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We present a simple approach to forecasting conditional probabilitydistributions of asset returns. We work with a parsimonious specification ofordered binary choice regression that imposes a connection on signpredictability across different quantiles. The model forecasts the futureconditional probability distributions of returns quite precisely when using apast indicator and past volatility proxy as predictors. Direct benefits of themodel are revealed in an empirical application to the 29 most liquid U.S.stocks. The forecast probability distribution is translated to significanteconomic gains in a simple trading strategy. Our approach can also be useful inmany other applications where conditional distribution forecasts are desired.

Forecasting dynamic return distributions based on ordered binary choice