Robeco is an originally Dutch asset management firm, since 2013 part of Orix, founded in 1929 as the Rotterdamsch Beleggings Consortium (Rotterdam Investment Consortium). As of 2014, the company had €246 billion of assets under management. It was acquired in 2001 by the Rabobank Groep and sold in 2013 to ORIX Corporation.Robeco offers assets management services to both institutional and private investors. The funds for private investors are available through Robeco itself and other financial institutions.
This article explores how investors can allocate strategically across equity styles depending on their objective: absolute return or benchmark-relative performance. Defensive factors improve Sharpe ratios over full cycles but come with higher relative risk and weaker information ratios. By contrast, benchmark-relative strategies benefit most from return-oriented factors. Dynamic allocation rarely survives costs and requires unusually high skill. The most effective approach is integration: Combining multiple factors and short-term signals within one framework reduces timing risk, lowers turnover, and improves both Sharpe and information ratios. These findings demonstrate how factor combinations can be tailored to meet different investment objectives, whether absolute or relative.
Benchmark indexes define investment mandates and anchor performance evaluation in active equity management, yet actual portfolio exposures may deviate systematically from those definitions. Using 4,592 global equity funds across 10 benchmark categories from 2015 to 2024, the authors document pervasive co-movement in active returns: Up to 90% of funds exhibit positive beta relative to their peer group's average performance. Regression results show that funds exhibit economically significant off-benchmark exposures across size, style, and region dimensions. Large-cap funds load on small-cap factors; small-cap funds retain exposure to larger stocks; developed market funds hold emerging market exposure; and international mandates exhibit systematic growth tilts. Tracking error is strongly associated with these structural tilts: High-active-risk funds display the most pronounced benchmark drift, while low-active-risk funds remain closer to index definitions. These findings indicate that benchmark categories overstate the purity of delivered exposures and that peer clustering reduces the diversification benefits of combining multiple managers within the same mandate. Effective governance requires distinguishing structural beta positioning from true alpha generation.
We examine the evolution and implementation challenges of options-implied signals for stock selection. Although such strategies exhibit robust performance from 1996 to 2008, we find a marked decline in performance thereafter. We identify a measurement bias in the construction of these strategies, stemming from options prices being recorded up to 10 minutes after stock market close. Correcting for this bias by lagging the options data substantially reduces strategy performance before 2008. This performance deterioration persists across multiple specifications: value-weighted versus equal-weighted portfolios, different rebalancing frequencies, and various methods of aggregating implied volatilities. Our results demonstrate that historical options-based strategy performance can be largely driven by nonimplementable timing advantages if this timing mismatch is not accounted for, necessitating careful attention to price synchronization and signal construction in strategy implementations.
The correlation between stock and bond returns is a cornerstone of asset allocation decisions. History reveals abrupt regime shifts in correlation after long periods of relative stability. We investigate the drivers of the correlation between stocks and bonds and find that inflation, real rates, and government creditworthiness are important explanatory variables. We examine the implications of a shift in the stock-bond correlation and find that increases are associated with higher multi-asset portfolio risk and higher bond risk premia.
Traditional mean-variance portfolio optimization is based on the premise that investors only care about risk and return. However, some investors also have non-financial objectives such as sustainability goals. We show how the traditional approach can readily be extended to mean-variance-sustainability optimization and explain why this 3D investing approach is ex-ante Pareto-optimal. We illustrate its efficacy empirically in several studies, including carbon footprint and sustainable development goal objectives. Importantly, we highlight conditions under which a 3D optimization approach is superior to a na & iuml;ve 2D approach augmented with sustainability constraints.
