What Is Business Forecasting?
It is not unusual to hear a company's management speak about forecasts: "Our sales did not meet the forecasted numbers," or "we feel confident in our forecasted economic growth and expect to exceed our targets." In the end, all financial forecasts are informed guesses regardless of whether they reflect the specifics of a business, such as sales growth, or predictions for the economy as a whole. In this article, we look at some of the methods and processes behind financial forecasts as well as the risks in trying predict the future.
- Forecasting is valuable to businesses so that they can make informed business decisions.
- Financial forecasts are fundamentally informed guesses, and there are risks involved in relying on past data and methods that cannot include certain variables.
- Forecasting approaches include qualitative models and quantitative models.
The Basics Of Business Forecasting
Understanding Business Forecasting
Companies use forecasting to help them develop business strategies. Financial and operational decisions are made based on economic conditions and how the future looks, albeit uncertain. Past data is collected and analyzed so that patterns can be found. Today, big data and artificial intelligence has transformed business forecasing methods.
Qualitative models have typically been successful with short-term predictions, where the scope of the forecast was limited. Qualitative forecasts can be thought of as expert-driven, in that they depend on market mavens or the market as a whole to weigh in with an informed consensus. Qualitative models can be useful in predicting the short-term success of companies, products, and services, but has limitations due to its reliance on opinion over measurable data. Qualitative models include:
- Market Research Polling a large number of people on a specific product or service to predict how many people will buy or use it once launched.
- Delphi Method: Asking field experts for general opinions and then compiling them into a forecast.
Quantitative models discount the expert factor and try to remove the human element from the analysis. These approaches are concerned solely with data and avoid the fickleness of the people underlying the numbers. These approaches also try to predict where variables such as sales, gross domestic product, housing prices, and so on, will be in the long term, measured in months or years. Quantitative models include:
- The indicator approach: The indicator approach depends on the relationship between certain indicators, for example, GDP and the unemployment rate remaining relatively unchanged over time. By following the relationships and then following leading indicators, you can estimate the performance of the lagging indicators by using the leading indicator data.
- Econometric modeling: This is a more mathematically rigorous version of the indicator approach. Instead of assuming that relationships stay the same, econometric modeling tests the internal consistency of datasets over time and the significance or strength of the relationship between datasets. Econometric modeling is applied to create custom indicators for a more targeted approach. However, econometric models are more often used in academic fields to evaluate economic policies.
- Time Series Methods: Time series use past data to predict future events. The difference between the time series methodologies lies in the fine details, for example, giving more recent data more weight or discounting certain outlier points. By tracking what happened in the past, the forecaster hopes to get at least a better than average view of the future. This is the most common type of business forecasting because it is inexpensive and no better or worse than other methods.
The Elements of Forecasting
There is substantial variation on a practical level when it comes to business forecasting. However, on a conceptual level, all forecasts follow the same process.
- A problem or data point is chosen. This can be something like "will people buy a high-end coffee maker?" or "what will our sales be in March next year?"
- Theoretical variables and an ideal data set are chosen. This is where the forecaster identifies the relevant variables that need to be considered and decides how to collect the data.
- Assumption time. To cut down the time and data needed to make a forecast, the forecaster makes some explicit assumptions to simplify the process.
- A model is chosen. The forecaster picks the model that fits the dataset, selected variables, and assumptions.
- Analysis. Using the model, the data is analyzed, and a forecast is made from the analysis.
- Verification. The forecast is compared to what actually happens to identify problems, tweak some variables, or, in the rare case of an accurate forecast, pat themselves on the back.
Once a forecast has been made, data visualization techniques may be helpful for presentation to other decisionmakers.
Problems With Forecasting
Business forecasting is vital for businesses because it allows them to plan production, financing, and other strategies. However, there are three problems with relying on forecasts:
- The data is always going to be old. Historical data is all we have to go on, and there is no guarantee that the conditions in the past will continue in the future.
- It is impossible to factor in unique or unexpected events, or externalities. Assumptions are dangerous, such as the assumptions that banks were properly screening borrowers prior to the subprime meltdown. Black swan events have become more common as our reliance on forecasts has grown.
- Forecasts cannot integrate their own impact. By having forecasts, accurate or inaccurate, the actions of businesses are influenced by a factor that cannot be included as a variable. This is a conceptual knot. In a worst-case scenario, management becomes a slave to historical data and trends rather than worrying about what the business is doing now.
Forecasting can be dangerous. Forecasts become a focus for companies and governments mentally limiting their range of actions by presenting the short to long-term future as pre-determined. Moreover, forecasts can easily break down due to random elements that cannot be incorporated into a model, or they can be just plain wrong from the start.
The negatives aside, business forecasting is here to stay. Appropriately used, forecasting allows businesses to plan ahead for their needs, raising their chances of staying competitive in the markets. That's one function of business forecasting that all investors can appreciate.