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Institution

Texas A&M University

EducationCollege Station, Texas, United States
About: Texas A&M University is a education organization based out in College Station, Texas, United States. It is known for research contribution in the topics: Population & Finite element method. The organization has 72169 authors who have published 164372 publications receiving 5764236 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors survey 365 analysts and conduct 18 follow-up interviews covering a wide range of topics, including the inputs to analysts' earnings forecasts and stock recommendations, the value of their industry knowledge, the determinants of their compensation, the career benefits of Institutional Investor All-Star status, and the factors they consider indicative of high-quality earnings.
Abstract: Our objective is to penetrate the “black box” of sell-side financial analysts by providing new insights into the inputs analysts use and the incentives they face. We survey 365 analysts and conduct 18 follow-up interviews covering a wide range of topics, including the inputs to analysts’ earnings forecasts and stock recommendations, the value of their industry knowledge, the determinants of their compensation, the career benefits of Institutional Investor All-Star status, and the factors they consider indicative of high-quality earnings. One important finding is that private communication with management is a more useful input to analysts’ earnings forecasts and stock recommendations than their own primary research, recent earnings performance, and recent 10-K and 10-Q reports. Another notable finding is that issuing earnings forecasts and stock recommendations that are well below the consensus often leads to an increase in analysts’ credibility with their investing clients. We conduct cross-sectional analyses that highlight the impact of analyst and brokerage characteristics on analysts’ inputs and incentives. Our findings are relevant to investors, managers, analysts, and academic researchers.

559 citations

Journal ArticleDOI
TL;DR: Maximization theory as mentioned in this paper is an alternative to reinforcement theory as a description of steady-state behavior, and it provides new insight into these situations and, because it takes context into account, has greater predictive power than reinforcement theory.
Abstract: Maximization theory, which is borrowed from economics, provides techniques for predicing the behavior of animals - including humans. A theoretical behavioral space is constructed in which each point represents a given combination of various behavioral alternatives. With two alternatives - behavior A and behavior B - each point within the space represents a certain amount of time spent performing behavior A and a certain amount of time spent performing behavior B. A particular environmental situation can be described as a constraint on available points (a circumscribed area) within the space. Maximization theory assumes that animals always choose the available point with the highest numerical value. The task of maximization theory is to assign to points in the behavioral space values that remain constant across various environmental situations; as those situations change, the point actually chosen is always the one with the highest assigned value.Maximization theory is an alternative to reinforcement theory as a description of steady-state behavior. Situations to which reinforcement theory has been directly applied (such as reinforcement of rats pressing levers and pigeons pecking keys in Skinner boxes) and situations to which reinforcement theory has occasionally been extended (such as human economic behavior and human self-control) can be described by maximization theory. This approach views behavior as a quantitative outcome of the interaction of the putative instrumental response, the reinforcer, and the other activities available in the situation. It provides new insight into these situations and, because it takes context into account, has greater predictive power than reinforcement theory.

558 citations

Journal ArticleDOI
TL;DR: For instance, the authors show that transcription involves both interpretive decisions (What is transcribed?) and representational decisions (How is it transcribed?). These decisions ultimately respond to the contextual conditions of the transcription process itself, including the transcriber's own expectations and beliefs about the speakers and the interaction being transcribed; the intended audience of the transcript; and its purpose.

558 citations

Journal ArticleDOI
TL;DR: The present investigation revealed that a psychological intervention-self-affirmation-facilitates self-control when the resource has been depleted and holds promise as a mental strategy that reduces the likelihood of self- control failure.
Abstract: Research has established that acts of self-control deplete a resource required for subsequent self-control tasks. The present investigation revealed that a psychological intervention-self-affirmation-facilitates self-control when the resource has been depleted. Experiments 1 and 2 found beneficial effects of self-affirmation on self-control in a depleted state. Experiments 3 and 4 suggested that self-affirmation improves self-control by promoting higher levels (vs. lower levels) of mental construal. Self-affirmation therefore holds promise as a mental strategy that reduces the likelihood of self-control failure.

558 citations

Journal ArticleDOI
TL;DR: The general Kerr-de Sitter metric was given in this paper in arbitrary space-time dimension D≥4, with the maximal number (D−1)/2) of independent rotation parameters.

557 citations


Authors

Showing all 72708 results

NameH-indexPapersCitations
Yi Chen2174342293080
Scott M. Grundy187841231821
Evan E. Eichler170567150409
Yang Yang1642704144071
Martin Karplus163831138492
Robert Stone1601756167901
Philip Cohen154555110856
Claude Bouchard1531076115307
Jongmin Lee1502257134772
Zhenwei Yang150956109344
Vivek Sharma1503030136228
Frede Blaabjerg1472161112017
Steven L. Salzberg147407231756
Mikhail D. Lukin14660681034
John F. Hartwig14571466472
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023211
2022938
20218,664
20208,925
20198,426