Glenn Ellison

Bio: Glenn Ellison is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Population & Mutual fund. The author has an hindex of 50, co-authored 112 publications receiving 23895 citations. Previous affiliations of Glenn Ellison include National Bureau of Economic Research & Microsoft.

Risk taking by mutual funds as a response to incentives

Abstract: This paper examines a potential agency conflict between mutual fund investors and mutual fund companies. Investors would like the fund company to use its judgment to maximize risk‐adjusted fund ret...

Risk Taking by Mutual Funds as a Response to Incentives

Abstract: This paper examines the agency conflict between mutual fund investors and mutual fund companies. Investors would like the fund company to use its judgment to maximize risk-adjusted fund returns. A fund company, however, in its desire to maximize its value as a concern has an incentive to take actions which increase the flow of investment.

Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach

Abstract: This paper discusses the prevalence of Silicon Valley-style localizations of individual manufacturing industries in the United States Several models in which firms choose locations by throwing darts at a map are used to test whether the degree of localization is greater than would be expected to arise randomly and to motivate a new index of geographic concentration The proposed index controls for differences in the size distribution of plants and for differences in the size of the geographic areas for which data is available As a consequence, comparisons of the degree of geographic concentration across industries can be made with more confidence We reaffirm previous observations in finding that almost all industries are localized, although the degree of localization appears to be slight in about half of the industries in our sample We explore the nature of agglomerative forces in describing patterns of concentration, the geographic scope of localization, and the extent to which agglomerations involve plants in similar as opposed to identical industries

Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach

Abstract: This paper discusses the prevalence of Silicon Valley‐style localizations of individual manufacturing industries in the United States. A model in which localized industry-specific spillovers, natural advantages, and pure random chance all contribute to geographic concentration is used to develop a test for whether observed levels of concentration are greater than would be expected to arise randomly and to motivate new indices of geographic concentration and of coagglomeration. The proposed indices control for differences in the size distribution of plants and for differences in the size of the geographic areas for which data are available. As a consequence, comparisons of the degree of geographic concentration across industries can be made with more confidence. Our empirical results provide a strong reaffirmation of the previous wisdom in that we find almost all industries to be somewhat localized. In many industries, however, the degree of localization is slight. We explore the nature of agglomerative forces in describing patterns of concentration, the geographic scope of localization, and the coagglomeration of related industries and of industries with strong upstream-downstream ties.

Learning, local interaction, and coordination

Abstract: This paper discusses the dynamic implications of learning in a large population coordination game, focusing on the structure of the matching process which describes how players meet. As in Kandori, Mailath, and Rob (1993) a combination of experimentation and myopia creates "evolutionary" forces which lead players to coordinate on the risk dominant equilibrium. To describe play with finite time horizons it is necessary to consider the rates at which the dynamic systems converge. In large populations with uniform matching, play is determined largely by historical factors. In contrast, when players interact with small sets of neighbors it is more reasonable to assume that evolutionary forces may determine the outcome.

Maximizing the spread of influence through a social network

Abstract: Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

The Effect of Word of Mouth on Sales: Online Book Reviews

Abstract: The authors examine the effect of consumer reviews on relative sales of books at Amazon.com and Barnesandnoble.com. The authors find that (1) reviews are overwhelmingly positive at both sites, but there are more reviews and longer reviews at Amazon.com; (2) an improvement in a book's reviews leads to an increase in relative sales at that site; (3) for most samples in the study, the impact of one-star reviews is greater than the impact of five-star reviews; and (4) evidence from review-length data suggests that customers read review text rather than relying only on summary statistics.

The Limits of Arbitrage

Abstract: In traditional models, arbitrage in a given security is performed by a large number of diversified investors taking small positions against its mispricing. In reality, however, arbitrage is conducted by a relatively small number of highly specialized investors who take large positions using other people's money. Such professional arbitrage has a number of interesting implications for security pricing, including the possibility that arbitrage becomes ineffective in extreme circumstances, when prices diverge far from fundamental values. The model also suggests where anomalies in financial markets are likely to appear, and why arbitrage fails to eliminate them.