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  • 1
    Online Resource
    Online Resource
    Crypto Wash Trading
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  Management Science
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS)
    Abstract: We present the first systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first significant digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by a specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged more than 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements and highlights the importance of fintech regulation. This paper was accepted by David Simchi-Levi, special issue of Management Science: Blockchains and crypto economics. Funding: This research was partly funded by the Ewing Marion Kauffman Foundation [Grant G-201907-6995], the National Natural Science Foundation of China [Grants 72192802, 72192800, and 72192801] , Ripple’s University Blockchain Research Initiative (UBRI), and the FinTech at Cornell Initiative. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.02709 .
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2021.02709
    RVK:
    QP 300
    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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  • 2
    Online Resource
    Online Resource
    Scaling Smart Contracts via Layer-2 Technologies: Some Empirical Evidence
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  Management Science
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS)
    Abstract: Blockchain-based smart contracts can potentially replace certain traditional contracts through decentralized enforcement and reduced transaction costs. However, scalability is a key bottleneck hindering their broader application and adoption, often leading to concentrated or exclusive networks. To avoid falling short of the original promise of the technology, firms actively explore “layer-2” methods for scaling. We provide some initial evidence on the economic implications of a layer-2 scaling solution, which moves information aggregation from on-chain to off-chain peer-to-peer networks. A parallel-system experiment allows clean identification because we observe the same unit in the treatment and control systems at the same time. We find that this scaling solution reduces operating costs by 76%, and importantly, leads to decentralization with lower market concentration and more participation, which in turn improves data accuracy. The findings provide insights on how blockchain and smart contracting technologies evolve toward achieving decentralized and scalable trust. This paper was accepted by David Simchi-Levi, information systems. Funding: W. Cong received funding from Ripple’s university blockchain research initiative (UBRI). Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.00281 .
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2023.00281
    RVK:
    QP 300
    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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  • 3
    Online Resource
    Online Resource
    A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection
    Hindawi Limited ; 2021
    In:  Mobile Information Systems Vol. 2021 ( 2021-12-30), p. 1-10
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2021 ( 2021-12-30), p. 1-10
    Abstract: Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2021/3168464
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2187808-0
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  • 4
    Online Resource
    Online Resource
    Call for Papers— Management Science Special Issue on Blockchains and Crypto Economics
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Management Science Vol. 67, No. 1 ( 2021-01), p. 6-7
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 67, No. 1 ( 2021-01), p. 6-7
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2020.3888
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 5
    Online Resource
    Online Resource
    Knowledge Accumulation, Privacy, and Growth in a Data Economy
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Management Science Vol. 67, No. 10 ( 2021-10), p. 6480-6492
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 67, No. 10 ( 2021-10), p. 6480-6492
    Abstract: We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R & D sector underemploys labor and overuses data—an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers’ data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions. This paper was accepted by Kay Giesecke, finance.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2021.3986
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 6
    Online Resource
    Online Resource
    Policy Uncertainty and Innovation: Evidence from Initial Public Offering Interventions in China
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Management Science Vol. 67, No. 11 ( 2021-11), p. 7238-7261
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 67, No. 11 ( 2021-11), p. 7238-7261
    Abstract: Public equity is an important source of risk capital, especially in China. The Chinese government has occasionally suspended IPOs, exposing firms already approved to IPO to indeterminate listing delays. The temporary bar on going public increases uncertainty about access to public markets for affected firms. We show that suspension-induced delay reduces corporate innovation activity both during the delay and for years after listing. Negative effects on tangible investment and positive effects on leverage are temporary, consistent with financial constraints during the suspensions being resolved after listing. Our results suggest that predictable, well-functioning IPO markets are important for firm value creation. They demonstrate that corporate innovation is cumulative and is negatively affected by policy uncertainty. This paper was accepted by Gustavo Manso, finance.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2020.3807
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 7
    Online Resource
    Online Resource
    Timing of Auctions of Real Options
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2020
    In:  Management Science Vol. 66, No. 9 ( 2020-09), p. 3956-3976
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 66, No. 9 ( 2020-09), p. 3956-3976
    Abstract: This paper endogenizes auction timing and initiation in auctions of real options. Because bidders have information rent, a seller faces a “virtual strike price” higher than the actual exercise cost. The seller inefficiently delays the auction to encourage bidder participation and uses the irreversible nature of time to gain partial control over option exercises. The seller’s private benefit at option exercise may restore efficient auction timing, but option exercises are always inefficiently late. When the seller lacks commitment to auction timing, bidders always initiate in equilibrium, resulting in earlier option exercise and higher welfare than auctions proscribing bidder initiation. Overall, auction timing modifies the distribution of the bidder valuations and has important implications for bidding strategies, auction design, and real outcomes. This paper was accepted by Gustavo Manso, finance.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2019.3374
    RVK:
    QP 300
    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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