Long-horizon predictability and information decay in equity markets

Abstract

We use machine learning to predict stock returns at forward horizons from 1 month ahead to 120 months ahead. Stock return predictability declines with the forecast horizon; it follows an asymptotic exponential decay process consisting of a permanent component (c. 20 bp/month) and a transient component (c. 240 bp/month) which decays at around 6% per month. Persistent but declining predictability at increasing horizons can be explained by persistent but declining benchmark risk factor exposures at each horizon. Limits to arbitrage linked to risk (but not implementation costs) also explains declining profitability.