Sector-level dispersion often tells a more useful story for asset allocation than index-level earnings forecasts. This case study covers how I built a sector-level S&P 500 earnings forecasting model in R, combining time series methods with Blue Chip Financial Forecasts survey data.
The model accounts for shared macro drivers across sectors while allowing sector-specific earnings sensitivities to diverge — useful for spotting which sectors are expected to outperform or lag the broader index.
Every forecast vintage is backtested against realized earnings, with visual diagnostics showing where the model over- or under-shoots by sector and forecast horizon.