آزمون تغییرپذیری عوامل موثر در پیش‌بینی بازده سهام با استفاده از مدلهای میانگین گیری پویا (DMA)
محورهای موضوعی : مهندسی مالی
حسین مقصود
1
(گروه مدیریت مالی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.)
حمیدرضا وکیلی فرد
2
(گروه مدیریت مالی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.)
تقی ترابی
3
(گروه اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.)
کلید واژه: پیش‌بینی, بازده سهام, پارامتر متغیر-زمان(TVP), مدل میانگین‌گیری پویا (DMA),
چکیده مقاله :
در این تحقیق تلاش شده است با استفاده از مدلهای میانگین‌گیری پویا و داده‌های ماهانه در بازه زمانی 1388:1 تا 1396:12 بازدهی سهام در بورس اوراق بهادار تهران بررسی شود. در این راستا متغیرهای کلان و شاخص‌های بازارهای موازی به منظور پیش‌بینی بازده سهام مورد استفاده قرار‌گرفته‌است. نخست با برآورد مدلهای رگرسیون بازگشتی، مدلهای پارامتر متغیر-زمان (TVP)، مدل انتخابی پویا (DMS) و مدل میانگین‌گیری پویا (DMA) در نرم افزار متلب مشاهده گردید مدل DMS با 95/0 α= β= بر اساس معیارهای سنجش عملکرد پیش‌بینی) MAFE، MSFE و Log(PL) ( از دقت پیش‌بینی بالاتری در مقایسه با سایر روش‌ها برخوردار است. همچنین بر اساس نتایج برآورد متغیر قیمت طلا (48 دوره)، نرخ ارز (36 دوره) و متغیر تورم (30 دوره) به ترتیب بالاترین و متغیرهای قیمت جهانی نفت و تولید ناخالص داخلی نیز به ترتیب با 28 و 2 تکرار کمترین تاثیر را بر بازدهی سهام داشته‌اند. نتایج مبین آن است که استفاده از مدلهای پویا با در نظر گرفتن تغییرات زمانی پارامترها و تغییر در مدل، کارایی پیش‌بینی بازدهی سهام را افزایش می‌دهد.
In this study, using dynamic averaging models and monthly data in the period 2001:4 until 2018:3, Tehran Stock Exchange returns be investigated. In this regard, macroeconomics variables and parallel markets indices have been used to forecast the stock returns. Initially, estimating various models such as Recursive models, time-varying parameter models (TVP), dynamic model selection (DMS) and dynamic model averaging (DMA) in Matlab software, It was observed that DMS model with α = β = 0.95 had higher forecast accuracy (based on MAFE, MSFE and Log (PL) metrics). Gold price (48-period), exchange rate (36-period) and inflation rate (30-period) had the highest effect on stock returns, respectively, and global oil prices and GDP had the lowest effect by 28 and 2, respectively. Finally, the results indicate that utilizing dynamic models by considering time variations in parameters and the variation of the model increases the efficiency of forecasting stock returns. Keywords: Forecasting, Stock Returns, time-varying Parameter (TVP), Dynamic Model Averaging (DMA).
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19) Cleofe Giorgino Maria, Barnabè Federico, Martin Kunc (2019).Integrating qualitative system dynamics with accounting practices: The case of integrated reporting and resource mapping, System research and behavioral science, 1-22.
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21) Emami, Sophia (2014). Option Pricing and Switching Regime Model,Masters Thesis, University of Guilan, and Faculty of Mathematical Sciences (in Persian).
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23) Kavetsky, Carlos,(2017). Calibrating a System Dynamic Model within an Integrative Framework to Test Foreign Policy Choices, Electronic Theses and Dissertations. 5578, 1-197
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Conference of National Production and Sustainable Employment, Challenges and Solutions, Boroujerd, Ayatollah Boroujerdi University(in Persian).
25) Krishna Reddy, Vaughan Clinton,(2016). Simulating Stock Prices Using Geometric Brownian motion: Evidence from Australian Companies, Australasian Accounting, Business and Finance Journal, 10(3), 23-47.
26) Mola'i, Saber; Barzani, Mohammadvaez; Samadi Saeed (2016).Modeling of Stock Price Behavior using Stochastic Differential Equations by Stochastic Volatility, Financial Knowledge of Securities Analysis
,Financial Studies, 9 (32), 13-1(in Persian).
27) Mohseni, Reza; SakhtKar madlal, Leila (1396). Estimating Stock priceof Energy Market including Oil, Gas, and Coal: Comparison of Linear and Nonlinear Markov Switching Regime Models, Iranian Journal of
Management Studies, 10 (3), 715-728(in Persian).
