Authors: Geoff Duncombe, Mike Nigro, Bradley Kay
Abstract: The authors propose a methodology using historical data to quantify the return premia for major asset-class based factors.
The paper introduces a handful of innovations intended to improve the accuracy of our long-term return forecasts. Specifically, we:
- Use new asset class return proxies to extend our analysis much further back than the daily return histories of most modern indices.
- Separate the most heterogeneous of the prior paper’s factors, Commodities, into six sector-based factors for which the long-term premia are individually estimated.
- Apply (what we believe to be) common sense adjustments to long-term histories — slightly overweighting recent returns and applying empirically-based shrinkage across the observed historical Sharpe ratios to generate our forward-looking estimates of each factor’s premium.