This study analyses the performance and market timing of US socially responsible (SR) mutual funds in relation to business cycle regime shifts and different grouping criteria: Ethical strategy focus, SR attributes scores and Morningstar category. Different methodologies are applied and results highlight the importance of considering specific benchmarks related to the investment style in evaluating the SR fund performance. Our results show that, in aggregate, the abnormal performance of SR funds is negative and significant in expansion periods, but no significant differences are found in recession periods. When specific benchmarks are considered, performance improves in recession periods, particularly for environmental funds, those with high SR attributes scores, and funds from the nine Morningstar style box categories. Market timing of SR funds takes positive values and is partially significant. Previous evidence of negative timing after a recent financial crisis vanishes when specific benchmarks are considered. For comparative purposes the performance of conventional US mutual funds is also analysed. There are no significant differences between the performance of SR and conventional mutual funds when a fair comparison is made within the same style categories. When all the SR funds are considered, they underperform conventional funds in expansion sub‐periods, but in recession sub‐periods they perform better, although the differences observed are not significant.

Sustainable investment responds to demands for carbon and climate-neutral societies. To address the urgency around climate change and provide investors with more qualified information, Morningstar has developed the Low Carbon Designation (LCD) to indicate that the companies held in a portfolio are in general alignment with the transition to a low-carbon economy. The designation is given to portfolios that have low carbon risk and fossil fuel exposure scores. The present study builds on the LCD by examining the relationship between these scores and financial performance. With this aim, we analyze 3920 socially responsible mutual funds from across the world.Results show differences in financial performance according to scores and investment areas. We find evidence that funds considered to have higher levels of sustainability achieved better performance than funds with higher exposure to companies involved in carbon and fossil fuel industries. We provide insights on the informativeness of these new scores with a focus on climate change and their relevance in helping investors to identify climate-aware funds. This study highlights the importance of introducing strategies to develop green finance; the analysis confirms that sustainability improves performance. Finally, the LCD indicator is shown to be relevant for making fairer comparisons among socially responsible funds and, ultimately, for developing low-carbon economies.

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

The last few years have witnessed a rapid evolution in the literature evaluating mutual fund performance using frontier techniques. The instruments applied, mostly DEA (Data Envelopment Analysis) and, to a lesser extent, FDH (Free Disposal Hull), are able to encompass several dimensions of performance, but they also have some disadvantages that might be preventing a wider acceptance. The recently developed order-m and order-α partial frontiers overcome some of the disadvantages (they are robust with respect to extreme values and noise, and do not suffer from the well-known curse of dimensionality) while keeping the main virtues of DEA and FDH (they are fully-nonparametric). In this article we apply not only the non-convex counterpart of DEA (FDH) but also order-m and order-α partial frontiers to a sample of US mutual funds. The results obtained for both order-m and order-α are useful, since a full ranking of the mutual funds' performance can be obtained. We merge these methods with the literature on mutual fund performance persistence. By combining the two literatures we derive an algorithm which establishes how the choice of m and α parameters intrinsic to order-m and order-α (respectively) relate to the existence of performance persistence and the contrarian effect.

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