Shock Modeling of Influencing Variables on Stock Return Forecasting with the Approach of BMA-BVR Models

Subject Areas : Labor and Demographic Economics

Majid Abdi ¹ , Seied Atefe Hosseini ² , Amir Gholam Abri ³

1 - PhD Candidate in accounting, Department of Accounting, Firuzkuh Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor, Department of Accounting, Firuzkuh Branch, Islamic Azad University, Tehran, Iran (Corresponding Author)
3 - Associate Professor, Department of Mathematics, Firuzkuh Branch, Islamic Azad University, Tehran, Iran

Received: 2022-12-12 Accepted : 2022-12-29 Published : 2022-11-22

Keywords: Stock Returns, Systematic Risk, Micro factors, Macro factors,

Abstract :

The purpose of the research is to predict stock returns using Bayesian averaging and BVAR. The current research is based on the applied research method and MATLAB 2021 and EVIEWS12 have been used to estimate the model. The time period of the research includes the years 2010 to 2019. First, 11 non-fragile variables out of 64 entered variables were identified with the Bayesian averaging model approach. Based on the results of the current ratio; ROE; P/E; oil revenue; The increasing coefficient of money in the whole period has a positive effect and inflation fluctuation variables; debt ratio; fluctuation of GDP growth; unofficial market exchange rate; Interest rate and systematic risk have a negative effect on yield in the whole period. Based on the results of variance analysis, the most explanatory of changes in stock returns is caused by the variable itself (20 percent), followed by interest rate variables (14 percent); Inflation volatility (13 percent) and debt ratio and systematic risk (10 percent) have the highest effect in explaining yield changes.

References:

• بهمنی، مریم. پورزرندی، محمد ابراهیم. مینویی، مهرزاد. (1401). عوامل موثر بر پیش بینی بازده سهام؛ استفاده از تحلیل حوزه دانش و تکنیک دلفی-فازی. فصلنامه علمی کارافن, (در حال انتشار),


• راغ، فاطمه. (1400). ارایه الگوی بهینه سبد سهام مبتنی بر وابستگی ساختاری بازده سهام، رساله دکتری، دانشگاه آزاد، علوم تحقیقات


• ریموند پی نوو، مدیریت مالی (1380). جلد دوم، ترجمه و اقتباس؛ دکتر علی جهانخانی و دکتر علی پارسائیان، انتشارات سمت.


• سینا، افسانه. فلاح، میرفیض. (1399). مقایسه عملکرد مدل‌های ارزش در معرض ریسک و کاپیولا- CVaR جهت بهینه‌سازی پرتفوی در بورس اوراق بهادار تهران. چشم انداز مدیریت مالی, 10(29), 125-146.


• صادقی، حجت اله. دهقان منشادی، سمانه. (1395). انحراف از توزیع نرمال و تاثیر آن بر ارزش در معرض خطر تفاضلی (مورد مطالعه: شرکت‌های حاضر در صنعت مالی بورس اوراق بهادار). دانش مالی تحلیل اوراق بهادار, 9(31), 69-83.


• کاظمی نجف آبادی، مصطفی. (1400). عوامل مؤثر بر بازدهی صندوق‎های‎ سرمایه‎گذاری مشترک در اقتصاد ایران. دو فصلنامه جستارهای اقتصادی, 18(35), 145-168.


• گرجی پور، محمد جواد. عثمانی، فریبا. ابراهیمی سالاری، تقی. (1400). بررسی اثر عوامل کلان اقتصادی بر بازدهی سهام در طی شیوع همه‌گیری کووید-19 (مورد مطالعه صنایع منتخب بورس اوراق بهادار تهران). فصلنامه پژوهش‌های اقتصاد صنعتی, 5(17), 59-70.


• محمدی، تیمور. فقهی کاشانی، محمد رضا. صامعی، مهدی. (1400). اثر اهرمی و نقش نسبت بدهی در شرکت‌های منتخب بورس اوراق بهادار تهران. پژوهش‌های اقتصادی ایران


• مهرآرا، محسن. بهلولوند، الهه. (1395). بررسی عوامل موثر بر ریسک نقدینگی در صنعت بانکداری مبتنی بر رویکرد بیزین: مطالعه موردی بانک‌های ایران. پژوهشنامه اقتصاد کلان 11(22)، 13-37.


• Alexio, H., Akram, U. and Sui, Y. (2022). The impact of macroeconomic indicators on US stock returns. Asia Pacific Journal of Marketing and Logistics, (March). doi: 10.1108/APJML-05-2018-0191.


• Angelini, E.; Banbura, M., & Runstler, G., (2010). Estimating and forecasting the Euro area monthly national accounts from a dynamic factor model. OECD Journal: Journal of Business Cycle Measurement and Analysis, (1): 1-22.


• Artis, M. ; Banerjee, A., & Marcellino, M. (2007). Factor forecasts for the UK. Journal of Forecasting, 24 (4): 279 -298.


• Aye, G . ; Gupta, R . ; Hammoudeh, Sh., & Kim, W. J. (2014). Forecasting the Price of Gold Using Dynamic Model Averaging . University of Pretoria, Department of Economics Working Paper Series.


• Aye, G.; Gupta, R.; Hammoudeh, Sh., & Kim, W. J. (2014). Forecasting the Price of Gold Using Dynamic Model Averaging. University of Pretoria, Department of Economics Working Paper Series.


• Wallingford, J. Bicksler, Discussion, J. Finance, 29 (1974), pp. 392-398.


• Balcilar, M.; Gupta, R.; Eyden, R.; Thompson, K., & Majumdar, A. (2018). Comparing the forecasting ability of financial conditions indices:The case of South Africa. The Quarterly Review of Economics and Finance, 69(C): 245-259.


