Pub Date : 2026-01-01Epub Date: 2026-02-03DOI: 10.1186/s40854-026-00908-x
Karel Hrazdil, Pavel Král, Jiri Novak, Nattavut Suwanyangyuan
We examine how academic journal reviewers' experience with the peer-review process influences their propensity to recommend manuscript acceptance or rejection. We use data on the total recommended rejections and acceptances for all referees who reviewed at least one paper for the Journal of Financial Economics (JFE) between 1994 and 2020. We show that reviewers who write more reports are more likely to recommend the acceptance of manuscripts. We also find that older reviewers, those who graduated from or are affiliated with prestigious universities, and those with more and highly cited publications are more likely to recommend acceptance. There is also some evidence that reviewers with doctoral training in economics, mathematics, physics, and engineering are more likely to recommend acceptance than those with a PhD in finance. We find no consistent evidence of significant differences between genders or among reviewer demographic characteristics. We also document that reviewers who themselves publish more successfully in JFE and publish highly cited articles are, ceteris paribus, more likely to recommend rejection of reviewed manuscripts. Our study utilizes a unique research setting to gain new insights into the determinants of the peer-review process in scientific journals.
{"title":"On the determinants of journal rejection rates: evidence from the Journal of Financial Economics.","authors":"Karel Hrazdil, Pavel Král, Jiri Novak, Nattavut Suwanyangyuan","doi":"10.1186/s40854-026-00908-x","DOIUrl":"10.1186/s40854-026-00908-x","url":null,"abstract":"<p><p>We examine how academic journal reviewers' experience with the peer-review process influences their propensity to recommend manuscript acceptance or rejection. We use data on the total recommended rejections and acceptances for all referees who reviewed at least one paper for the <i>Journal of Financial Economics (JFE)</i> between 1994 and 2020. We show that reviewers who write more reports are more likely to recommend the acceptance of manuscripts. We also find that older reviewers, those who graduated from or are affiliated with prestigious universities, and those with more and highly cited publications are more likely to recommend acceptance. There is also some evidence that reviewers with doctoral training in economics, mathematics, physics, and engineering are more likely to recommend acceptance than those with a PhD in finance. We find no consistent evidence of significant differences between genders or among reviewer demographic characteristics. We also document that reviewers who themselves publish more successfully in JFE and publish highly cited articles are, <i>ceteris paribus</i>, more likely to recommend rejection of reviewed manuscripts. Our study utilizes a unique research setting to gain new insights into the determinants of the peer-review process in scientific journals.</p>","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"12 1","pages":"52"},"PeriodicalIF":7.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12868043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146126760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-02-09DOI: 10.1186/s40854-025-00848-y
Huei-Wen Teng, Wolfgang Karl Härdle, Joerg Osterrieder, Daniel Traian Pele, Lennart John Baals, Vassilios Papavassiliou, Karolina Bolesta, Audrius Kabašinskas, Olivija Filipovska, Nikolaos S Thomaidis, Alexios-Ioannis Moukas, Sam Goundar, Jamal Abdul Nasir, Abraham Itzhak Weinberg, Veni Arakelian, Ciprian-Octavian Truică, Mutlu Akar, Esra Kabaklarlı, Elena-Simona Apostol, Maria Iannario, Barbara Bȩdowska-Sójka, Hanna Kristín Skaftadóttir, Ozgur Yildirim, Albulena Shala, Galena Pisoni, Ioana Florina Coita, Szabolcs Korba, Christian M Hafner, Peter Schwendner, Bálint Molnár, Elda Xhumari
Digital assets (DAs) such as cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies, are transforming financial markets with new business models, investment opportunities, and transaction efficiencies. Underpinned by blockchain, distributed ledger technology, and smart contracts, digital innovations are reshaping the financial ecosystem. However, their rapid growth introduces substantial risks, including fraud, market manipulation, cybersecurity threats, and regulatory uncertainty. This position paper offers an interdisciplinary and empirically grounded analysis of the DA landscape. We define and classify major asset types, trace their evolution from speculative instruments to functional tools, and assess current adoption trends. Additional technological developments (e.g., decentralized finance and NFT expansion) are examined for their role in accelerating this transformation. We also analyze the global regulatory landscape, highlighting jurisdictional differences, classification challenges, and emerging governance frameworks. To address key risks, we derive mitigation strategies via quantitative analysis and case-based evidence. The risks include balancing innovation with investor protection through adaptive regulatory design, promoting cross-border regulatory harmonization to prevent arbitrage and fragmentation, and supporting experimentation through regulatory sandboxes and innovation hubs. By adopting a forward-looking, evidence-based, and collaborative regulatory approaches, stakeholders can harness the benefits of DAs while managing systemic risks and maintaining market integrity.
