Value Return Predictability Across Asset Classes and Commonalities in Risk Premia

This repository contains data and replication code for the paper "Value Return Predictability Across Asset Classes and Commonalities in Risk Premia" forthcoming in the "Review of Finance."

In a nutshell

We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. In all these asset classes, expected value returns vary by at least as much as their unconditional level. A single common component of the value spreads captures about two-thirds of value return predictability, and the remainder is asset-class-specific. We argue that common variation in value premia is consistent with rationally time-varying expected returns, because (i) common value is closely associated with standard proxies for risk premia, such as the dividend yield, intermediary leverage and illiquidity, and (ii) value premia are globally high in bad times.

Data

Datasets, in csv formats, for the asset classes we analyse are in the folder "Dataset_Value_Sorted_Hedge_Portfolios" for both types of weighting schemes. Dataset names follow the asset class naming convention in the paper.

Replication code

Table_02 is a jupyter notebook that replicates Table 2 in the published paper.

Cite as

@article{baba:boons:tamoni:2020,
  title={Value return predictability across asset classes and commonalities in risk premia},
  author={Baba-Yara, Fahiz and Boons, Martijn and Tamoni, Andrea},
  journal={Review of Finance, (forthcoming)},
  year={2020}
}