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作者：Coutts, Alexander; Koh, Boon Han; Murad, Zahra
作者单位：York University - Canada; University of Exeter; University of Portsmouth; Ministry of Education of Azerbaijan Republic; Azerbaijan State University of Economics (UNEC)
摘要：Feedback is vital for growth and learning, yet anecdotal evidence suggests people often hesitate to provide it, and its provision may be shaped by asymmetries and gender-related biases. We study feedback provision across variations in the nature of performance signals, their instrumental value, and the recipient's gender. We find that a surprising degree of both positive and negative feedback is withheld, with a follow-up experiment suggesting that advisors' feedback decisions are driven mainl...
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作者：Chu, Liya; Wang, Kent; Zhang, Bohui; Zhou, Guofu
作者单位：Xi'an Jiaotong University; The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; Washington University (WUSTL)
摘要：We study the relation between firms' environmental, social, and governance (ESG) performance and the aggregate stock market returns. Based on 38 individual ESG measures, we construct a market-level ESG index. With both the traditional predictive regression approach and two recently developed machine-learning methods, we find that the ESG index has strong and positive predictive power on the market both in-and out-of-sample, and both the cash flow and discount rate channels are the economic dri...
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作者：Blair, Michael R.; Alizamir, Saed; Wang, Shouqiang
作者单位：Wilfrid Laurier University; University of Virginia; University of Texas System; University of Texas Dallas
摘要：Extreme weather from climate change creates unprecedented fluctuations in residential heating and cooling demand. Understanding how households use thermostats and react to ambient weather is key to achieving demand reductions and avoiding power crises. To this end, we analyze high-frequency microlevel time-series data from smart thermostat users to examine how households adjust their thermostat operations in response to outdoor temperature. Comparing households within a city, we find that hous...
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作者：Zhang, Jasmine; Zhang, Xiao-Jun
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：This paper documents that the payoffs from investing in growth stocks, as measured by the decile-rank distributions (DRD) of future revenues, earnings, investment, and stock returns, follow a bimodal U-shaped pattern. In contrast, the DRD of value stocks follows a traditional bell-shaped distribution. This divergence in payoff structures suggests growth stocks are more prone to structural shocks, such as disruptive technologies. Consequently, their pricing is heavily influenced by investors' a...
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作者：Liao, Scott; Nicoletti, Allison; Su, Barbara
作者单位：University of Toronto; University of Pennsylvania; George Mason University
摘要：We examine whether loan loss provision validity (i.e., the extent of loan loss provisions mapping into future charge offs) within the bank holding company (BHC) is associated with internal capital allocation efficiency. Exploiting the filing requirements for subsidiary banks, we find that within-BHC provision validity, our measure of internal information quality, is positively associated with internal capital market efficiency. We also find that this association is concentrated in BHCs more li...
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作者：Eren, Egemen; Schrimpf, Andreas; Xia, Fan Dora
作者单位：Bank for International Settlements (BIS); Centre for Economic Policy Research - UK
摘要：We document that the sectoral composition and marginal buyers of government debt differ notably across jurisdictions and over time. We use instrumental variables derived from monetary policy surprises to estimate the demand elasticities of various sectors. In the United States, commercial banks and mutual funds exhibit the most priceelastic demand, whereas the foreign official sector has a price-inelastic demand. Based on these estimates and under certain assumptions, we find that a 1% increas...
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作者：Ghosh, Sukti; Singh, Tasjit
作者单位：Singapore Management University; INSEAD Business School
摘要：Many firms are pledging to reduce greenhouse gas emissions across their value chains. However, this requires their suppliers to also adopt more climate-friendly practices for decarbonization. This can involve addressing gaps in not only the suppliers' ability but also, their willingness to adopt such practices, which can be challenging if the suppliers perceive the practices as risky or potentially detrimental to their economic well-being. We examine the effectiveness of relational investments...
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作者：Flynn, Joel P.
作者单位：Yale University
摘要：This paper studies price and liquidity dynamics in the presence of costly shortselling when uninformed traders have limited willingness-to-pay to trade securities. In this setting, unraveling and Bayesian social learning interact to produce a novel mechanism, dynamic unraveling: Unraveling that generates signals that lead to future unraveling. Applying the theory, I show how dynamic unraveling explains low-volume crashes: falls in the prices of securities on low or declining trading volume. In...
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作者：Wu, Qinyu; Yu-Meng, Jonathan; Mao, Tiantian
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Ottawa
摘要：Wasserstein distributionally robust optimization (DRO) has gained prominence in operations research and machine learning as a powerful method for achieving solutions with favorable out-of-sample performance. Two compelling explanations for its success are the generalization bounds derived from Wasserstein DRO and its equivalence to regularization schemes commonly used in machine learning. However, existing results on generalization bounds and regularization equivalence are largely limited to s...
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作者：Han, Jinhui; Hu, Ming; Shen, Guohao
作者单位：Peking University; University of Toronto; Hong Kong Polytechnic University
摘要：We consider a data-driven newsvendor problem, where one has access to past demand data and the associated feature information. We solve the problem by estimating the target conditional quantile function using a deep neural network (DNN). The remarkable representational power of DNNs allows our framework to incorporate or approximate various extant data-driven models. We provide theoretical guarantees in terms of excess risk bounds for the DNN solution characterized by the network structure and...
