Back To: INSURANCE & RISK MANAGEMENT
What is Forecasting?
Forecasting makes informed predictions about future occurrences based on historical data. In business, these predictions regard future trends in a specific market, industry, or economy. Forecasting techniques are employed in risk management, strategic management, and financial management.
A Little More on What is Forecasting
Risk managers use forecasting to identify potential unfavorable situations or occurrences that could negatively affect organizational performance. This situations can occur externally or internally – both of which have an internal effect.
Strategic managements use forecasting to predict elements of customer demand and occurrences within the competitive market or industry.
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 methods to deduce trends like future GDP or unemployment rates.
Economists use environmental assumptions to analyze along with other understood environmental elements to understand cause and effect within an economy.
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
Forecasting practices in US corporations: survey results, Sanders, N. R., & Manrodt, K. B. (1994). Interfaces, 24(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 Forecasting, 6(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.
Implementing statistical criteria to select return forecasting models: what do we learn?, Bossaerts, P., & Hillion, P. (1999). The Review of Financial Studies, 12(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 and uncertainty in the economic and business world, Makridakis, S., Hogarth, R. M., & Gaba, A. (2009). International Journal of Forecasting, 25(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 Forecasting, 3(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.
Implementing collaborative forecasting to improve supply chain performance, McCarthy, T. M., & Golicic, S. L. (2002). International Journal of Physical Distribution & Logistics Management, 32(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.