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  • 1
    Online Resource
    Online Resource
    Informa UK Limited ; 2022
    In:  Journal of the American Statistical Association Vol. 117, No. 539 ( 2022-07-03), p. 1296-1310
    In: Journal of the American Statistical Association, Informa UK Limited, Vol. 117, No. 539 ( 2022-07-03), p. 1296-1310
    Type of Medium: Online Resource
    ISSN: 0162-1459 , 1537-274X
    RVK:
    RVK:
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2064981-2
    detail.hit.zdb_id: 207602-0
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  • 2
    Online Resource
    Online Resource
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-7-1), p. 1-12
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-7-1), p. 1-12
    Abstract: The development of new technologies drives the digital transformation of the credit reporting industry and also brings new opportunities for cross-border credit reporting cooperation. This study focuses on a cross-border credit information sharing platform based on blockchain 3.0 and its legal guarantee, with an aim to promote the integrated development of credit reporting across the Taiwan Straits. This study first points out the difficulties of cross-border credit information sharing; second, the technical characteristics of blockchain such as distributed storage, point-to-point transmission, security and credibility, open source, and programmability are naturally suitable for solving the information sharing problems in the credit reporting industry. So, this study proposes a framework and a specific design of a cross-strait credit information sharing platform based on blockchain 3.0 infrastructure; finally, in order to ensure the smooth operation of the platform, this study suggests signing a series of nonnormative “soft law” documents in the principles of equality, willingness, and consultation to provide legal guarantee for cross-border credit sharing.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
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  • 3
    Online Resource
    Online Resource
    Elsevier BV ; 2024
    In:  Research in International Business and Finance Vol. 70 ( 2024-06), p. 102399-
    In: Research in International Business and Finance, Elsevier BV, Vol. 70 ( 2024-06), p. 102399-
    Type of Medium: Online Resource
    ISSN: 0275-5319
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2165501-7
    detail.hit.zdb_id: 424514-3
    SSG: 3,2
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  • 4
    Online Resource
    Online Resource
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2020
    In:  Management Science Vol. 66, No. 11 ( 2020-11), p. 5448-5464
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 66, No. 11 ( 2020-11), p. 5448-5464
    Abstract: We investigate how board expertise affects chief executive officer (CEO) incentives and firm value. The CEO engages in a sequence of tasks: first acquiring information to evaluate a potential project, then reporting his or her assessment of the project to the board, and finally implementing the project if it is adopted. We demonstrate that the CEO receives higher compensation when the board agrees with the CEO on the assessment of the project. Board expertise leads to (weakly) better investment decisions and helps motivate the CEO's evaluation effort; however, it may induce underreporting and reduce the CEO's incentives to properly implement the project. Consequently, if motivating the CEO to evaluate projects is the major concern (e.g., innovative industries), board expertise exhibits an overall positive effect on firm value; however, if motivating the CEO to implement projects is the major concern (e.g., mature industries), board expertise can harm firm value. This paper was accepted by Shiva Rajgopal, accounting.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    RVK:
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2020
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 5
    Online Resource
    Online Resource
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  Management Science
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS)
    Abstract: The stock option-implied volatility skew reflects both the structural risk characteristics of the underlying company and the short-term information flow about the stock price movement. This paper builds a semistructural, cross-sectional option pricing model to separate the structural risk contributions from the information flow. The model identifies two structural risk sources that contribute to the cross-sectional variation of the skew: the company’s business cyclicality and its default risk. The model can explain as much as 44% of the cross-sectional variation in implied volatility skew and is particularly informative during and after recessions. The remaining skew variation reflects mainly short-term information flow and can be used to construct stock portfolios with much better investment performance and without hidden structural risk exposures. This paper was accepted by Agostino Capponi, finance. Funding: L. Wu gratefully acknowledges support by a grant from the City University of New York PSC-CUNY Research Award Program. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4872 .
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    RVK:
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2023
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 6
    Online Resource
    Online Resource
    Wiley ; 2021
    In:  Production and Operations Management Vol. 30, No. 11 ( 2021-11), p. 3967-3986
    In: Production and Operations Management, Wiley, Vol. 30, No. 11 ( 2021-11), p. 3967-3986
    Abstract: Experimental results support that overconfident newsvendors deviate from the optimal inventory levels to place orders closer to the mean of demand distributions, thereby lowering profits. To improve performance, we investigate whether nonstandard preferences based on prospect theory can manage overconfident newsvendors. We develop a framework where a manager determines a profit‐based target, which serves as a reference point in the newsvendor's utility function. We show that target‐setting can induce optimal orders for low‐margin products. We extend our model to a duopoly and show that the type of competition determines whether target‐setting improves performance beyond the monopolist setting. Decentralized competition, where competing newsvendors have different managers, reduces the effectiveness of target‐setting. However, centralized competition, where competing newsvendors share the same manager, increases the range of profit margins within which target‐setting can achieve the system‐optimal order quantities. We compare profit‐margin targets with gross‐profit targets throughout our analysis. Although the targets offer similar performance, we find that profit‐margin [gross‐profit] targets dominate for extremely [moderately] low‐margin products when the manager is uncertain about the newsvendor's overconfidence level. However, when managing a newsvendor with an uncertain degree of loss aversion, we find that profit‐margin targets offer greater benefits regardless of the product's profitability.
    Type of Medium: Online Resource
    ISSN: 1059-1478 , 1937-5956
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2151364-8
    detail.hit.zdb_id: 1108460-1
    SSG: 3,2
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  • 7
    In: Journal of Environmental Planning and Management, Informa UK Limited
    Type of Medium: Online Resource
    ISSN: 0964-0568 , 1360-0559
    RVK:
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2000921-5
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