28) Nabavi Chashmi, Seyed Ali; Mokhtarinejad, Marieh (2016). Comparisonof Brownian Motion and Fractional Brownian motion and Garch Models in Estimating of Stock Returns Volatility, Journal of Financial Engineering and Securities Management, (29), 44- 25(in Persian).
29) Nisi, AbdulSadeh; Chamani Enbaji, Roya; Shojaee Manesh, Leily (2012). Three Basic Models in Financial Mathematics, Journal of AdvancedMathematical Modeling, 2 (1), 77-96(in Persian).
30) Omrani, Somayeh (2019). Price Forecasting using Stochastic Differential Equations and Time Series, MSc Thesis, Kharazmi University -Faculty of Economics Sciences, Tehran (in Persian).
31) Pedro P. Mota & Manuel L. Esquível, (2016). Model selection for stock prices data, Journal of Applied Statistics, 43:16, PP2977-2987.
32) Pourmoradi, Marzieh; Shabani, Zeinab; Sam Deliri, Leila (2016).Approach of Stochastic Differential Equations in Predicting of Financial Variables - A Case Study of Iran Khodro Stock in Tehran Stock Exchange(TSE), 9th Iranian Association Conference of Operational Research of Shiraz Industrial University (in Persian).
33) Primbs James A., RossBarmish B.,(2018). On Robustness of Simultaneous Long-Short Stock Trading Control with Time-Varying Price Dynamics , IFAC-PapersOnLine , 50(1), 12267-12272.
34) Rahimov, Kamran; Nemati, Omid (2015). Calibration of simulated models using Aimsun software, 1st International Conference on Human,Architecture, Civil and Urban Engineering, Tabriz (in Persian).
35) Shao, J. Chin. Ann. Math. Ser. B (2018). Ergodicity and First Passage Probability of Regime-Switching Geometric Brownian Motions, Chinese Annals of Mathematics, Series B, 39(4), 739–754
36) Sterman, J.D.,(1984). Appropriate Summary Statistics for Evaluating the Historical Fit of System Dynamics Models. Dynamica, 10, 51-66.
37) Sterman, John D. (2016). Business Dynamics (Systems Thinking andModeling for a Complex World), Bararpour kouroush et al., Vol1, Samtpublications (in Persian).
38) Steven P. Lalley, (2016). Stochastic Differential Equations, University of Chicago, Department of Statistics, Working paper, 1-11.
39) Zare, Hashem; Rezaei Sakha, Zeinab; Zare, Mohammad (2018). AnEquilibrium Model for Stochastic Simulation of Iranian Market Behavior:
An Approach from Physical Economics, Financial Management Strategy, 6( 21), pp. 104-73(in Persian).
40) Zhikun Ding, Wenyan Gong, Shenghan Li and Zezhou Wu(2018).
System Dynamics versus Agent-Based Modeling: A Review of Complexity Simulation, sustainability, 10, 2484; 1-13.
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13) Ahmad, S.; Tahar, R.M.; Muhammad-Sukki, F.; Munir, A.B.; Rahim, R.A., (2016). Application of system dynamics approach in electricity sector modelling: A review. Renewable and Sustainable Energy Reviews,56,29–37.
14) Asgari, Mohsen (2015). Investigating Fractional Brownian Motion andIts Role in Analysis of Stock Price Trend, MSc Thesis, Shahed University, Department of Applied Sciences(in Persian).
15) Azizi S.M.E.P.M., Neisy A. (2018). A New Approach in Geometric Brownian motion Model. In: Cao BY. (eds) Fuzzy Information and Engineering and Decision, Advances in Intelligent Systems and Computing,
vol 646.
16) Bongiorno E.G., Goia A., Vieu P. (2017). On the Geometric Brownian Motion assumption for financial time series. In: Aneiros G., G. Bongiorno E., Cao R., Vieu P. (eds) Functional Statistics and Related Fields.
Contributions to Statistics. Springer, Cham.
17) Braumann Carlos A, (2019). Introduction to Stochastic Differential Equations with Applications to Modelling in Biology and Finance (Chapter 8: Study of geometric Brownian motion (the stochastic Malthusian model orBlack–Scholes model( ), John Wiley & Sons Ltd
18) Boudreault Mathieu, Renaud Jean‐François (2019). Actuarial Finance: Derivatives, Quantitative Models and Risk Management, One (Chapter 14:Brownian motion), John Wiley & Sons, Inc.
19) Cleofe Giorgino Maria, Barnabè Federico, Martin Kunc (2019).Integrating qualitative system dynamics with accounting practices: The case of integrated reporting and resource mapping, System research and behavioral science, 1-22.
20) Dobrow Robert P., (2016). Introduction to Stochastic Processes with R (Chapter 8: Brownian motion), John Wiley & Sons, Inc.
