Research Review | 16 May 2025 | Asset Allocation

Rethinking the Stock-Bond Correlation
Thierry Roncalli (Amundi Asset Management & University of Evry)
February 2025
The stock-bond correlation is a basics of finance and is related to some of the fundamentals of asset management. However, understanding the stock-bond correlation is not easy. In this presentation, we answer the following questions What is the natural sign of the stock-bond correlation? Why do some investors prefer a positive stock-bond correlation while others prefer a negative stock-bond correlation? How does the stock-bond correlation relate to the theory of risk premia? What is the leverage effect of correlation? What are the conditions for negative stock-bond correlation? What are the implications for strategic asset allocation (SAA) and tactical asset allocation (TAA)?

The Risk and Reward of Investing
Ronald Q. Doeswijk (independent) & Laurens Swinkels (Erasmus U. Rotterdam)
February 2025
We examine the risks and rewards of investing by constructing a comprehensive market portfolio valued at $150 trillion in global assets and spanning 1970–2022 at a monthly frequency. The monthly frequency allows for a more accurate estimation of investment risks compared with previous studies. Even though the Sharpe ratio of the global market portfolio is not much higher than that of equities, it is much more stable over time. Moreover, drawdowns of the global market portfolio are less deep and shorter. When the market portfolio is expressed in currencies other than the U.S. dollar, risks of investing appear larger.

Leveraging the Low-Volatility Effect
Lodewijk van der Linden (Robeco Quantitative Investments), et al.
October 2024
Low-volatility has become a mainstream investment style over the past two decades, recognized for delivering high risk-adjusted returns. However, many investors fail to fully capitalize on this strategy due to benchmark constraints. Low-volatility stocks tend to lag during prolonged bull market, a challenge that can be addressed using leverage. This paper outlines five use cases to leverage upon the low-volatility effect, including an enhanced strategy, an alternative to the 60/40 asset allocation, and the use of long and short-extension with stocks and market futures. These approaches help investors aiming to meet objectives ranging from stable performance, consistent outperformance, market-neutral returns, or as an alternative for put options, unlocking the full potential of this underutilized factor.

A Century of Macro Factor Investing – Diversified Multi-Asset Multi-Factor Strategies through the Cycles
Alexander Swade (State Street Global Markets & Lancaster University), et al.
April 2025
We diversify an investment portfolio across macroeconomic factors that are mimicked by investable asset classes and style factors. Using a century of global data we analyze the resulting macro factor portfolio’s sensitivities to different macroeconomic scenarios and highlight the relevance of navigating time variation in macroeconomic risk premia. Specifically, we adapt the portfolio allocation to align with the identified macro environment as predicted by a forward-looking business cycle model. A Black-Litterman framework is used to thus improve upon a diversified macro factor allocation and to further tap into predictive asset class and style factor signals.

Global Tactical Asset Allocation: Updated Results and Real-Market Implementation Using Python and IBKR
Carlo Zarattini (Concretum Group)
April 2025
This study revisits and expands on one of the most influential investment research papers of the past two decades: A Quantitative Approach to Tactical Asset Allocation, authored by Meb Faber in 2006 and published in The Journal of Wealth Management in 2007. We begin with a concise overview of the original strategy, then present updated historical performance results using data through March 2025. Next, we explore how different rebalancing frequencies can affect outcomes, and propose a tranche-based approach to help practitioners reduce the impact of rebalance timing luck. Finally, we show how investors can put this strategy into practice with a simple Python script that automates portfolio rebalancing through Interactive Brokers.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno