Luck versus skill over time: time-varying performance in the cross-section of mutual fund returns

ABSTRACT

Using returns histories spanning January 1984 to October 2014 of 5785 actively managed US closed-end equity mutual funds, we address the ‘thorny problems’ highlighted by Fama and French (The Journal of Finance, 2010, vol. 65, p. 1925) that arise due to their resampling procedure. This prevents them from capturing time variation in the parameters of equilibrium asset pricing models. These problems are addressed by combining innovative procedures which allow for testing of multiple break dates on fund-specific parameters along with cross-section bootstraps that remain valid in the presence of time-varying parameters. We find that substantial proportion – 8% – of the estimated versions of the asset pricing model have significant changes in their parameters. The effects of this time variation on the cross-section distribution of the risk-adjusted performance measure are significant and substantially increase centiles of the right tail of this distribution when compared to those produced without time-varying parameters. Our evidence regarding the lack of actively managed US equity mutual funds that generate excess returns is significantly weaker than those of Fama and French but our results do not overturn their pessimistic conclusion regarding the lack of skilled managers. We do find, unlike Fama and French, that managers generating negative returns are just unlucky but have no skill.     

A cost-effectiveness and budget impact analysis of apremilast in patients with psoriatic arthritis in Italy

Abstract

Aims: The aim of this study was to conduct a cost-effectiveness analysis, as well as a budget impact analysis, on the use of apremilast for the treatment of adult patients with psoriatic arthritis (PsA), within the Italian National Health Service (NHS).

Methods: A Markov state transition cohort model, which was adapted to the Italian context, was used to compare the costs of the currently available treatments and of the patients’ quality of life with two alternative treatment sequences, with or without apremilast as pre-biologic therapy. Moreover, a budget impact model was developed based on the population of patients treated for PsA in Italy, who can be eligible for treatment with apremilast. The eligible population was represented by adult patients with PsA who had an inadequate response to or were intolerant to previous disease-modifying antirheumatic drugs (DMARDs), for the approved indication, and for the treatment studied in the economic analytic model.

Results: This cost-effectiveness analysis estimated that the strategy of using apremilast before biologic therapy is cost-effective, with an incremental cost-effectiveness ratio of €32,263.00 per QALY gained which is slightly over the normal threshold found in other Italian economic studies, which usually considers a 40-year-period. Conversely, the budget impact analysis was conducted over 3?years, and it led to an estimated annual saving of €1.6 million, €4.6 million and €5.5 million in the first, second and third year of apremilast commercialization, respectively, for a total saving of €11.75 million in 3?years.

Limitations: Limitations of this analysis include the absence of head-to-head trials comparing therapies included in the economic model, the lack of comparative long-term data on treatment efficacy, and the assumption of complete independence between the considered response rates to therapy.

Conclusion: The use of apremilast as a first option before the use of biologic agents may represent a cost-effective treatment strategy for patients with PsA who fail to respond to, or are intolerant to, previous DMARD therapy. In addition, based on a budget impact perspective, the use of apremilast may lead to cost savings to the Italian healthcare system.     

Bayesian network models for incomplete and dynamic data

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper, we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.     

Integrated reporting and capital markets in an international setting: The role of financial analysts

This study investigates the interplay between integrated reporting (IR) and capital markets. In particular, building on voluntary disclosure and information processing theories, we hypothesize and empirically find that IR adoption improves analysts' ability to make accurate earnings forecasts. Whereas previous studies focus on the South African context, we rely on an international sample that also allows us to study the moderating effect of the corporate governance regime (shareholder or stakeholder oriented). The results suggest that IR improves analysts' ability to make accurate predictions to a larger extent in North America than in Europe, and we derive interesting insights on the much‐debated nature of IR. This study offers valuable insights to policy makers interested in improving disclosure practices in the financial market.     

