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Real and Private-Value Assets
No full textReal and private-value assets—defined here as the sum of real estate, infrastructure, collectibles, and noncorporate business equity—compose an investment class worth an estimated $84 trillion in the U.S. alone. Furthermore, private values can affect pricing in many other financial markets, such as that for sustainable investments. This paper introduces the research on real assets and private values that can be found in this special issue. It also reviews recent advances and highlights new research directions on a number of topics in the real assets space that we believe to be particularly important and exciting
Pessimistic Target Prices by Short Sellers
No full textThe proportion of short-selling attacks including target prices has more than doubled from 2010 to 2018. In 637 attacks with target prices, short sellers claim that the attacked firms’ stock prices should drop by 65% on average, but the mean (median) decline is only 7% (16%) one year after the attacks, implying severe pessimistic bias. Nevertheless, we show that these target prices are informative about the severity of overvaluation. They are associated with ex ante overvaluation characteristics, the severity of allegations, non-price reactions by the attacked firms and shareholders, and most importantly, the future returns in various windows, even after controlling for various factors including key qualitative characteristics of the attacks. Further, the disclosure of target prices accelerates price discovery after attacks. Finally, we present evidence that some investors, and in particular retail investors, are worse off because they seem to overlook the informativeness of short sellers’ target prices
The Impact of Privacy Laws on Online User Behavior
No full textPolicymakers worldwide draft privacy laws that require trading-off between safeguarding consumer privacy and preventing economic loss to companies that use consumer data. However, little empirical knowledge exists as to how privacy laws affect companies’ performance. Accordingly, this paper empirically quantifies the effects of the enforcement of the EU’s General Data Protection Regulation (GDPR) on online user behavior over time, analyzing data from 6,286 websites spanning 24 industries during the 10 months before and 18 months after the GDPR’s enforcement in 2018. A panel differences estimator, with a synthetic control group approach, isolates the short- and long-term effects of the GDPR on user behavior. The results show that, on average, the GDPR’s effects on user quantity and usage intensity are negative; e.g., the numbers of total visits to a website decrease by 4.9% and 10% due to GDPR in respectively the short- and long-term. These effects could translate into average revenue losses of 2.5 million for ad-based websites 18 months after GDPR. The GDPR’s effects vary across websites, with some industries even benefiting from it; moreover, more-popular websites suffer less, suggesting that the GDPR increased market concentration
Quality and Product Differentiation: Theory and Evidence from the Mutual Fund Industry
No full textWe study product differentiation in the mutual fund industry. We design a model in which funds with heterogeneous perceived quality can choose their level of product differentiation. In equilibrium, high quality funds choose broad market designs (i.e., low differentiation) appealing to many investors, while low quality funds adopt niche designs (i.e., high differentiation) that investors either love or loath. Using as a measure of fund differentiation the degree of textual uniqueness of investment strategy description in fund prospectuses, we confirm empirically that funds with lower expected performance tend to differentiate more. We use the issuance of Morningstar rating to previously unrated funds as an exogenous shock to perceived quality to identify the economic mechanism. We find that funds receiving a low rating increase their product differentiation. The effect is mainly concentrated on funds run by small management companies, a feature associated with lower performance. This increase in product differentiation makes funds more likely to survive. It also has a market-level impact on the menu of funds available to investors
Design as a Dynamic Capability: A Building Capability Framework
No full textDesign is increasingly adopted by organization as an innovation capability to renew and transform the firm and therefore acting as dynamic capability. However, few article address how firms build a new dynamic capability and especially in the case of the design capability. Based on a longitudinal case study of the building of design as an innovation capability within an insurance company, we suggest a capability-building framework: a capability is built by acquiring resources, deploying them in activities such as projects, capitalizing on the learning from one project to another, and thus building progressively knowledge that is then shared and diffused among the resources resulting in the renewal of their competences. This framework highlights a reinforcement dimension and distinguishes operations (Designing, spreading Design and managing Design) from building on these operations (building the Design expertise and transforming the organization through Design) in order to contribute to the firm’s resources renewal
The Value of Specialization in Private Equity: Evidence from the Hotel Industry
No full textWe show that PE sector specialists outperform generalists at every stage of the investment life cycle. Using granular data for thousands of U.S. hotels over the last two decades, we document that specialists exert a greater positive influence on more margins of hotel operations, earn higher net cash flows over the holding period, and achieve larger capital gains upon exit than do their generalist peers and other, non-PE investors backing ex ante equivalent assets. By contrast, PE generalists’ strongest comparative advantage may be better access to attractively priced acquisition financing. Our results provide novel evidence on the heterogeneity of PE investment strategies and associated performance outcomes
Déchets radioactifs : retour sur l’évaluation socio-économique du projet Cigéo
No full textInternational audienceCigéo aims to store the most hazardous radioactive waste in a deep geological repository. The Socio-Economic Assessment (SEA) of the project is intended to analyse the gains and costs for the community, particularly in comparison with long-term storage, which will be the responsibility of future generations. This SEA has raised various methodological questions due to the duration of the project, the difficulties in monetising certain costs and benefits, the uncertainties about future societies and the choice of the discount rate. One conclusion is that Cigéo provides, through the safe burial of the waste, an ‘‘insurance benefit’’ in the face of a risk of degradation of future societies.Cigéo vise à stocker en couche géologique profonde les déchets radioactifs les plus dangereux. L’évaluation socio-économique (ESE) du projet est destinée à analyser les gains et les coûts pour la collectivité, notamment par comparaison à un entreposage de longue durée restant à la charge des générations futures. Cette ESE a soulevé diverses questions méthodologiques en raison de la durée du projet, des difficultés à monétiser certains coûts et avantages, des incertitudes sur les sociétés du futur et du choix du taux d’actualisation. Une conclusion est que Cigéo procure, grâce à l’enfouissement sécurisé des déchets, un « bénéfice assurantiel » face à un risque de dégradation des sociétés futures
Computational Indicators in the Legal Profession: Can Artificial Intelligence Measure Lawyers' Performance?
No full textThe assessment of the legal professionals’ performance is increasingly important in the market of legal services to provide relevant information both to consumers and to law firms regarding the quality of legal services. In this article, we explore how computational indicators are produced to assess lawyers’ performance in courtroom litigation, analyzing the specific types of information they can generate. We capitalize on artificial intelligence (AI) methods to analyze a sample of 8,045 cases from the French Courts of Appeal, explore different associations involving lawyers, courts, and cases, and assess the strengths and flaws of the resulting metrics to evaluate the performance of legal professionals. The methods we use include natural language processing, machine learning, graph mining and advanced visualization. Based on the examination of the resulting analytics, we uncover both the advantages and challenges of assessing performance in the legal profession through AI methods. We argue that computational indicators need to address deficiencies regarding their methodology and diffusion to users to become effective means of information in the market of legal services. We conclude proposing adjustments to computational indicators and existing regulatory tools to achieve this purpose, seeking to pave the way for further research on this topic
