اخبار و اعلانات
آمار
| تعداد نشریات | 27 |
| تعداد شماره‌ها | 566 |
| تعداد مقالات | 5,825 |
| تعداد مشاهده مقاله | 8,162,060 |
| تعداد دریافت فایل اصل مقاله | 5,460,762 |
Analyzing the Advantage of Combination of Density Forecasts in Tehran Stock Exchange | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 3، دوره 4، شماره 1، فروردین 2021، صفحه 23-31 اصل مقاله (940.24 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2020.32870.1237 | ||
| نویسندگان | ||
S.Raheleh Shahrokhi 1؛ Hamid Khaloozadeh  2؛ HamidReza Momeni3
| ||
| 1Faculty of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| 2Department of Systems and Control Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology | ||
| 3Department of Systems and Control, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| چکیده | ||
| Today, stock market plays a key role in the economy of any country and is considered as one of the growth indicators of any economy. Gaining the skills of gathering and analyzing data simultaneously, as well as using this knowledge in economic investigations, make time and precision factors to be the drawcard of any investor in competition with others. Therefore, having a predictive approach with the lowest degree of error will lead to smarter management of resources. Due to the complex and stochastic nature of the stock market, conventional forecasting approaches in this field have usually faced serious challenges, most notably losing the robustness when the data type changed over time. Moreover, by focusing on point forecasting, some useful statistical information about the objective random variable has been ignored inadvertently, undermining the prediction efficiency. The focus of this study is on density forecasting models which, unlike point forecasting, contain a description of uncertainty. Also, to take advantage of the diversity and robustness features of the combination, instead of an individual prediction, a combination of the density forecasting given by the different structures of ARMA, ANN, and RBF models is presented. In order to analyze the capabilities of these approaches in Tehran Stock Exchange (TSE), two basic methods of this category have been used to predict the price of MAPNA stock -one of the fifty active companies in this market- in the period 2012 to 2019. | ||
| کلیدواژه‌ها | ||
| Forecast Combination؛ Density Forecasting؛ Neural Networks؛ Simple Average؛ Stock Market | ||
| مراجع | ||
|
[1] A. Azar, and S. Karimi, "Stock return prediction using accounting ratio with the neural network approach," Financial Research Journal, Vol. 11, No. 28, pp. 3-20, 2009. [2] H. Khaloozadeh, and A. Khaki Sedigh, "Stock Price Modeling and Forecasting Using Stochastic Differential Equations," Journal of Economic Research, No. 69, pp. 1-6, 2005. [3] S. A. Ghoreishi, and H. Khaloozadeh, "Application of Covariance Matrix Adaptation-Evolution Strategy to Optimal Portfolio," International Journal of Industrial Electronics, Control and Optimization, Vol. 2, No. 2, pp. 81-90, 2019. [4] H. Abrishami, M. Mehrara, M. Ahrari, and S. Mirghasemi, "Forecasting The GDP in Iran Based on GMDH Neural Network," Journal of Economic Research, No. 88, pp. 1-24, 2009. [5] H. Abrishami, A. Moeini, M. Mehrara, M. Ahrari, and F. Soleymanikia, "Forecasting Gasoline Price Using GMDH Neural Network Based on GA," Iranian Journal of Economic Research, No. 36, pp. 37-58, 2008. [6] S. Soni, "Applications of ANNs in Stock Market Prediction: A Survey," International Journal of Computer Science & Engineering Technology, Vol. 2, pp. 71-83, 2011. [7] G. S. Atsalakis, K. P. Valavanis, "Surveying Stock Market Forecasting Techniques-Part I: Conventional Methods," Journal of Computation Optimization in Economics and Finance Research Compendium, pp. 49-104, 2010. [8] G. E. Box, G. M. Jenkins, and G. C. Reinsel, "Time Series Analysis: Forecasting and Control," Wiley, New York, 2013. [9] S. McDonald, S. Coleman, T. M. McGinnity, Y. Li, and A. Belatreche, "A Comparison of Forecasting Approaches for Capital