This lesson explores the quantitative techniques used to enhance the accuracy and reliability of cash flow forecasts .

8.1 The Role of Statistical Methods
Data-driven forecasting methods offer a structured and objective approach to predicting future cash flows. By identifying patterns, relationships, and trends in historical data, statistical methods make forecasts repeatable, explainable, and scalable . Cash forecasting is the foundation for managing liquidity, and a key skill is to “calculate forecasted cash flows using statistical tools” .

8.2 Time Series Forecasting Methods

• Simple Moving Average (SMA): Bases a forecast on a rolling average of past values. It is easy to use but slower to respond to changes in direction .

• Exponential Smoothing: Gives more weight to recent data, making it more responsive. Simple exponential smoothing is suitable for stationary data (no long-term trend), while more advanced versions (Holt’s Linear Trend or Holt-Winters) are designed for data with a trend or seasonality .

8.3 Causal Forecasting Methods

• Regression Analysis: Quantifies how changes in independent drivers (e.g., sales, inventory levels) affect a dependent variable (cash flow). To avoid spurious correlations with non-stationary data, it is often necessary to transform the data first