We propose a novel approach to extract factors from large data sets that maximize covariation with the quantiles of a target distribution of interest. From the data underlying the Chicago Fed’s National Financial Conditions Index, we build targeted financial conditions indices for the quantiles of future US GDP growth. We show that our indices yield considerably better out-of-sample density forecasts than competing models, as well as insights on the importance of individual financial series for different quantiles. Notably, leverage indicators appear to co-move more with the median of the predictive distribution, while credit and risk indicators are more informative about downside risks.