Designing a Model for Projection of Tehran Exchange Return Employing Autoregressive Moving Average (ARMA) and Autoregressive Moving Average with External Inputs (ARMAX) Models and Assessing the Performance Thereof

Document Type : Original Article

Author

Shahid Beheshti University - Faculty member

Abstract

Capital markets’ return is influenced by multiple and various factors. These factors range from macro global factors to dependent variable historical behaviors. Each of many researchers has selected a segment or segments of this vast range of factors impacting capital market return in different countries and embarked on devising a model for projection of the respective capital market return. The present research has utilized self-explanatory and combined models for the purpose of modeling and projecting Tehran Exchange’s return using Autoregressive Moving Average (ARMA) and Autoregressive Moving Average with External Inputs (ARMAX) models. In this research, to utterly expound the model and to factor in maximum factors, following examination of  return issues and the factors impacting return, the subject of projection and the common methodology thereof have been surveyed and different models of projecting capital market return have been examined in detail. Then, Classic Linear Regression Models, Autoregressive Moving Average (ARMA) and Autoregressive Moving Average with External Inputs (ARMAX) models were used to predict the return of Tehran Exchange. After estimating the above models, deploying 99-period data and confirming the power of expressing them via applying diagnostic tests, the return of Tehran Exchange for the next 4 periods was projected. These predictions by applying estimation models were compared with the real data and the optimum model was selected using Akaike Information Criterion (AIC), Schwartz-Bayesian Information Criterion, Hannan-Quinn and also MSE, MAE, MAPE criteria. The final result indicates superiority of ARMA over ARMAX

