Pub Date : 2025-12-23DOI: 10.1016/j.jbankfin.2025.107611
Wenbin Cao , Xiaoman Duan , Scott Linn , Pierre Six
We expand the frequency domain asset pricing literature, traditionally focused on equities and bonds, to include the oil market. Our analysis extends to both the frequency and calendar time domains, offering new tests for the theories of storage and normal backwardation (hedging pressure). Our study highlights that the main relationships of both theories operate continuously in time at intermediate frequencies. Our analysis in the time–frequency domain enables us to refine extant conclusions regarding financialization in the oil market.
{"title":"New tests of the theory of storage and the theory of normal backwardation: Time and frequency dimensions","authors":"Wenbin Cao , Xiaoman Duan , Scott Linn , Pierre Six","doi":"10.1016/j.jbankfin.2025.107611","DOIUrl":"10.1016/j.jbankfin.2025.107611","url":null,"abstract":"<div><div>We expand the frequency domain asset pricing literature, traditionally focused on equities and bonds, to include the oil market. Our analysis extends to both the frequency and calendar time domains, offering new tests for the theories of storage and normal backwardation (hedging pressure). Our study highlights that the main relationships of both theories operate continuously in time at intermediate frequencies. Our analysis in the time–frequency domain enables us to refine extant conclusions regarding financialization in the oil market.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"183 ","pages":"Article 107611"},"PeriodicalIF":3.8,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.jbankfin.2025.107609
Jonathan A. Batten , Lanlan Liu , Yezhou Sha
This study examines how provincial legal environments shape the profitability of illegal insider trading in China. Using 521 adjudicated insider-trading cases from 2006 to 2018, we hand-collect detailed information from court judgments and CSRC sanction documents to reconstruct holding-period returns and illicit gains. We combine these data with established provincial indices of legal development and firm-level measures of ex ante litigation risk to test whether legal risk is priced in illegal insider trades. We find that stronger provincial legal environments are associated with significantly higher per-trade profitability among illegal trades that insiders execute after accounting for enforcement risk. This pattern is consistent with a risk-compensation mechanism rather than a failure of enforcement, as stricter legal environments deter low-return trades and leave only trades with sufficiently high expected gains. Firm-level litigation exposure further strengthens this effect. The results remain robust to sample-selection corrections, alternative return measures and a range of heterogeneity tests. Overall, our findings show how institutional variation in enforcement shapes insider incentives and the risk–return trade-off of illegal trading.
{"title":"Illegal insider trading profitability and the legal environment","authors":"Jonathan A. Batten , Lanlan Liu , Yezhou Sha","doi":"10.1016/j.jbankfin.2025.107609","DOIUrl":"10.1016/j.jbankfin.2025.107609","url":null,"abstract":"<div><div>This study examines how provincial legal environments shape the profitability of illegal insider trading in China. Using 521 adjudicated insider-trading cases from 2006 to 2018, we hand-collect detailed information from court judgments and CSRC sanction documents to reconstruct holding-period returns and illicit gains. We combine these data with established provincial indices of legal development and firm-level measures of ex ante litigation risk to test whether legal risk is priced in illegal insider trades. We find that stronger provincial legal environments are associated with significantly higher per-trade profitability among illegal trades that insiders execute after accounting for enforcement risk. This pattern is consistent with a risk-compensation mechanism rather than a failure of enforcement, as stricter legal environments deter low-return trades and leave only trades with sufficiently high expected gains. Firm-level litigation exposure further strengthens this effect. The results remain robust to sample-selection corrections, alternative return measures and a range of heterogeneity tests. Overall, our findings show how institutional variation in enforcement shapes insider incentives and the risk–return trade-off of illegal trading.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"185 ","pages":"Article 107609"},"PeriodicalIF":3.8,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.jbankfin.2025.107606
Felix Bekemeier , Fabian Schär , Hato Schmeiser
This paper presents a model in which risk-averse individuals can purchase insurance via traditional indemnity contracts or Decentralized Finance (DeFi) smart contract-based instruments. The model incorporates key features of DeFi insurance, including parametric payouts, basis risk arising from imperfect loss verification and pooled collateralization involving the risk of liquidity shortfalls. We characterize optimal insurance choices as a function of pricing, payout correlation and risk preferences. Numerical results show that DeFi insurance can complement or replace traditional coverage, improving welfare when basis and default risks are moderate or pricing advantages are substantial. The analysis reveals how DeFi-specific frictions shape insurance demand and provides insight into how DeFi instruments may shift market structure and expand the set of attainable risk transfer outcomes.
