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Forecasting (Business) – Definition

Forecasting (Business) Definition

Forecasting makes informed predictions based on historical data. These predictions regard the future trends in a specific market, industry, or economy. Forecasting techniques are used by businesses to make decisions related to budget allocation and upcoming expenses. The projected demands of the offerings are key factors used in forecasting.

A Little More on What is Forecasting

Investors use forecasting to predict the events that can affect a company — like those effecting sales expectations and share prices. Forecasting serves as an important benchmark for companies having strategic and operational planning goals. For example, stock analysts apply forecasting to deduce trends like future GDP or unemployment rates.

Stock analysts apply different forecasting techniques to predict future stock price movements. Statistical methods are used to determine the connection among multiple variables known to affect stock prices. These connections are based on time or the specific events. For instance, a sales forecast can be based on a particular time period (the period of the next 12 months) or the event (the competitor’s business purchase).

Economists use environmental assumptions relevant to the situation to analyze along side the understood forecasting variables. Based on these assumptions and variables, a suitable data set is chosen for evaluation. They analyze the data, and the forecast is made. Lastly, a verification period happens where they compare the forecast to the actual results to develop a more precise forecasting model for future.

Qualitative and Quantitative Forecasting

Qualitative forecasting models rely on expert opinions to draw conclusions about future realities. Some of qualitative forecasting models employ market research surveys and polls based (often based on the Delphi method). Quantitative methods of forecasting do not include expert opinions and use statistical data comprising of quantitative information. Quantitative forecasting models entail time series methods, indicators’ analysis, discounting, and econometric modeling.

References Business Forecasting

Academic Research on Forecasting

Multivariate normality and forecasting of business bankruptcy, Karels, G. V., & Prakash, A. J. (1987). Journal of Business Finance & Accounting14(4), 573-593.

Business cycles, inflation, and forecasting, Moore, G. H. (1983). NBER Books.

Forecasting practices in US corporations: survey results, Sanders, N. R., & Manrodt, K. B. (1994). Interfaces24(2), 92-100. This paper explores the reasons managers rely heavily on judgemental forecasting methods, and attempted to identify what needs of practitioners are not met with current procedures using a survey of 500 US corporations. It also cites the different obstacles to the use of formal forecasting method by managers.

Forecasting the business cycle using survey data, Öller, L. E. (1990). International Journal of Forecasting6(4), 453-461. This paper shows how survey data can be used as a good indicator for business cycle turning points, following the probability of the provision of inaccurate data by univariate models. A case study of the Finnish forest industry is offered as an example.

New developments in business forecasting, Lapide, L. (2001). The Journal of Business Forecasting20(2), 13.

Six rules for effective forecasting, Saffo, P. (2007). Harvard business review85(7/8), 122.

Implementing statistical criteria to select return forecasting models: what do we learn?, Bossaerts, P., & Hillion, P. (1999). The Review of Financial Studies12(2), 405-428. This paper explores the use of statistical model selection criteria to forecasting model. In this paper, the authors implement several model selection criteria in order to verify recent evidence of predictability in excess stock returns and to determine which variables are valuable predictors. results confirm the presence of in-sample predictability in an international stock market dataset, but discover that even the best prediction models have no out-of-sample forecasting power.

Forecasting: who, what, when and how, Herbig, P., Milewicz, J., & Golden, J. E. (1993). The Journal of Business Forecasting12(2), 16.

Forecasting and uncertainty in the economic and business world, Makridakis, S., Hogarth, R. M., & Gaba, A. (2009). International Journal of Forecasting25(4), 794-812. In this article, a discussion on the limited predictability in the economic and business environment. This paper also provides a framework that allows decision and policy makers to face the future — despite the inherent limitations of forecasting and the uncertainty, sometimes huge, surrounding most future-oriented decisions.

Sales forecasting practices: Results from a United States survey, Dalrymple, D. J. (1987). International Journal of Forecasting3(3-4), 379-391. The paper presents the results of a survey designed to discover how business firms prepare sales forecasts, what methods they prefer, and the accuracy of their predictions. The survey showed that subjective, extrapolation and naive techniques are widely used by American business firms in various forecasting situations. Also, some business firms are reducing forecasting errors by making greater use of computers and seasonal adjustments.

Sales forecasting methods and accuracy, Dalrymple, D. J. (1975). Business Horizons18(6), 69-73.

Implementing collaborative forecasting to improve supply chain performance, McCarthy, T. M., & Golicic, S. L. (2002). International Journal of Physical Distribution & Logistics Management32(6), 431-454. This research employs case study methodology to explore the synergies to be gained from combining sales forecasting annd collaboration for better organizational performance. Depth interviews were conducted with executives at three firms currently engaged in collaborative forecasting with supply chain partners. Seven guidelines to implementing interfirm collaborative forecasting are presented.

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