Sales Forecasting with Financial Indicators and Experts' Input

We investigate how uncertainty in retail sales can be explained by the return on a financial market index. This information can be employed in forecasting, hedging, and risk management. Our forecasting model expresses the total sales of a retailer as a function of sales forecasts generated by equity analysts, the term of the forecast, and the return on an aggregate financial market index over the term of the forecast. Using a panel of annual firm-level sales forecasts for 97 retailers over 10 years, each year containing multiple forecasts of varying terms, we show that a large and significant part of the sales forecast errors is explained by market returns. Surprisingly, this information is not accounted for in the analysts' forecasts. Therefore, we develop a method of augmenting sales forecasts with market returns thereby improving their accuracy. We conduct an extensive study of the model forecast updating performance and show that the accuracy improvement can exceed 15% in out-of-sample tests under various performance metrics, compared to both equity analysts and a standard time-series method. We also demonstrate the usefulness of the financial market data for operational hedging decisions