{"title":"Decentralized Finance risk transfer and smart contract-based insurance","authors":"Felix Bekemeier , Fabian Schär , Hato Schmeiser","doi":"10.1016/j.jbankfin.2025.107606","DOIUrl":"10.1016/j.jbankfin.2025.107606","url":null,"abstract":"<div><div>This paper presents a model in which risk-averse individuals can purchase insurance via traditional indemnity contracts or Decentralized Finance (DeFi) smart contract-based instruments. The model incorporates key features of DeFi insurance, including parametric payouts, basis risk arising from imperfect loss verification and pooled collateralization involving the risk of liquidity shortfalls. We characterize optimal insurance choices as a function of pricing, payout correlation and risk preferences. Numerical results show that DeFi insurance can complement or replace traditional coverage, improving welfare when basis and default risks are moderate or pricing advantages are substantial. The analysis reveals how DeFi-specific frictions shape insurance demand and provides insight into how DeFi instruments may shift market structure and expand the set of attainable risk transfer outcomes.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"183 ","pages":"Article 107606"},"PeriodicalIF":3.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1016/j.jbankfin.2025.107599
Guillaume Coqueret , Bertrand Tavin , Yuxin Zhou
We examine the asset pricing implications of sustainability in commodity markets. We focus on metals and agricultural goods, for which we collect production-based environmental footprint data, namely greenhouse gas emissions and water consumption. We then build green-minus-brown portfolios and find no evidence that sustainability is priced in the cross-section of metals’ and agricultural commodities’ returns. We also document strong welfare benefits when diversifying equity and bond allocations with low-carbon commodities. Investor welfare, measured by the certainty equivalent return, increases by 22% when the commodity share is 20%. These results reveal the dual opportunities, both financial and environmental, brought by low footprint commodities.
{"title":"Sustainability in commodity markets","authors":"Guillaume Coqueret , Bertrand Tavin , Yuxin Zhou","doi":"10.1016/j.jbankfin.2025.107599","DOIUrl":"10.1016/j.jbankfin.2025.107599","url":null,"abstract":"<div><div>We examine the asset pricing implications of sustainability in commodity markets. We focus on metals and agricultural goods, for which we collect production-based environmental footprint data, namely greenhouse gas emissions and water consumption. We then build green-minus-brown portfolios and find no evidence that sustainability is priced in the cross-section of metals’ and agricultural commodities’ returns. We also document strong welfare benefits when diversifying equity and bond allocations with low-carbon commodities. Investor welfare, measured by the certainty equivalent return, increases by 22% when the commodity share is 20%. These results reveal the dual opportunities, both financial and environmental, brought by low footprint commodities.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"184 ","pages":"Article 107599"},"PeriodicalIF":3.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.jbankfin.2025.107598
Christian Fieberg , Matthies Hesse , Gerrit Liedtke , Adam Zaremba
Can generative artificial intelligence (GenAI) help us predict financial stability? To address this question, we employ TopicGPT, a prompt-based framework for topic modeling powered by large language models. By analyzing over 238,000 corporate earnings calls and 4300 Federal Reserve speeches over the period from 2002 to 2023, we combine microeconomic and macroeconomic perspectives to forecast key measures of financial stability. TopicGPT’s ability to generate interpretable and tailored topics improves predictions for systemic risk measures, such as the National Financial Conditions Index and a capital shortfall, outperforming traditional models, particularly for long-term horizons. The two data sources complement each other: earnings calls provide dynamic, firm-specific insights critical for short-term forecasts, while Fed speeches highlight systemic risks, offering a long-term perspective. Together, they identify critical themes – such as economic conditions, debt management, and the housing market – and enable real-time risk assessment.
{"title":"Predicting financial stability with TopicGPT: Insights from corporate and central bank communications","authors":"Christian Fieberg , Matthies Hesse , Gerrit Liedtke , Adam Zaremba","doi":"10.1016/j.jbankfin.2025.107598","DOIUrl":"10.1016/j.jbankfin.2025.107598","url":null,"abstract":"<div><div>Can generative artificial intelligence (GenAI) help us predict financial stability? To address this question, we employ TopicGPT, a prompt-based framework for topic modeling powered by large language models. By analyzing over 238,000 corporate earnings calls and 4300 Federal Reserve speeches over the period from 2002 to 2023, we combine microeconomic and macroeconomic perspectives to forecast key measures of financial stability. TopicGPT’s ability to generate interpretable and tailored topics improves predictions for systemic risk measures, such as the National Financial Conditions Index and a capital shortfall, outperforming traditional models, particularly for long-term horizons. The two data sources complement each other: earnings calls provide dynamic, firm-specific insights critical for short-term forecasts, while Fed speeches highlight systemic risks, offering a long-term perspective. Together, they identify critical themes – such as economic conditions, debt management, and the housing market – and enable real-time risk assessment.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"183 ","pages":"Article 107598"},"PeriodicalIF":3.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145712143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-25DOI: 10.1016/j.jbankfin.2025.107596
Heiko Jacobs, Alexander Lauber
Recent literature shows that investors’ revealed beliefs often point to the use of comparatively simple valuation approaches or heuristics rather than complex models with several dimensions of systematic risk to price assets. Against this background, we comprehensively analyze how different stock-level performance measures affect media tone in firm-specific articles in several major markets. While the realized risk-adjusted abnormal returns of all tested models are positively related to media sentiment, the CAPM-adjusted return as well as the raw stock return have the strongest impact in direct comparisons. Overall, the results are most consistent with the conjecture that, on average, reporting tends to be influenced more by straightforward valuation approaches than by risk adjustments derived from multi-factor asset pricing models. Further largely supportive evidence comes from return decompositions, subsample tests, reporting about mutual funds as well as from survey results.