Markets," Computational Intelligence for Financial Engineering & Economics (CIFEr) IEEE Conference, pp. 32-39, 2014. [10] H. A. Sinai, S. Mortazavi, and Y. Teimoori asl, "Tehran Exchange Price Index Forecasting by Artificial Neural Networks," Accounting and auditing Review, Vol. 2, No. 3, pp. 59-83, 2005. [11] G. S. Atsalakis, K. P. Valavanis, “Surveying Stock Market Forecasting Techniques–Part II: Soft Computing Methods,” Expert Systems with Applications, Vol. 36, No. 3, pp. 5932–5941, 2009. [12] S. McDonald, S. Coleman, T. M. McGinnity, Y. Li, and A. Belatreche, "A Comparison of Forecasting Approaches for Capital Markets," Computational Intelligence for Financial Engineering & Economics (CIFEr) IEEE Conference, pp. 32-39, 2014. [13] D. Gholamiangonabadi, S. D. Mohseni Taheri, A. Mohammadi, and M. B. Menhaj, "Investigating the Performance of Technical Indicators in Electrical Industry in Tehran's Stock Exchange Using Hybrid Methods of SRA, PCA and Neural Networks," 5th IEEE Conference on Thermal Power Plants (CTPP), pp. 75-82. 2014. [14] H. White, "Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns," IEEE International Conference, pp. 451–458, 1988. [15] Y. Li, W. Ma, "Applications of Artificial Neural Networks in Financial Economics: A Survey, "International Symposium on Computational Intelligence and Design, IEEE Computer Society, Vol. 01, pp. 211–214, 2010. [16] Y. Wang, I. Ch. Choi, "Market Index and Stock Price Direction Prediction Using Machine Learning Techniques: An Empirical Study on the KOSPI and HIS," ScienceDirect, pp. 1–13, 2013. [17] T. B. Trafalis, H. Ince, "Support Vector Machine for Regression and Applications to Financial Forecasting," Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN), Vol. 6, 2000. [18] F. E. Tay, L. Cao, “Application of Support Vector Machines in Financial Time Series Forecasting,” Omega, Vol. 29, No. 4, pp. 309– 317, 2001. [19] L. J. Cao, F. E. H. Tay, “Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting,” IEEE Transactions on Neural Networks, Vol. 14, No. 6, pp. 1506–1518, 2003. [20] R. Khemchandani, S. Chandra, and others, “Regularized Least Squares Fuzzy Support Vector Regression for Financial Time Series Forecasting,” Expert Systems with Applications, Vol. 36, No. 1, pp. 132–138, 2009. [21] G. Hinton, S. Osindero, and Y. W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation, Vol. 18, No. 7, pp. 1527–1554, 2006. [22] L. Wang, H. Zou, J. Su, L. Li, and S. Chaudhry, “An ARIMA-ANN Hybrid Model for Time Series Forecasting,” Systems Research and Behavioral Science, 2013. [23] Z. Hua and B. Zhang, “A Hybrid Support Vector Machines and Logistic Regression Approach for Forecasting Intermittent Demand of Spare Parts,” Applied Mathematics and Computation, Vol. 181, No. 2, pp. 1035–1048, 2006. [24] G. P. Zhang, “Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model,” Neuro-computing, Vol. 50, pp. 159–175, 2003. [25] A. Raoofi, A. H. Montazer-Hojjat, and P. Kiani, “Comparison of Several Combined Methods for Forecasting Tehran Stock Exchange Index,” International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 4, pp. 315-333, 2016. [26] S. Fallahpour, Gh. Golarzi, and N. Fatourechian. "Predicting Stock Price Movement Using Support Vector Machine Based on Genetic Algorithm in Tehran Stock Exchange Market," Financial Research Journal, Vol. 15, No. 2, pp. 269-288, 2013. [27] A. M. Rather, A. Agarwal, V. N. Sastry, "Recurrent Neural Network and a Hybrid Model for Prediction of Stock Returns," Expert Systems with Applications, Vol. 42, No. 6, pp. 3234-3241, 2015. [28] R. Bisoi, P. K. Dash, "A Hybrid Evolutionary Dynamic Neural Network for Stock Market Trend Analysis and Prediction Using Unscented Kalman Filter," Applied Soft Computing, Vol. 19, pp. 41-56, 2014. [29] J. Patel, S. Shah, P. Thakkar, K. Kotecha, "Predicting Stock and Stock Price Index Movement Using Trend Deterministic Data Preparation and Machine Learning Techniques," Expert Systems with Applications, Vol. 42, No. 4, pp. 259-268, 