273: M6 - Forecasting Flashcards
What is the primary focus of forecasting company revenues?
Primary focus is to forecast in company revenues
Two categories of forecasting approaches
1) Quantitative
2) Qualitative
Quantitative Forecasts
Quantitative forecasts are based on statistical models using historical data to generate numeric predictions. The obvious benefit to this approach is it is tangible and easy to visualize. The downside is that the past is rarely an accurate predictor of the future. If it were, the process wouldn’t be called forecasting, it would be called extrapolation.
Quantitative models are useful when there is a long timeseries of data available and the company being valued and its industry are reasonably stable.
Qualitative Forecasts
Qualitative forecasts are predictions are based on the opinions and views of experts or a panel of experts. Essentially these predictions are a judgement call by an informed person or persons. These types of forecasts are particularly useful with start-ups or new product segments of an existing company where little representative, historical data is available.
Top-Down Forecasting
In top-down forecasting, we start at a very high, macroeconomic level and work down to a given company’s forecast of revenue. The advantage of this approach is the easy access to macroeconomic data which are the inputs into the forecast. The only company-level data considered is typically market share. The downside is the approach doesn’t consider the process by which market share is captured.
Top-down forecasting is useful for large established firms in reasonably mature industries
What is TAM
TAM is referred to as the “Total Addressable Market) (TAM) for a given product segment. The TAM is impacted global macroeconomic GDP forecasts or other macro factors. Once a forecast for a product’s TAM has been derived:
Total Addressable Market (TAM) -> Market Share -> Revenue
Bottom-up Forecasting
Product/Service -> Sale Volume and Prices -> Revenue
Hybrid Forecasting
If one were to survey equity analysts in an attempt to determine which form of forecasting is more popular they would invariably find that many analysts would describe their forecasting process as top down/bottom up. Essentially, the analyst is combining both methodologies to derive a forecast. It is a rare product that is not impacted by the underlying economy in which the product is being marketed and sold.
Statistical methods which we can use to forecast revenue given historical data:
1) Naive Approach
2) Moving Average Approach
3 Exponential Smoothing Approach
4) Trend Analysis Approach
5) Regression Analysis Approach
Naive Approach
In the naïve approach, we assume that revenue growth in the future will be the same as the most recent period. The advantage here is the simplicity of the approach, however, unless the most recent past is most representative of the future, this approach is not accurate.
Moving Average Approach
In the moving average approach, we assume that revenue growth will be the average over a number of recent years, typically 3-5 years. This approach is slightly more complicated and has the advantage of smoothing growth over a number of years.
Exponential Smoothing Approach
The exponential smoothing approach is very similar to the moving average approach, however, when the average is calculated, the different years in the average are weighted differently
Trend Analysis Approach
trend analysis relies on the assumption that the data will follow a predictable trend in the data
Spurious Relationships
A potential concern that we must consider with using the regression approach is the potential for spurious relationships or correlations between variables. When we run a regression, we are hypothesizing that the independent variables influence the dependent variable
Qualitative Analysis
Qualitative factors have the advantage of being entirely forward looking and can be adjusted specifically to the context of the firm and the industry. The downside is that qualitative factors are driven by the opinion of experts with incomplete information and inherent biases that are not observable.