{"title":"Media reporting and asset pricing models","authors":"Heiko Jacobs, Alexander Lauber","doi":"10.1016/j.jbankfin.2025.107596","DOIUrl":"10.1016/j.jbankfin.2025.107596","url":null,"abstract":"<div><div>Recent literature shows that investors’ revealed beliefs often point to the use of comparatively simple valuation approaches or heuristics rather than complex models with several dimensions of systematic risk to price assets. Against this background, we comprehensively analyze how different stock-level performance measures affect media tone in firm-specific articles in several major markets. While the realized risk-adjusted abnormal returns of all tested models are positively related to media sentiment, the CAPM-adjusted return as well as the raw stock return have the strongest impact in direct comparisons. Overall, the results are most consistent with the conjecture that, on average, reporting tends to be influenced more by straightforward valuation approaches than by risk adjustments derived from multi-factor asset pricing models. Further largely supportive evidence comes from return decompositions, subsample tests, reporting about mutual funds as well as from survey results.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"182 ","pages":"Article 107596"},"PeriodicalIF":3.8,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1016/j.jbankfin.2025.107597
Doron Avramov , Si Cheng , Andrea Tarelli
This paper develops and tests an equilibrium model of active fund management with ESG considerations. Heterogeneous sustainability preferences lead fund managers to intensify information acquisition on assets across the ESG spectrum, broadening the scope of active management. This information channel enhances price informativeness, lowers discount rates, and increases portfolio deviation from benchmarks. The model predicts a negative and concave ESG-expected return relation, stronger for green assets and weaker for brown assets. Using data on U.S. mutual funds and stocks from 2007–2021, we find supporting evidence based on price informativeness and the implied cost of equity capital.
{"title":"Active fund management when ESG matters","authors":"Doron Avramov , Si Cheng , Andrea Tarelli","doi":"10.1016/j.jbankfin.2025.107597","DOIUrl":"10.1016/j.jbankfin.2025.107597","url":null,"abstract":"<div><div>This paper develops and tests an equilibrium model of active fund management with ESG considerations. Heterogeneous sustainability preferences lead fund managers to intensify information acquisition on assets across the ESG spectrum, broadening the scope of active management. This information channel enhances price informativeness, lowers discount rates, and increases portfolio deviation from benchmarks. The model predicts a negative and concave ESG-expected return relation, stronger for green assets and weaker for brown assets. Using data on U.S. mutual funds and stocks from 2007–2021, we find supporting evidence based on price informativeness and the implied cost of equity capital.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"182 ","pages":"Article 107597"},"PeriodicalIF":3.8,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.jbankfin.2025.107595
Nils Lohmeier, Christoph Schneider
A familiarity bias of target shareholders allows bidders to opportunistically choose the payment method in mergers and acquisitions. We employ the Stambaugh, Yu and Yuan (2015) mispricing score to identify overvalued bidders, reconfirming that overvaluation is a central driver of the payment choice. Using an instrumental variable based on exogenous price pressure, we provide causal evidence for bidder opportunism. Further analyses show that target shareholders more familiar with the bidder are more likely to accept equity despite particularly adverse market reactions. Our results suggest that behavioral biases of shareholders contribute to the transmission of stock market inefficiencies to the market for corporate control.
