Document Type : Research Paper


1 Department of Industrial Engineering, Productivity Management System, Industrial Management Institute (IMI), Tehran, Iran.

2 Technology Enterprises Incubator Center, University of Mazandaran, Babolsar, Iran.

3 Department of Economy and Political Science, Central Branch, Islamic Azad University, Tehran, Iran.


The present study aims at suggesting a model for intelligent investment, through enabling us to be Autoregressive Integrated Moving Average of ARIMA and seasonal coefficient. In this study, the researcher uses seasonal fluctuation Model. The previous trend of time series, related to the companies for a period of 11 years, from 2006 to 2017, was carried out based on seasonal data. Then the researcher predicted the final price based on moving average method. In the next stage, the proportion of real final price and predicted the final price is calculated regarding each period. Then, the seasonal coefficient average is calculated for similar seasons. In the final stage, the value of a prediction, for a given period, is calculated when moving average method is multiplied by a seasonal coefficient average. As a result, seasonal coefficient of a given stock is derived.


[1]       Abounoori, E., Tour, M. J. P. A. S. M., & Applications, i. (2019). Stock market interactions among Iran, USA, Turkey, and UAE. 524, 297-305.
[2]       Ajayi, R. A., Mehdian, S., & Perry, M. J. (2004). The day-of-the-week effect in stock returns: further evidence from Eastern European emerging markets. Emerging Markets Finance and Trade, 40(4), 53-62.
[3]       Alam, I. M. S., & Sickles, R. C. (1998). The relationship between stock market returns and technical efficiency innovations: evidence from the US airline industry. Journal of Productivity Analysis, 9(1), 35-51.
[4]       Ariel, R. A. (1987). A monthly effect in stock returns. Journal of Financial Economics, 18(1), 161-174.
[5]       Balaban, E. (1995). Day of the week effects: new evidence from an emerging stock market. Applied Economics Letters, 2(5), 139-143.
[6]       Baldwin, G. H. (1982). The Delphi technique and the forecasting of specific fringe benefits. Futures, 14(4), 319-325.
[7]       Bodie, Z., Kane, A., & Marcus, A. J. (2002). Investments. International Edition. New York, Boston, London.
[8]       Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control: John Wiley & Sons.
[9]       Brockwell, P., & Davis, R. Time series: theory and methods. 1991. Springer, 21, 33-34.
[10]    Brooks, C., & Persand, G. (2001). Seasonality in Southeast Asian stock markets: some new evidence on day-of-the-week effects. Applied Economics Letters, 8(3), 155-158.
[11]    Brown, P., Keim, D. B., Kleidon, A. W., & Marsh, T. A. (1983). Stock return seasonalities and the tax-loss selling hypothesis: Analysis of the arguments and Australian evidence. Journal of Financial Economics, 12(1), 105-127.
[12]    Cataldo, I., Anthony, J., & Savage, A. A. (2000). The January effect and other seasonal anomalies: A common theoretical framework: Emerald Group Publishing Limited.
[13]    Cholette, P. A. (1982). Prior information and ARIMA forecasting. Journal of Forecasting, 1(4), 375-383.
[14]    Cholette, P. A., & Lamy, R. (1986). Mutivariate ARIMA forecasting of irregular time series. International Journal of Forecasting, 2(2), 201-216.
[15]    Coutts, J. A., & Sheikh, M. A. (2002). The anomalies that aren't there: the weekend, January and pre-holiday effects on the all gold index on the Johannesburg Stock Exchange 1987-1997. Applied Financial Economics, 12(12), 863-871.
[16]    De Alba, E. (1993). Constrained forecasting in autoregressive time series models: A Bayesian analysis. International Journal of Forecasting, 9(1), 95-108.
[17]    De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443-473.
