Forecasting dynamic return distributions based on ordered binary choice

TL;DR

This paper introduces a straightforward ordered binary choice regression model for accurately forecasting the entire conditional distribution of asset returns, leveraging past indicators and volatility, with demonstrated economic benefits in stock trading.

Contribution

It proposes a novel, parsimonious ordered binary choice approach that captures sign predictability across quantiles for distribution forecasting.

Findings

Accurately forecasts return distributions using simple predictors.

Achieves significant economic gains in stock trading strategies.

Effective across multiple liquid U.S. stocks.

Abstract

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