{"title":"Bidder opportunism, familiarity, and the M&A payment choice","authors":"Nils Lohmeier, Christoph Schneider","doi":"10.1016/j.jbankfin.2025.107595","DOIUrl":"10.1016/j.jbankfin.2025.107595","url":null,"abstract":"<div><div>A familiarity bias of target shareholders allows bidders to opportunistically choose the payment method in mergers and acquisitions. We employ the Stambaugh, Yu and Yuan (2015) mispricing score to identify overvalued bidders, reconfirming that overvaluation is a central driver of the payment choice. Using an instrumental variable based on exogenous price pressure, we provide causal evidence for bidder opportunism. Further analyses show that target shareholders more familiar with the bidder are more likely to accept equity despite particularly adverse market reactions. Our results suggest that behavioral biases of shareholders contribute to the transmission of stock market inefficiencies to the market for corporate control.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"182 ","pages":"Article 107595"},"PeriodicalIF":3.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.jbankfin.2025.107592
Yuecheng Jia , Betty Simkins , Shu Yan , Hongyu Zhang , Jiangyu Zhao
This paper investigates whether investors’ anchoring bias affects cryptocurrency returns. We use the nearness to the 52-week high () as a proxy for anchoring behavior and document a significant positive association between and subsequent cross-sectional cryptocurrency returns. The relationship remains robust after controlling for standard return predictors and employing alternative econometric specifications. A value-weighted spread portfolio, cANCHOR, which goes long on cryptocurrencies with high and short on those with low , generates an average return of around 130 basis points per week. Additional analyses help rule out competing explanations based on risk exposure or market frictions. Augmenting the benchmark three-factor model of Liu, Tsyvinski, and Wu (2019) with our cANCHOR factor yields a novel four-factor model that better explains cross-sectional cryptocurrency returns and outperforms alternative approaches proposed in the literature.
{"title":"Psychological anchoring effect and cross section of cryptocurrency returns","authors":"Yuecheng Jia , Betty Simkins , Shu Yan , Hongyu Zhang , Jiangyu Zhao","doi":"10.1016/j.jbankfin.2025.107592","DOIUrl":"10.1016/j.jbankfin.2025.107592","url":null,"abstract":"<div><div>This paper investigates whether investors’ anchoring bias affects cryptocurrency returns. We use the nearness to the 52-week high (<span><math><mrow><mi>N</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>e</mi><mi>s</mi><msub><mrow><mi>s</mi></mrow><mrow><mn>52</mn></mrow></msub></mrow></math></span>) as a proxy for anchoring behavior and document a significant positive association between <span><math><mrow><mi>N</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>e</mi><mi>s</mi><msub><mrow><mi>s</mi></mrow><mrow><mn>52</mn></mrow></msub></mrow></math></span> and subsequent cross-sectional cryptocurrency returns. The relationship remains robust after controlling for standard return predictors and employing alternative econometric specifications. A value-weighted spread portfolio, cANCHOR, which goes long on cryptocurrencies with high <span><math><mrow><mi>N</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>e</mi><mi>s</mi><msub><mrow><mi>s</mi></mrow><mrow><mn>52</mn></mrow></msub></mrow></math></span> and short on those with low <span><math><mrow><mi>N</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>e</mi><mi>s</mi><msub><mrow><mi>s</mi></mrow><mrow><mn>52</mn></mrow></msub></mrow></math></span>, generates an average return of around 130 basis points per week. Additional analyses help rule out competing explanations based on risk exposure or market frictions. Augmenting the benchmark three-factor model of Liu, Tsyvinski, and Wu (2019) with our cANCHOR factor yields a novel four-factor model that better explains cross-sectional cryptocurrency returns and outperforms alternative approaches proposed in the literature.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"182 ","pages":"Article 107592"},"PeriodicalIF":3.8,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-05DOI: 10.1016/j.jbankfin.2025.107585
Jonathan Chiu , Thorsten V. Koeppl
Why do BigTech platforms introduce payment services? And do their users benefit? Digital platforms often run business models where activities on the platform generate data that can be monetized off the platform. The platform then trades off the value of such data against the cost that arises from subsidizing activities in order to compensate users for their loss of privacy. The way data interact with payments determines whether payments are introduced and how the introduction impacts users. When data help to provide better payments (data-driven payments), platforms have too little incentives to introduce payments, even though users benefit. Introduction is more likely when payments also generate additional data (payment-driven data), but the adoption of better payments may then hurt users.
{"title":"PayTech on BigTech platforms","authors":"Jonathan Chiu , Thorsten V. Koeppl","doi":"10.1016/j.jbankfin.2025.107585","DOIUrl":"10.1016/j.jbankfin.2025.107585","url":null,"abstract":"<div><div>Why do BigTech platforms introduce payment services? And do their users benefit? Digital platforms often run business models where activities on the platform generate data that can be monetized off the platform. The platform then trades off the value of such data against the cost that arises from subsidizing activities in order to compensate users for their loss of privacy. The way data interact with payments determines whether payments are introduced and how the introduction impacts users. When data help to provide better payments (data-driven payments), platforms have too little incentives to introduce payments, even though users benefit. Introduction is more likely when payments also generate additional data (payment-driven data), but the adoption of better payments may then hurt users.</div></div>","PeriodicalId":48460,"journal":{"name":"Journal of Banking & Finance","volume":"182 ","pages":"Article 107585"},"PeriodicalIF":3.8,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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