Almost Exact Risk Budgeting with Return Forecasts for Portfolio Allocation

TL;DR

This paper introduces a convex optimization approach for risk-budgeted portfolio allocation that incorporates return forecasts and transaction costs, demonstrating practical benefits in equity, bond, and index constituent selection.

Contribution

It generalizes risk budgeting to include return forecasts and costs within a convex framework, scalable to large asset sets, and applies it to real-world financial problems.

Findings

Effective in equity and bond allocation scenarios

Improves index constituent selection for NASDAQ100

Scalable convex formulation for large asset universes

Abstract

In this paper, we revisit the portfolio allocation problem with designated risk-budget [Qian, 2005]. We generalize the problem of arbitrary risk budgets with unequal correlations to one that includes return forecasts and transaction costs while keeping the no-shorting (long-only positions) constraint. We offer a convex second order cone formulation that scales well with the number of assets and explore solutions to the problem in different settings. In particular, the problem is solved on a few practical cases - on equity and bond asset allocation problems as well as formulating index constituents for the NASDAQ100 index, illustrating the benefits of this approach.