• Belmonte, M., & Koop,G. (2014). Model Switching and Model Averaging in Time-Varying Parameter Regression Models. in Ivan Jeliazkov, Dale J. Poirier (ed.) Bayesian Model Comparison (Advances in Econometrics, Volume 34) Emerald Group Publishing Limited: 45-69.


• Boubaker, H. and Sghaier, N. (2013). Portfolio optimization in the presence of dependent financial returns with long memory: A copula based approach, Journal of Banking and Finance, 37 (2), 361-377.


• Buncic, D., & Moretto, C. (2015). Forecasting copper prices with dynamic averaging and selection models. North American Journal of Economics and Finance, 33: 1 -38.


• Asness, A. Frazzini, The devil in HML’s details, J. Portfolio Manag., 39 (4) (2013), pp. 49-68.


• Clements, M. P., & Krolzig, H. M. (1998). A comparison of the forecast performance of Markov-switching and Threshold Autoregressive Models of US GNP. The Econometrics Journal, 1 (1): 47-75.


• Clements, M. P., & Smith, J. (1997). The performance of alternative forecasting methods for SETAR models. International Journal of Forecasting, 13 (4):463-475.


• Bower, R. Bower, D. Logue, Arbitrage pricing theory and utility stock returns, J. Finance, 39 (4) (1984), pp. 1041-1054.


• Drachal, K. (2016). Forecasting spot oil price in a dynamic model averaging framework have the determinants changed over time?. Energy Economics, 60:35-46.


• Ferreira, D., & Palma A. (2015). Forecasting inflation with the Phillips curve: A dynamic model averaging approach for Brazil. Rev. Bras.Econ, 69(4): 451-465.


• Filippo, D.G. (2015). Dynamic model averaging and CPI inflation forecasts: A comparison between the Euro area and the United States. Journal of Forecasting, 34(8): 619-648.


• Forni, M.; Hallin, M.; Lippi, M., & Reichlin, L. (2003). Do financial variables help forecasting inflation and real activity in the Euro area?. Journal of Monetary Economics, 50 (6): 1243-55.


• Cubadda & S. Grassi & B. Guardabascio, 2022. "The Time-Varying Multivariate Autoregressive Index Model," Papers 2201.07069, arXiv.org


• Hamilton, J. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57 (2): 357-384.


• Koop, G & Korobilis, D. (2013). A New Index of Financial Conditions. University of Glasgow, Adam Smith Business School, Gilbert Scott building, Glasgow.


• Koop, G. (2012). Using VARs and TVP-VARs with many macroeconomic variables. Central European Journal of Economic Modelling and Econometrics, 4: 143-167, working paper version.


• Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4):267-358.


• Koop, G., & Korobilis, D. (2011). UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?. Economic Modelling, 28: 2307-18.


• Koop, G., & Korobilis, D. (2012). Forecasting inflation using dynamic model averaging. International Economic Review, 53(3): 867-886.


• Koop, G.; McIntyre, S.; Mitchell, J. & Poon, A., (2020). “Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970”. Journal of Applied Econometrics, No. 35(2), Pp: 176-197.


• Korobilis, D. (2013). Assessing the transmission of monetary policy shocks using time -varying parameter dynamic factor models. Oxford Bulletin of Economics and Statistics 75:157-179.


• Lee, C.Y., Lai, C.K., (2020). Small probabilistic discounts stimulate spending: Pain of paying in price promotions. Journal of the Association for Consumer Research, 4(2), 160–171..


• Marcellino, M.; Stock, J., & Watson M. (2003). Macroeconomic forecasting in the Euro area: Country specific versus area-wide information. European Economic Review, 47 (1): 1-18.


• McMillan, D.G. (2018). Non-linear Forecasting of Stock Returns: Does Volume Help? International Journal of forecasting, 23(1): 115–126..


• Naser, H. (2014). An Econometric Investigation of Forecasting GDP, Oil Prices,and Relationships among GDP and Energy Sources. PhD thesis. University of Sheffeild.


• Naser, H., & Alaali, F. (2018). Can oil prices help predict US stock market returns: An evidence using a DMA approach. Empirical Economics, 55(4): 1757-77.


• Nicoletti, G., & Passaro, R. (2012). Sometimes it helps the evolving predictive power of spreads on GDP dynamics, Working Paper Series, European Central Bank, N o . 1447


• Rechvalsky, T, Ven, J. (2021). Unsystematic risk on stock returns. Journal of Marketing, 79(2), 62–78. doi: 10.1509/jm.12.0408.


• Risse M., & Kern M. (2016). Forecasting house -price growth in the Euro area with dynamic model averaging. The North American Journal of Economics and Finance, 38: 70 -85 .


• Saleille, N. (2015). Forecasting the French GDP: Essay on statistical models to forecast aggregate macroeconomic variables. Master thesis, Paris School of Economics.


• Schumacher, C. (2007). Forecasting German GDP using alternative factor models based on large datasets. Journal of Forecasting, 26 (4): 271-302.


• Stock, J., & Watson, M. (1998). Diffusion indexes. NBER Working Paper No.w6702.


• Stock, J., & Watson, M. (2002a). Forecasting using principal components from a large number of predictors. Journal of the American statistical association, 97(460): 1167-79.


• Stock, J., & Watson, M. (2002b). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20 (2): 147-162.


• Stock, J., & Watson, M. (2005). An Empirical Comparison of Methods for Forecasting using Many Predictors. Manuscript, Princeton University.


• Stock, J., & Watson, M. (2006). Macroeconomic forecasting using many predictors. In: Elliott, G., Granger, C., Timmerman, A. (Eds.), Handbook of Economic Forecasting. North Holland, Amsterdam.