Common methods of financial forecasting
When it comes to forecasting, several methods are available, each with merits depending on the purpose of the forecast and the business model in question. It’s crucial to understand that budgeting and record-keeping are essential parts of forecasting. The more accurate and detailed a forecast is, the more reliable it will be.
The four basic forecasting methods are straight line, moving average, simple linear regression and multiple linear regression. Each approach has pros and cons.
Straight line
Straight-line forecasting is a simple way to predict growth. It uses historical financial data and basic math to show potential future outcomes based on current growth rates.
An advantage of this strategy is its simplicity. A drawback is that it doesn’t consider changing market conditions, making long-term predictions risky.
The straight-line approach can provide valuable insight for short-term budgeting and planning, but a company should use more complex methods to make long-term predictions.
Moving average
The moving average forecasting method evaluates standard financial metrics such as revenue, profit, sales growth and stock prices. It uses short-term calculations to create an ever-evolving average value that helps businesses identify underlying patterns.
An advantage is that this method allows for faster trend identification. A disadvantage is that it can lag if used over long periods. As such, it best serves as a tool to detect changes in the short run.
Simple linear regression
The simple linear regression method forecasts future values of dependent variables based on previous values. It uses a linear relationship between independent and dependent variables to create a trend line.
Pros of this method include ease of implementation, low cost and ability to identify trends. A con is it’s limited in handling complex relationships between variables and can be influenced by outliers.
Multiple linear regression
The multiple linear regression model is the most advanced of forecasting methods. It can account for complex relationships between dependent and independent variables, providing more accurate results than simple linear regression.
Although multiple linear regression is the most accurate forecasting method, it also requires more data and resources than other methods. Multiple linear regression models should be used only when you have sufficient data to predict performance accurately.
