Application of Exponential Smoothing Method for Forcasting Revenue of Vietnamese Steel Companies

Date Received: 04-03-2014

Date Accepted: 27-03-2014

Date Published: 06-08-2025

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KINH TẾ XÃ HỘI VÀ PHÁT TRIỂN NÔNG THÔN

How to Cite:

Anh, N., Ha, L., & Giam, D. (2025). Application of Exponential Smoothing Method for Forcasting Revenue of Vietnamese Steel Companies. Vietnam Journal of Agricultural Sciences, 12(2), 205–213. https://doi.org/10.31817/tckhnnvn.2014.12.2.

Application of Exponential Smoothing Method for Forcasting Revenue of Vietnamese Steel Companies

Nguyen Quoc Anh (*) , Le Thanh Ha , Do Quang Giam

  • Tác giả liên hệ: [email protected]
  • Keywords

    Exponential smoothing, forecast, revenue, steel sector

    Abstract


    For the time being, the tools to which the investors choose to make decisions in Vietnam stock market still have limits, especially the tools to predict future business results of enterprises. Exponential smoothing is one of appropriate methods to forcast a timeseries data based on the past observations. Therefore, the article focused on applying this method to forcast revenue and business performance of the listed steel companies. The main purpose was to support decision making of investors and business owners. The forecasted results indicated that the revenues in the first quarter of 2013 of Hoa Sen Joint Stock Company, Lien Huu A Joint Stock Company and Viet-Y Joint Stock company were VND 2,488 billion, VND1,820 billion,and VND 1,137 billion, respectively. The article also pointed out difference between the forecates and actual revenues of the selected companies and gives recommendations for managers and investors in decision making.

    References

    Chaman L. Jain (2007). “Benchmarking Forcasting Models”,The journal of Business Forcasting, 25(4): 14-17.

    Chaman L. Jain (2006). “Benchmarking Forcasting Practices in Corporate America”, The journal of Business Forcasting, 24(4).

    Công Thắng, Hồng Quân (2006). “Dự báo sai giá thép, hại doanh nghiệp”, Báo Lao động.

    Online link: http://vietbao.vn/Kinh-te/Du-bao-sai-gia-

    thep-hai-doanh-nghiep/70057240/87/<19/3/2014>.

    Đỗ Quang Giám, Vũ Thị Hân, Lý Thị Lan Phương, Nguyễn Thu Thủy (2012). “Xây dựng mô

    hình Arima cho dự báo lượng khách quốc tế đến Việt Nam”, Tạp chí Khoa học và Phát triển,

    (2): 366-377.

    Fujio John M. Tanaka (2010). “A Short review of steel demand forecasting methods”,UNIDO 2010.

    Online link: reposit.sun.ac.jp/dspace/.../ 740/1<19/3/2014>.

    Hanke, J.E. & Wichern, D.W. (2005). “Business forcasting”, 8th Edition, Prentice Hall.

    Hiệp hội thép Việt Nam (2012). “Báo cáo phân tích tình hình ngành thép Việt Nam”, truy cập tại http:// satthep.com.vn.

    Joseph J. LaViola Jr., (2003). “An Experiment Comparing Double Exponential Smoothing and Kalman Filter-Based Predictive Tracking Algorithms”, Brown University Technology Center for Advanced Scientific Computing and Visualization PO Box 1910, Providence, RI, 02912

    Nandini Kannana, D. K. (2001). “Estimating parameters in the damped exponential model”, Signal processing, 81(11): 2343-2351.

    Nguyễn Trọng Hoài (2003). “Mô hình hóa chuỗi thời gian trong kinh doanh và kinh tế”, Ấn bản lần 2, Nhà xuất bản thống kê.

    Nguyễn Trọng Hoài (2009). “Dự báo và phân tích dữ liệu trong kinh tế và tài chính”, Nhà xuất bản Thống kê.

    Pokahontas Nguyen (2011). “Ích lợi của việc lên ngân sách và dự báo doanh thu”, Business Knowledge Resource.

    Robert D.Klassen and Benito E. Flores (2001). “Forcasting Practices of Canadian Firms: Survey Results and Comparison”, International Journal of

    Production Economics, 70: 163-174

    Thu Hà và Sông Trà (2012). “Ðầu tư theo phong trào, hàng loạt doanh nghiệp đóng cửa, giải thể”, Báo Nhân Dân điện tử.

    Online link: http://socongthuong.namdinh.gov. vn/Home/CNthuongmai/ 2012/131/au-tu-theo-phong-trao-hang-loat-doanh-nghiep-dong-cua-giai. aspx<15/1/2014>.

    Wilson, J.Holton & Barry Keating (2007). “Business Forecasting With Accompanying Excel-Based ForecastXTM Software”, 5th Edition, New York: McGraw-Hill Irwin.