21) Emami, Sophia (2014). Option Pricing and Switching Regime Model,Masters Thesis, University of Guilan, and Faculty of Mathematical Sciences (in Persian).
22) Forrester, J.W.,(1994). System dynamics, systems thinking, and soft, O.R. System Dynamic Review, 10, 245–256.
23) Kavetsky, Carlos,(2017). Calibrating a System Dynamic Model within an Integrative Framework to Test Foreign Policy Choices, Electronic Theses and Dissertations. 5578, 1-197
24) Khuchiyani Ramin, Hosseini Seyed Mohammad, Shojaee Fatemeh (2018). Comparison of Linear and Nonlinear Forecasting Models ofPharmaceutical Stocks Based on Stochastic Differential Equations,
Conference of National Production and Sustainable Employment, Challenges and Solutions, Boroujerd, Ayatollah Boroujerdi University(in Persian).
25) Krishna Reddy, Vaughan Clinton,(2016). Simulating Stock Prices Using Geometric Brownian motion: Evidence from Australian Companies, Australasian Accounting, Business and Finance Journal, 10(3), 23-47.
26) Mola'i, Saber; Barzani, Mohammadvaez; Samadi Saeed (2016).Modeling of Stock Price Behavior using Stochastic Differential Equations by Stochastic Volatility, Financial Knowledge of Securities Analysis
,Financial Studies, 9 (32), 13-1(in Persian).
27) Mohseni, Reza; SakhtKar madlal, Leila (1396). Estimating Stock priceof Energy Market including Oil, Gas, and Coal: Comparison of Linear and Nonlinear Markov Switching Regime Models, Iranian Journal of
Management Studies, 10 (3), 715-728(in Persian).
28) Nabavi Chashmi, Seyed Ali; Mokhtarinejad, Marieh (2016). Comparisonof Brownian Motion and Fractional Brownian motion and Garch Models in Estimating of Stock Returns Volatility, Journal of Financial Engineering and Securities Management, (29), 44- 25(in Persian).
29) Nisi, AbdulSadeh; Chamani Enbaji, Roya; Shojaee Manesh, Leily (2012). Three Basic Models in Financial Mathematics, Journal of AdvancedMathematical Modeling, 2 (1), 77-96(in Persian).
30) Omrani, Somayeh (2019). Price Forecasting using Stochastic Differential Equations and Time Series, MSc Thesis, Kharazmi University -Faculty of Economics Sciences, Tehran (in Persian).
31) Pedro P. Mota & Manuel L. Esquível, (2016). Model selection for stock prices data, Journal of Applied Statistics, 43:16, PP2977-2987.
32) Pourmoradi, Marzieh; Shabani, Zeinab; Sam Deliri, Leila (2016).Approach of Stochastic Differential Equations in Predicting of Financial Variables - A Case Study of Iran Khodro Stock in Tehran Stock Exchange(TSE), 9th Iranian Association Conference of Operational Research of Shiraz Industrial University (in Persian).
33) Primbs James A., RossBarmish B.,(2018). On Robustness of Simultaneous Long-Short Stock Trading Control with Time-Varying Price Dynamics , IFAC-PapersOnLine , 50(1), 12267-12272.
34) Rahimov, Kamran; Nemati, Omid (2015). Calibration of simulated models using Aimsun software, 1st International Conference on Human,Architecture, Civil and Urban Engineering, Tabriz (in Persian).
35) Shao, J. Chin. Ann. Math. Ser. B (2018). Ergodicity and First Passage Probability of Regime-Switching Geometric Brownian Motions, Chinese Annals of Mathematics, Series B, 39(4), 739–754
36) Sterman, J.D.,(1984). Appropriate Summary Statistics for Evaluating the Historical Fit of System Dynamics Models. Dynamica, 10, 51-66.
37) Sterman, John D. (2016). Business Dynamics (Systems Thinking andModeling for a Complex World), Bararpour kouroush et al., Vol1, Samtpublications (in Persian).
38) Steven P. Lalley, (2016). Stochastic Differential Equations, University of Chicago, Department of Statistics, Working paper, 1-11.
39) Zare, Hashem; Rezaei Sakha, Zeinab; Zare, Mohammad (2018). AnEquilibrium Model for Stochastic Simulation of Iranian Market Behavior:
An Approach from Physical Economics, Financial Management Strategy, 6( 21), pp. 104-73(in Persian).
40) Zhikun Ding, Wenyan Gong, Shenghan Li and Zezhou Wu(2018).
System Dynamics versus Agent-Based Modeling: A Review of Complexity Simulation, sustainability, 10, 2484; 1-13.