Keywords

References

  1. Bidram, R. (2002). "Eviews in econometrics" Tehran: productivity charter publishing (in Persian).
  2. Bilson, Christopher M., Timothy, J., Brailsford, & Vincent J. Hooper (2001). “SelectingMacroeconomic Variables as Explanatory Factors of Emerging Stock Market Returns”. Pacific-Basin Finance Journal, 9, 401-426.
  3. Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting: springer.
  4. Caiado, J. (2004). "Modelling and forecasting the volatility of the portuguese stock index PSI-20". Portuguese Journal of Management Studies, 51, 3-21.
  5. Chang, T., Chen, W.-Y., Gupta, R., & Nguyen, D. K. (2015). Are stock prices related to the political uncertainty index in OECD countries? Evidence from the bootstrap panel causality test. Economic Systems, 39(2), 288-300. doi:https://doi.org/10.1016/j.ecosys.2014.10.005
  6. Chuangxia Huang, Xu Gong,, Xiaohong Chen, & Fenghua Wen (2013). Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model; Abstract and Applied Analysis; Volume 2013, Article ID 143194, 13 pages.
  7. Damodar, N. Gujarati (2011). "Basic econometrics" translated by: hamid abrishami, university of terhran publication (In Persian).
  8. Ederington, Louis, Wei Guan. (2004). "Forecasting Volatility", Journal of Futures Markets.
  9. Febrian, E., & Aldrin, H. (2006). "MODELING AND FORECASTING JAKARTA STOCK EXCHANGE: STOCK MARKET VOLATILITY", The 8th IRSA International Conference.
  10. Friedmann, M. (1953). “Eaaays in positive economics”, university of Chicago press.
  11. Fung, laurence & Ip wing yu (2008). “PREDICTING STOCK MARKET RETURNS BY COMBINING FORECASTS”, www.ssrn.com.
  12. Gay, R. (2008). "Effect of Macroeconomic Variables on Stock Market Returns for Four Emerging Economies: Brazil, Russia, India, and China". International Business & Economics Research Journal, 7(3), 58.
  13. Gencay, R., & Stengos, T. (1998). "Moving Average Rules, Volume and the Predictability of Security Returns with Feedforward Networks". Journal of Forecasting. 17, 401-414
  14. Guru-Gharana, K. K., Rahman, M., & Parayitam, S. (2009). "Influences of Selected Macroeconomic Variables on U.S. Stock Market Returns and their Predictability over Varying Time Horizons". Academy of Accounting and Financial Studies Journal, 13(1), 13-30
  15. Hui-Shan Lee, David Ching-Yat Ng, Teck-Chai Lau, Chee-Hong Ng (2016). Forecasting Stock Market Volatility on Bursa Malaysia Plantation Index. International Journal of Finance and Accounting, 5(1), 54-61. doi: 10.5923/j.ijfa.20160501.07.
  16. Johnson, L. A., & Montoyomery (1976). "Forecasting and time series analysis", Newyrok: Macgraw Hill.
  17. Kareemzadeh, M. (2007). “The survey of relationship betwen macro monetary factors and tehran stock market return". The economic research quarterly publication, 26 (In Persian).
  18. Kothari, S., Lewellen, J., & Warner.B. (2006). "Stock returns, aggregate earnings surprises, and behavioral finance". Journal of Financial Economics, 79, 537-568.
  19. Kovačić. Zlatko. J. (2008). "Forecasting Volatility: Evidence from the Macedonian Stock Exchange". International Research Journal of Finance and Economics, 18.
  20. Leigh, William, Ross Hightower & Naval Modani (2005). "Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike". Expert Systems with Applications, 28, 1-8.
  21. MCMillan, David, Alan Speight and Owain Apgwilym. (2000). "Forecasting UK stock market volatility". Applied Financial Economics, 10, 435-448.
  22. Miguel, A., Ferreira, Pedro Santa-Clara (2011). "Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole". Journal of Financial Economics, 514-537.
  23. Moeeni, A., Ahrari, M. & Hamouni, A. (2010) "Modelling and Forecasting the Tehran Stock Exchange Index". Quarterly Journal of Economic Research and Policies, 50 (In Persian).
  24. Okech, Timothy Chrispinus; Mugambi, Mike (2016). Effect of Macroeconomic Variables on Stock Returns of Listed Commercial Banks in Kenya; USIU-A Digital Repository Home; Chandaria School of Business.
  25. Osoolian, M. (2006). "The survey of macro economic factors impact on tehran stock market return" university of Tehran, faculity of management (In Persian).
  26. Piraee, M. R., & Shahsavar, M., R. (2010). "The Impacts of Macroeconomic Variables on the Iranian Stock Market". The economic research quarterly publication, 9, 21-38 (In Persian).
  27. Pourebrahimi, M., R. (2010). "Modeling and forecasting the volatility of Tehran Exchange Dividend Price Index (TEDPIX)". Journal of financial research, 30, 23-34 (In Persian).
  28. Raee, R. (1998). "Introducing a portfolio Model using with artificial intelligence" university of terhran, faculity of management (In Persian).
  29. Sadeghi Sharif, J. (2004). "Introducing a conitional CAPM IN TEHRAN STOCK MARKET". University of terhran, faculity of management (In Persian).
  30. Sajadi, H., Azar, A., Farazmand, H., & Soufi, H. A. (2011). "The survey of relationship betwen macro economic factors and tehran stock market return". Accounting research publication, 6 (In Persian).
  31. Samadi, S., Shirani Fakhr, Z., & Davarzadeh, M. (2008). "Investigating the Influence of World Price of Gold and Oil on the Tehran Stock Exchange Index: Modelling and Forecasting". The economic SURVEY quarterly publication, 2 (In Persian).
  32. SHrivastava, Utkarsh. Gyan Prakash, Joydip Dhar, Saurabh Porwal, (2010). "Regression Based Approach to Filter Conditional Mean and Variance Model Forecast of Stock Market Returns". International Research Journal of Finance and Economics1450-2887 Issue 50.
  33. William, M., & Umit, G. (2010). "Aggregate Market Reaction to Earnings Announcements". Journal of Accounting Research.
  34. Yang, X. (2011). The effects of economic and political events on the behaviour of Stock Market Index in China. Paper presented at the 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC).

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