Discounting earnings with stochastic discount rates

This paper presents new equity valuation formulae in closed form that extend the abnormal earnings growth (AEG) valuation of Ohlson [2005. “On Accounting-Based Valuation Formulae.” Review of Accounting Studies 10: 323–347] to the cases of time-varying or stochastic cost of capital as in Ang and Liu [2004. “How to Discount Cash Flows with Time-Varying Expected Returns.” Journal of Finance 59 (6): 2745–2783] or to cases of stochastic interest rates as in Ang and Liu [2001. “A General Affine Earnings Valuation Model.” Review of Accounting Studies 6: 397–425]. Interest rates are modelled by quadratic term structure models, which are not hindered by restrictions to factors correlation or by other shortcomings of affine term structure models in discounting long-term earnings. This is crucial since valuation can be very sensitive to the correlation between the factors driving earnings and interest rates. Positive correlation reduces price-earnings ratios according to US data. Valuation is also sensitive to the ‘volatility’ of abnormal earnings growth. The residual earnings risk-neutral valuation of Ang and Liu (2001) is adapted to quadratic term structure models.     

Corporate watchdogs

We investigate the role of financial analysts as corporate watchdogs. We show that firms that are subject to intense analyst monitoring are more likely to be investigated by the Securities and Exchange Commission or to be the subject of a securities class action. Using cross-sectional variations in managerial entrenchment, we find that this effect is not a reflection of the “dark side of analyst coverage,” analysts pushing executives to misbehave to exceed short-term expectations. Our findings are robust to different identification strategies addressing the endogeneity of analyst coverage decisions.     

Towards a classification of marine wildlife crime: Marketing strategies to curtail illegal fishing,malicious acts,and waterway pollution

Much effort has been dedicated to fighting wildlife crime with modest results. This paper focuses on marine wildlife crime, which suffers from a certain level of neglect due to terrestrial bias (a predominant focus on land animals). Despite legislative and enforcement efforts, there has been little integration of marketing techniques to curtail marine wildlife crime. For these reasons, we set out to study current issues surrounding marine wildlife crime by conducting interviews with three experts in areas of marine education, research, and legislation. The interviews provide an overview of the threats to marine wildlife, aid in the development of a classification of marine wildlife crime, and provide strategies as to what marketing actions may be useful to reduce it.     

Portfolio optimization under solvency II: a multi-objective approach incorporating market views and real-world constraints

Decisions in Economics and Finance - We propose a new approach to handle the problem of portfolio optimization for non-life insurance company incorporating the solvency capital requirement (SCR),...     

Innovation and Design in the Age of Artificial Intelligence

At the heart of any innovation process lies a fundamental practice: the way people create ideas and solve problems. This “decision making” side of innovation is what scholars and practitioners refer to as “design.” Decisions in innovation processes have so far been taken by humans. What happens when they can be substituted by machines? Artificial Intelligence (AI) brings data and algorithms to the core of the innovation processes. What are the implications of this diffusion of AI for our understanding of design and innovation? Is AI just another digital technology that, akin to many others, will not significantly question what we know about design? Or will it create transformations in design that current theoretical frameworks cannot capture? This paper proposes a framework for understanding the design and innovation in the age of AI. We discuss the implications for design and innovation theory. Specifically, we observe that, as creative problem-solving is significantly conducted by algorithms, human design increasingly becomes an activity of sensemaking, that is, understanding which problems should or could be addressed. This shift in focus calls for the new theories and brings design closer to leadership, which is, inherently, an activity of sensemaking. Our insights are derived from and illustrated with two cases at the frontier of AI—Netflix and Airbnb (complemented with analyses of Microsoft and Tesla)—which point to two directions for the evolution of design and innovation in firms. First, AI enables an organization to overcome many past limitations of human-intensive design processes, by improving the scalability of the process, broadening its scope across traditional boundaries, and enhancing its ability to learn and adapt on the fly. Second, and maybe more surprising, while removing these limitations, AI also appears to deeply enact several popular design principles. AI thus reinforces the principles of Design Thinking, namely: being people-centered, abductive, and iterative. In fact, AI enables the creation of solutions that are more highly user centered than human-based approaches (i.e., to an extreme level of granularity, designed for every single person); that are potentially more creative; and that are continuously updated through learning iterations across the entire product life cycle. In sum, while AI does not undermine the basic principles of design, it profoundly changes the practice of design. Problem-solving tasks, traditionally carried out by designers, are now automated into learning loops that operate without limitations of volume and speed. The algorithms embedded in these loops think in a radically different way than a designer who handles the complex problems holistically with a systemic perspective. Algorithms instead handle complexity through very simple tasks, which are iterated continuously. This paper discusses the implications of these insights for design and innovation management scholars and practitioners.