Publications
Publications
We examine how active risk- and holdings-based diversification of equity portfolios affect performance and vulnerability to large losses. Conducting a comprehensive empirical study of US-based funds, we find that risk-based and sector-based diversification significantly reduce active tail risk and the likelihood of extreme losses, without substantially diminishing portfolio performance. These effects are nonlinear and decreasing, suggesting that investors need not minimizing the concentration of their portfolios. We also examine these relationships on an unprecedented large sample of portfolios using a novel methodology that allows the production of portfolios with similar levels of risk, and find that they are robust to several definitions of extreme risk. Our results highlight the practical value of diversification in managing portfolio risk while maintaining competitive performance.
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A precise understanding of the risks driving portfolio performance is a key component of asset management. We show that classical methods used to estimate risk factor contributions can lead to important “misattributions” of performance. These misattributions are not well documented, in spite of their potentially significant impact on the understanding of investment strategies. We investigate the drivers of misattribution episodes, including model uncertainty, time-varying factor parameters and extreme data points, and propose analytical approaches to mitigate their effects. We also show that introducing more model complexity does not necessarily lead to better estimates. By addressing misattribution drivers, our research contributes to better-informed decisions and more effective risk management in dynamic market environments.
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The latest development in empirical Asset Pricing is the use of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. We review and critically assess the most recent and relevant contributions in the literature grouping them into five categories defined by the Machine Learning (ML) approach employed: regularization, dimension reduction, regression trees/random forest (RF), neural networks (NNs), and comparative analysis. We summarize the empirical findings with special attention to their economic interpretation providing hints for future developments.
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Working Papers
We introduce a new methodology that generates random equity portfolios with a fixed level of risk and weight concentration. It uses elements of group theory to define a rotation with random angles which, when applied on a portfolio, transforms its weights but conserves its risk and concentration. The implementation of this approach is straightforward to implement and only requires commonly available price data. The ability to generate random portfolios with fixed levels of risk is very desirable in many areas of research. We illustrate its potential by applying it for the study of active risk diversification and the creation of new benchmarking paradigms.
It is well established that investors price market liquidity risk. Yet, there exists no financial claim contingent on liquidity. We propose a contract to hedge uncertainty over future transaction costs, detailing potential buyers and sellers. Introducing liquidity derivatives in Brunnermeier and Pedersen (2009) improves financial stability by mitigating liquidity spirals. We simulate liquidity option prices for a panel of NYSE stocks spanning 2000 to 2020 by fitting a stochastic process to their bid-ask spreads. These contracts reduce the exposure to liquidity factors. Their prices provide a novel illiquidity measure reflecting cross-sectional commonalities. Finally, stock returns signicantly spread along simulated prices.
Presented at the 2023 Global Finance Conferece, the 2023 Workshop Banks and Financial Markets, the 2022 Credit Conference, the 2022 DGF Conference, 2022 Derivative Market Conference, the 2022 Financial Markets and Corporate Governance Conference, the 2022 GSB Research Symposium, the 4th International Conference on European Studies, the ISB Summer Research Conference 2022, the 15th edition of the IRMC, the Leibniz Institute for Financial Research SAFE, the Nova School of Business and Economics, the 2022 Southern Finance Association Meeting, and the 11th Annual Stern/Salomon Microstructure Meeting
Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors.
Presented at the 12th International Research Meeting in Business and Management, the 2023 GSB Research Symposium, the 2023 Global Finance Conference, the 6th Dauphine Finance PhD Workshop, the 2023 Financial Markets and Corporate Governance Conference, and at the Leibniz Institute for Research SAFE
Cross-predictability denotes the fact that some assets can predict other assets' returns. I propose a novel performance-based measure that disentangles the economic value of cross-predictability into two components: the predictive power of one asset's signal for other assets' returns (cross-predictive signals) and the amount of an asset's return explained by other assets' signals (cross-predicted returns). Empirically, the latter component dominates the former in the overall cross-prediction effects. In the crosssection, cross-predictability gravitates towards small firms that are strongly mispriced and difficult to arbitrage, while it becomes more difficult to cross-predict returns when market capitalization and book-to-market ratio rise.