Finance and Applications,2020, 5(3), P.1-19. Doi:10.22034/amfa.2020.1893194.1376

 

[18] Panahian, H., Ghazifari, S., Modelling and Investigating the Differences and Similarities in the Volatility of the Stocks Return in Tehran Stock Exchange Using the Hybrid Model PANEL-GARCH, Advances in Mathematical Finance and Applications,2018, 3(2), P.13-26. Doi:10.22034/amfa.2018.540828

 

[19] Bajalan, S., Fallahpour, S., Dana, N., The Stock Trend Prediction Using Volume Weighted Support Vector Machine with a Hybrid Feature Selection Method to Predict the Stock Price Trend in Tehran Stock Exchange, Financial Management Strategy,2016, 4(3), P. 121-148. Doi:10.22051/jfm.2016.2575

 

[20] Rai, R., Habibi, M., Prediction of Tehran Stock Exchange Index by combining methods of analysis of main components, support vector regression and cumulative particle motion, Financial Management Strategy, 2017, 4(4), P.1-23. Doi:10.22051 / JFM.2017.12410.1175