2015. [30] A. Timmermann, “Forecast Combinations,” CEPR Discussion Paper, No. 5361, 2005. [31] I. Sánchez, "Adaptive Combination of Forecasts with Application to Wind Energy," International Journal of Forecasting, Vol.24, No. 4, pp. 679-693, 2008. [32] G. Claeskens, J. R. Magnus, A. L. Vasnev, and W. Wang, "The Forecast Combination Puzzle: A Simple Theoretical Explanation," International Journal of Forecasting, Vol. 32, No. 3, pp. 754-762, 2016. [33] S. M. Blanc, and T. Setzer, "When to Choose the Simple Average in Forecast Combination," Journal of Business Research, Vol. 69, No. 10, pp. 3951-3962, 2016. [34] S Ch. G. Gibbs, and A. L. Vasnev, "Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts," UNSW Business School Research Paper, 2017. [35] J. D. Samuels, and R. M. Sekkel, "Model Confidence Sets and Forecast Combination," International Journal of Forecasting, Vol. 33, No. 1, pp. 48-60, 2017. [36] F. X. Diebold, and M. Shin, "Machine Learning for Regularized Survey Forecast Combination: Partially- Egalitarian LASSO and Its Derivatives," International Journal of Forecasting, Vol.35, No. 4, pp. 1679-1691, 2019. [37] D. Shaub, "Fast and Accurate Yearly Time Series Forecasting with Forecast Combinations," International Journal of Forecasting, Vol. 36, No. 1, pp. 116-120, 2020. [38] Y. Wang, N. Zhang, Y. Tan, T. Hong, D. S. Kirschen, and C. Kang, "Combining Probabilistic Load Forecasts," IEEE Transactions on Smart Grid, Vol. 10, No. 4, pp. 3664-3674, 2018. [39] A. F. Atiya, "Why Does Forecast Combination Work So Well? ," International Journal of Forecasting, Vol. 36, No. 1, pp. 197-200. 2020. [40] K. C. Lichtendahl Jr, and R. L. Winkler, "Why Do Some Combinations Perform Better than Others? ," International Journal of Forecasting, Vol. 36, No. 1, pp. 142-149, 2020. [41] M. P. Clements, and D. I. Harvey, "Combining Probability Forecasts," International Journal of Forecasting, Vol. 27, No. 2, pp. 208-223, 2011. [42] J. Nowotarski, and R. Weron, "Recent Advances in Electricity Price Forecasting: A Review of Probabilistic Forecasting," Renewable and Sustainable Energy Reviews, Vol. 81, pp. 1548-1568, 2018. [43] G. Kapetanios, J. Mitchell, S. Price, and N. Fawcett, "Generalised Density Forecast Combinations," Journal of Econometrics, Vol. 188, pp. 150–165, 2015. [44] J. M. Bates, and C. W. Granger, "The Combination of Forecasts," Journal of the Operational Research Society, Vol. 20, No. 4, pp. 451-468, 1969. [45] J. H. Stock, M. W. Watson, “A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series,” National Bureau of Economic Research, 1998. [46] J. H. Stock, M. W. Watson, "Combination Forecasts of Output Growth in a Seven‐Country Data Set," Journal of Forecasting, Vol. 23, No. 6, pp. 405-430, 2004. [47] A. Timmermann, “Forecast combinations,” Handbook of economic forecasting, Vol. 1, pp. 135-196, 2006. [48] R. T. Clemen, "Combining Forecasts: A Review and Annotated Bibliography," International Journal of Forecasting, Vol. 5, No. 4, pp. 559-583, 1989. [49] F. X. Diebold, J. A. Lopez, "Forecast Evaluation and Combination," Handbook of Statistics, Vol. 14, pp. 241-268, 1996. [50] S. M. Blanc, and T. Setzer, "When to Choose the Simple Average in Forecast Combination," Journal of Business Research, Vol. 69, No. 10, pp. 3951-3962, 2016. [51] A. S. Tay, and K. F. Wallis, "Density Forecasting: A Survey," Journal of forecasting, Vol. 19, No. 4, pp. 235-254, 2000. [52] K. F. Wallis, "Combining Density and Interval Forecasts: A Modest Proposal," Oxford Bulletin of Economics and Statistics, Vol. 67, pp. 983-994, 2005. [53] Z. Hua, and B. Zhang, "Improving Density Forecast by Modeling Asymmetric Features: An Application to S&P500 Returns," European Journal of Operational Research, Vol. 185, No. 2, pp. 716-725, 2008. [54] J. Mitchell, and K. F. Wallis, "Evaluating Density Forecasts: Forecast Combinations, Model Mixtures, Calibration and Sharpness," Journal of Applied Econometrics, Vol. 26, No. 6, pp. 1023-1040, 2011. | ||
|
آمار تعداد مشاهده مقاله: 192 تعداد دریافت فایل اصل مقاله: 194 |
||
