-
作者：Cason, Timothy N.; Tabarrok, Alex; Zubrickas, Robertas
作者单位：Purdue University System; Purdue University; George Mason University; George Mason University; University of Bath; Vilnius University
摘要：Crowdfunding can suffer from information asymmetry, leaving some investors disappointed with low-quality projects, whereas other high-quality projects remain unfunded. We show that refund bonuses, which provide investors a payment if a fundraising campaign is unsuccessful, can signal project quality and help overcome the market failure in crowdfunding. Because strong projects have a lower risk of bonus payout, entrepreneurs with strong projects are more likely to offer bonuses. This signals hi...
-
作者：Li, Zhuoxin; Wang, Gang
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Delaware
摘要：Restaurants are increasingly relying on on-demand delivery platforms (e.g., DoorDash, Grubhub, and Uber Eats) to reach customers and fulfill takeout orders. Although on-demand delivery is a valuable option for consumers, whether restaurants benefit from or are being hurt by partnering with these platforms remains unclear. This paper investigates whether and to what extent the platform delivery channel substitutes restaurants' own takeout/dine-in channels and the net impact on restaurant revenu...
-
作者：He, Qiyang; Leung, Henry; Qiu, Buhui; Zhou, Zhou
作者单位：University of Sydney; Zhejiang University
摘要：Seeking Alpha (SA) is the most popular crowdsourced social media platform specializing in the financial analysis of U.S. firms, and it attracts over 17 million visitors per month. We find that firm coverage initiation on SA significantly promotes corporate innovation activities. SA coverage promotes corporate innovation by disseminating innovationrelated information about the covered firm to external investors, thereby alleviating the firm's financial constraints. Moreover, firms with higher i...
-
作者：Chen, Zefeng; Jiang, Zhengyang
作者单位：Peking University; Northwestern University; National Bureau of Economic Research
摘要：Do digital payment technologies generate liquidity premia like cash and Treasury? We provide an estimate in the context of the world's largest digital payment platform, Alipay. Our empirical strategy exploits the variation in the timing of the introduction of money market funds that users on this platform can hold and use for digital transactions. We find that, once a fund becomes eligible for these transactions, its size increases by 45 times on average. Through the lens of an equilibrium dem...
-
作者：DeMiguel, Victor; Martin-Utrera, Alberto; Uppal, Raman
作者单位：University of London; London Business School; Iowa State University; Universite Catholique de Lille; EDHEC Business School; Center for Economic & Policy Research (CEPR)
摘要：The increasing number of institutions exploiting factor-investing strategies raises concerns that competition may erode profits. We use a game-theoretic model to show that, whereas competition among investors exploiting a particular factor erodes profits because of the negative externality of their price impact on each other, competition to exploit other factors can increase profits from the first factor because of the positive externality from trading diversification (netting of trades across...
-
作者：Zhao, Tuoyi; Zhou, Wen-Xin; Wang, Lan
作者单位：University of Miami; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：The data-driven newsvendor problem with features has recently emerged as a significant area of research, driven by the proliferation of data across various sectors such as retail, supply chains, e-commerce, and healthcare. Given the sensitive nature of customer or organizational data often used in feature-based analysis, it is crucial to ensure individual privacy to uphold trust and confidence. Despite its importance, privacy preservation in the context of inventory planning remains unexplored...
-
作者：Chen, Andrew Y.
作者单位：Federal Reserve System - USA; Federal Reserve System Board of Governors
摘要：Many scholars have called for raising statistical hurdles to guard against false discoveries in academic publications. I show these calls may be difficult to justify empirically. Published data exhibit bias: Results that fail to meet existing hurdles are often unobserved. These unobserved results must be extrapolated, which can lead to weak identification of revised hurdles. In contrast, statistics that can target only published findings (e.g. empirical Bayes shrinkage and the false discovery ...
-
作者：Glennon, Britta; Morales, Francisco; Carnahan, Seth; Hernandez, Exequiel
作者单位：University of Pennsylvania; National Bureau of Economic Research; Universidad de los Andes - Chile; Washington University (WUSTL)
摘要：We investigate the effect of employing skilled immigrants on the competitive performance of organizations by studying European football (soccer) clubs in Germany, Italy, France, England, and Spain from 1990 to 2020. Microdata from this setting offers unusual transparency about players' birthplaces and their contributions to organizational performance. Further, country-level rules govern how many immigrant players clubs can deploy. Using changes to these rules as the basis for instrumental vari...
-
作者：Fedyk, Anastassia
作者单位：University of California System; University of California Berkeley
摘要：Do individuals anticipate time inconsistency in others? This paper jointly investigates beliefs about one's own and others' present bias. In an online laboratory experiment, participants engaged in a real-effort task display little awareness of their own present bias but anticipate present bias in others. Structurally, I estimate a present bias parameter 13 of 0.82. Participants perceive others' 13 to be 0.87, indicating substantial sophistication, contrasted with 1.03 for themselves, indicati...
-
作者：Li, Sophia Zhengzi; Tang, Yushan
作者单位：Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; Shanghai University of Finance & Economics
摘要：We develop an automated system to forecast volatility by leveraging more than 100 features and five machine learning algorithms. Considering the universe of S&P 100 stocks, our system results in superior out-of-sample volatility forecasts compared with existing risk models across forecast horizons. We further demonstrate that our system remains robust to different specifications and is scalable to a broader S&P 500 stock universe via hyperparameter transfer learning. Finally, the statistical i...
