FP&A Week: Breaking the Paradox
October 10-13, 2023
By Robert J. Zwerling and Jesper H. Sorensen, founders of the Finance Analytics Institute
Robert J Zwerling and Jesper H Sorensen are the founders of the Finance Analytics Institute.
Mr. Zwerling is also Managing Director of Aurora Predictions, providing intuitive analytics with AI software for Finance and Operations.
Mr. Sorensen is also Senior Finance Director in a Fortune 100 technology company with a proven track record of advancing the analytics agenda.
To learn more about the Finance Analytics Institute please visit at www.fainstitute.com.
Humans have personal and political pressures that pull at them and, therefore, they are biased towards something.
As long as there are humans involved in making a forecast, the forecast will be biased.
The key to making a forecast unbiased is to find a method where humans have minimal influence on the outcome.
The first rule of forecasting is to have a dialogue between finance and business to lock in one single forecast that is owned by both business and finance. Having multiple forecasts is not an option.
Although finance and business do not always agree on numbers, such an alignment can be supported through the use of unbiased forecasting when finance lets the “data talk” and leaves bias out.
Unbiased forecasting is a framework where finance uses multiple methods to forecast, which cannot be manipulated and, as such, are independent of personal opinions. These are the methods where historical data, market data, statistics or an industry index are examined, and forecast algorithms are applied to predict future outcomes.
In this article, we will investigate five of those methods. Each of them has its own pros and cons.
Crowd forecasting does not rely on a single individual but a whole group of people providing their own individual views of the future, thus, reducing the bias component.
The variability of individual performance makes it hard to know which individual to trust. If you aggregate the forecasts of a crowd of people, you are much more likely to come up with a more accurate forecast on average.
With crowd forecasting, there are several pros and cons to consider:
Understanding the competitive landscape can be used as an unbiased gauge to assess a forecast. Using the quarterly announcement from public companies is a guide to forecasts for the following quarters. If finance has noticed a pattern between its own performance and its competitor’s performance, those insights can be used to provide an unbiased forecast.
There are also some pros and cons with this method:
A regression uses the historical relationship between an independent (often time) and a dependent variable, such as sales, revenue, etc., to predict the future values of the dependent variable.
Smoothing and Moving average covers a number of different methods, including ARIMA, Holt-Winter, etc. These models are statistical techniques using historical time-series data and applying algorithms to predict future outcomes.
Leading indicators are industrial and economic metrics from which an indication of the value or direction of another variable (for example, a sales forecast) can be obtained.
They are called "leading" because their direction or magnitude historically "leads" the focal variable. For example, we may find that the unemployment rate indicates (leads) the future of a company’s revenue.
If finance would like to achieve a higher forecast accuracy, it needs to:
The ability of finance to provide unbiased recommendations both improves forecast accuracy and helps finance become a strategic partner to the business.
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