Search

The Impact of the Prohibition of Trade-through In the paper titled "Does the Prohibition of Trade-Through Hurt Liquidity Demanders?" the authors explore the effects of the order protect rule (OPR) in the United States, which prohibits any trade-through in the stock market that does not execute at the best possible price among fast trading venues. The study found that, whereas trade-throughs may offer flexible trading strategies that can benefit liquidity demanders, the benefits are insignificant for most cases, especially for small trades and stocks with fast resilience. The paper suggests that the current separate regulations for fast and slow venues may be extended to differentiate stocks with fast and slow resilience speeds. The results are supported by a case study using the data from the Australian Securities Exchange. Overall, this study supports the regulation of the OPR. The order protect rule (OPR) in the United States generally prohibits any trade-through, that is, a market order that is not executed at the best possible price among fast (electronic and automated) trading venues. By deriving upper and lower bounds for the difference in the execution costs in a dynamic model, we find that, although trade-through allows for flexible trading strategies and may benefit the liquidity demander, the benefit is insignificant in most cases, especially for small trades and stocks with fast resilience. Therefore, considering other benefits of the OPR studied in the literature, this study supports the regulation and suggests that the current separate regulations for fast and slow venues may be extended to differentiate stocks with fast and slow resilience speeds. Funding: P. Gao received financial support from the National Natural Science Foundation of China [Grants 72201234 and 72192805] and the Research Grant Council of Hong Kong (the Collaborative Research Fund) [Grant C6032-21G]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2454. [ABSTRACT FROM AUTHOR]

Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on market performance by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we design four treatment groups with different choice capacities in which users can only interact with other users in the same group and randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs and generalize our findings by discussing how choice capacity can be designed to increase engagement and matching outcomes. Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on the number of choices and matches made on a platform by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we (1) select users who are of a similar age and live in the same geographical location, (2) design four treatment groups with different choice capacities in which users can only interact with other users in the same group, and (3) randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We posit and empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs. Furthermore, we generalize our findings to other online matching platforms and discuss how choice capacity can be designed to increase engagement and matching outcomes. History: Ravi Bapna, Martin Bichler, Bob Day, and Wolfgang Ketter, Senior Editors; Thayer Morrill, Associate Editor. This paper has been accepted for the Information Systems Research Special Section on Market Design and Analytics. Funding: The work described in this paper was substantially supported by the Early Career Scheme grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. HKUST 26504318]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.1028. [ABSTRACT FROM AUTHOR]

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation. History: Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: Z. Chen was supported by [General Research Fund Grant 9043424, NSFC/RGC Joint Research Scheme N_CityU105/21] from the Hong Kong Research Grants Council. S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"] and the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0274) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0274). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Financial support by the Early Career Scheme from the Hong Kong Research Grants Council [Project No. CityU 21502820], the CityU Start-Up Grant [Project No. 9610481], the CityU Strategic Research Grant [Project No. 7005688], the National Natural Science Foundation of China [Project No. 72032005], and Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited [Project No. 9229076] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0181), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0181). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Corporate bonds with large increases in implied volatility over the past month underperform those with large decreases in implied volatility by 0.6% per month. In contrast to existing studies that show implied volatility changes carry information about fundamental news, our evidence suggests that implied volatility changes contain information about uncertainty shocks to the firm. Our results are consistent with the notion that informed traders with new information about firm risk prefer to trade in the option market and the corporate bond market underreacts to this information. This paper was accepted by Haoxiang Zhu, finance. Funding: We thank the Canadian Derivatives Institute for financial support. J. Cao and X. Zhan acknowledge generous financial support of the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants GRF 14501720, 14500919]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4379. [ABSTRACT FROM AUTHOR]