Earnings forecasts are usually published at the index level, but sector-level dispersion often tells a more useful story for asset allocation. 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.
Rather than treating each sector independently, 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 to track forecast accuracy over time, with visual diagnostics showing where the model over- or under-shoots by sector and by forecast horizon.