361 research outputs found

    Can Observers Predict Trustworthiness?

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    We investigate whether experimental subjects can predict behavior in a prisoner's dilemma played on a TV show. Subjects report probabilistic beliefs that a player cooperates, before and after the players communicate. Subjects correctly predict that women and players who make a voluntary promise are more likely to cooperate. They are able to distinguish truth from lies when a player is asked about her intentions by the host. Subjects are to some extent able to predict behavior; their beliefs are 7~percentage points higher for cooperators than for defectors. We also study their Bayesian updating. Beliefs do not satisfy the martingale property and display mean reversion

    Examining the Impact of STR Weekly RevPAR Announcements on Lodging Stock Returns

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    This study investigated whether or not there were abnormal stock market returns on the announcement date of weekly RevPAR (revenue per available room) data by the lodging industry research firm STR. Using event study methodology, the study found that there were not statistically significant abnormal returns on the weekly RevPAR announcement date for the period from 2004 to 2009. The implications of this study are important to the hotel investment community including lodging stock owners and investors, stock analysts, investment bankers, and consultants as it indicates that there is not advance trading in lodging stocks based on the STR weekly RevPAR announcements

    Implied cost of capital investment strategies - evidence from international stock markets

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    Investors can generate excess returns by implementing trading strategies based on publicly available equity analyst forecasts. This paper captures the information provided by analysts by the implied cost of capital (ICC), the internal rate of return that equates a firm's share price to the present value of analysts' earnings forecasts. We find that U.S. stocks with a high ICC outperform low ICC stocks on average by 6.0% per year. This spread is significant when controlling the investment returns for their risk exposure as proxied by standard pricing models. Further analysis across the world's largest equity markets validates these results

    Identifying allosteric fluctuation transitions between different protein conformational states as applied to Cyclin Dependent Kinase 2

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    BACKGROUND: The mechanisms underlying protein function and associated conformational change are dominated by a series of local entropy fluctuations affecting the global structure yet are mediated by only a few key residues. Transitional Dynamic Analysis (TDA) is a new method to detect these changes in local protein flexibility between different conformations arising from, for example, ligand binding. Additionally, Positional Impact Vertex for Entropy Transfer (PIVET) uses TDA to identify important residue contact changes that have a large impact on global fluctuation. We demonstrate the utility of these methods for Cyclin-dependent kinase 2 (CDK2), a system with crystal structures of this protein in multiple functionally relevant conformations and experimental data revealing the importance of local fluctuation changes for protein function. RESULTS: TDA and PIVET successfully identified select residues that are responsible for conformation specific regional fluctuation in the activation cycle of Cyclin Dependent Kinase 2 (CDK2). The detected local changes in protein flexibility have been experimentally confirmed to be essential for the regulation and function of the kinase. The methodologies also highlighted possible errors in previous molecular dynamic simulations that need to be resolved in order to understand this key player in cell cycle regulation. Finally, the use of entropy compensation as a possible allosteric mechanism for protein function is reported for CDK2. CONCLUSION: The methodologies embodied in TDA and PIVET provide a quick approach to identify local fluctuation change important for protein function and residue contacts that contributes to these changes. Further, these approaches can be used to check for possible errors in protein dynamic simulations and have the potential to facilitate a better understanding of the contribution of entropy to protein allostery and function
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