Importance and Methods of Financial Forecasting
Importance/Advantages of an effective financial forecast:
• Demonstrates the financial viability of a new business venture. Allowing you to construct a model of how your business might perform financially if certain strategies, events and plans are carried out
• Allows you to measure the actual financial operation of the business against the forecast financial plan and make adjustments where necessary
• Allows you to guide your business in the right direction and take control of your cash flow
• Provides a benchmark against which to measure future performance
• Identifies potential risks and cash shortfalls to keep the business out of financial trouble
• Provides an estimate of future cash needs and whether additional private equity or borrowing is necessary
• Assists you to secure a bank loan or other funding, lenders and investors require financial forecasts to show your capacity to repay the loan
Financial forecasting methods
There are a number of methods that can be used to develop a financial forecast. These methods fall into two general categories, which are quantitative and qualitative. A quantitative approach relies upon quantifiable data, which can then be statistically manipulated.
A qualitative approach relies upon information that cannot actually be measured. Examples of quantitative methods are:
Causal methods. These methods assume that the item being forecasted has a cause-and-effect relationship with one or more other variables. For example, the existence of a movie theater can drive sales at a nearby restaurant, so the presence of a blockbuster movie can be expected to increase meal sales in the restaurant. The primary causal analysis method is regression analysis.
Time series methods. These methods derive forecasts based on historical patterns in the data that are observed over equally spaced time intervals. The assumption is that there is a recurring pattern in the data that will repeat in the future. Three examples of time series methods are:
Rule of thumb. This is based on a simplified analysis rule, such as copying forward the historical data without alteration. For example, sales for the current month are expected to be the same as the sales generated in the immediately preceding month.
Smoothing. This approach uses averages of past results, possibly including weightings for more recent data, thereby smoothing out irregularities in the historical data.
Decomposition. This analysis breaks down the historical data into its trend, seasonal, and cyclical components, and forecasts each one.
Examples of qualitative methods are:
Market research. This is based on discussions with current and potential customers regarding their need for goods and services. Information must be gathered and analyzed in a systematic manner in order to minimize biases caused by small data sets, inconsistent customer questioning, excessive summarization of data, and so forth. This is an expensive and time-consuming research method. It can be useful for detecting changes in consumer sentiment, which will later be reflected in their buying habits.
Opinions of knowledgeable personnel. This is based on the opinions of those having the greatest and most in-depth knowledge of the information being forecasted. For example, the senior management team may derive forecasts based on their knowledge of the industry. Or, the sales staff may prepare sales forecasts that are based on their knowledge of specific customers. An advantage of using the sales staff for forecasting is that they can provide detailed forecasts, possibly at the level of the individual customer. There is a tendency for the sales staff to create overly optimistic forecasts.
Delphi method. This is a structured methodology for deriving a forecast from a group of experts, using a facilitator and multiple iterations of analysis to arrive at a consensus opinion. The results from each successive questionnaire are used as the basis for the next questionnaire in each iteration; doing so spreads information among the group if certain information was initially not available to everyone. Given the significant time and effort required, this method is best used for the derivation of longer-term forecasts.
Qualitative methods are especially necessary during the early stages of a company or product, where there is little historical information that can be used as the basis for a quantitative analysis.