[18]    Ebrahimzadeh Shermeh, H., Alavidoost, M. H., & Darvishinia, R. J. J. o. A. R. o. I. E. (2018). Evaluating the efficiency of power companies using data envelopment analysis based on SBM models: a case study in power industry of Iran. 5(4), 286-295.
[19]    Erdugan, R. (2012). The effect of economic factors on the performance of the Australian stock market. Victoria University,
[20]    Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of finance, 25(2), 383-417.
[21]    Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617.
[22]    Fogarty, D. W., Hoffmann, T. R., & Stonebraker, P. W. (1989). Production and operations management: South-Western Pub.
[23]    Guo, S., & Wang, Z. (2008). Market efficiency anomalies: A study of seasonality effect on the Chinese stock exchange. In: Handelshögskolan vid Umeå universitet.
[24]    Jaffe, J., & Westerfield, R. (1985). The week‐end effect in common stock returns: the international evidence. The journal of finance, 40(2), 433-454.
[25]    Kohli, R. K., & Kohers, T. (1992). The week-of-the-month effect in stock returns: The evidence from the S&P composite index. Journal of Economics and Finance, 16(2), 129.
[26]    Kuria, A. M., & Riro, G. K. (2013). Stock market anomalies: A study of seasonal effects on average returns of Nairobi securities exchange. Research Journal of Finance and Accounting, 4(7), 207-215.
[27]    Liano, K., Marchand, P. H., & Huang, G.-C. (1992). The holiday effect in stock returns: Evidnece from the OTC market. Review of Financial Economics, 2(1), 45.
[28]    Lim, S., Oh, K. W., & Zhu, J. (2014). Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market. European Journal of Operational Research, 236(1), 361-368.
[29]    Mazal, L. (2009). Stock market seasonality: Day of the week effect and January effect. Master’s thesis, Universidad del Pas Vasco/Euskal Herriko Unibertsitatea,
[30]    Pankratz, A. (2009). Forecasting with univariate Box-Jenkins models: Concepts and cases (Vol. 224): John Wiley & Sons.
[31]    Raj, M., & Kumari, D. (2006). Day-of-the-week and other market anomalies in the Indian stock market. International Journal of Emerging Markets, 1(3), 235-246.
[32]    Ramezanian, R., Peymanfar, A., & Ebrahimi, S. B. J. A. S. C. (2019). An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market. 105551.
[33]    Rozeff, M. S., & Kinney Jr, W. R. (1976). Capital market seasonality: The case of stock returns. Journal of Financial Economics, 3(4), 379-402.
[34]    Schwert, W. (2003). Anomalies and Market Efficiency, Handbook of the Economics of Finance, ed. Constantinides, Harris and Stultz. In: Elsevier.
[35]    Seyyed, F. J., Abraham, A., & Al-Hajji, M. (2005). Seasonality in stock returns and volatility: The Ramadan effect. Research in International Business and Finance, 19(3), 374-383.
[36]    Shermeh, H. E., Najafi, S., & Alavidoost, M. (2016). A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies. Energy, 112, 686-697.
[37]    Thaler, R. H. (1987). Anomalies: the January effect. Journal of Economic Perspectives, 1(1), 197-201.
[38]    Wachtel, S. B. (1942). Certain observations on seasonal movements in stock prices. The journal of business of the University of Chicago, 15(2), 184-193.
[39]    Weigerding, M., & Hanke, M. J. B. R. (2018). Drivers of seasonal return patterns in German stocks. 11(1), 173-196.
[40]    Wissema, J. (1987). Trends in Technology Forecasting: R&D Management. 研究技術計画, 2(4), 520.
[41]    Yakob, N. A., Beal, D., & Delpachitra, S. (2005). Seasonality in the Asia Pacific stock markets. Journal of Asset Management, 6(4), 298-318.
[42]    Ledolter, J., Box, G. E., Tiao, G. C., & WISCONSIN UNIV MADISON DEPT OF STATISTICS. (1976). Topics in Time Series Analysis IV. Various Aspects of Parameter Changes in ARMA Models (No. 499). Technical Report.