{"title":"Digital assets: risks, regulations, mitigation.","authors":"Huei-Wen Teng, Wolfgang Karl Härdle, Joerg Osterrieder, Daniel Traian Pele, Lennart John Baals, Vassilios Papavassiliou, Karolina Bolesta, Audrius Kabašinskas, Olivija Filipovska, Nikolaos S Thomaidis, Alexios-Ioannis Moukas, Sam Goundar, Jamal Abdul Nasir, Abraham Itzhak Weinberg, Veni Arakelian, Ciprian-Octavian Truică, Mutlu Akar, Esra Kabaklarlı, Elena-Simona Apostol, Maria Iannario, Barbara Bȩdowska-Sójka, Hanna Kristín Skaftadóttir, Ozgur Yildirim, Albulena Shala, Galena Pisoni, Ioana Florina Coita, Szabolcs Korba, Christian M Hafner, Peter Schwendner, Bálint Molnár, Elda Xhumari","doi":"10.1186/s40854-025-00848-y","DOIUrl":"10.1186/s40854-025-00848-y","url":null,"abstract":"<p><p>Digital assets (DAs) such as cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies, are transforming financial markets with new business models, investment opportunities, and transaction efficiencies. Underpinned by blockchain, distributed ledger technology, and smart contracts, digital innovations are reshaping the financial ecosystem. However, their rapid growth introduces substantial risks, including fraud, market manipulation, cybersecurity threats, and regulatory uncertainty. This position paper offers an interdisciplinary and empirically grounded analysis of the DA landscape. We define and classify major asset types, trace their evolution from speculative instruments to functional tools, and assess current adoption trends. Additional technological developments (e.g., decentralized finance and NFT expansion) are examined for their role in accelerating this transformation. We also analyze the global regulatory landscape, highlighting jurisdictional differences, classification challenges, and emerging governance frameworks. To address key risks, we derive mitigation strategies via quantitative analysis and case-based evidence. The risks include balancing innovation with investor protection through adaptive regulatory design, promoting cross-border regulatory harmonization to prevent arbitrage and fragmentation, and supporting experimentation through regulatory sandboxes and innovation hubs. By adopting a forward-looking, evidence-based, and collaborative regulatory approaches, stakeholders can harness the benefits of DAs while managing systemic risks and maintaining market integrity.</p>","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"12 1","pages":"65"},"PeriodicalIF":7.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study provides a comprehensive review of machine learning (ML) applications in the fields of business and finance. First, it introduces the most commonly used ML techniques and explores their diverse applications in marketing, stock analysis, demand forecasting, and energy marketing. In particular, this review critically analyzes over 100 articles and reveals a strong inclination toward deep learning techniques, such as deep neural, convolutional neural, and recurrent neural networks, which have garnered immense popularity in financial contexts owing to their remarkable performance. This review shows that ML techniques, particularly deep learning, demonstrate substantial potential for enhancing business decision-making processes and achieving more accurate and efficient predictions of financial outcomes. In particular, ML techniques exhibit promising research prospects in cryptocurrencies, financial crime detection, and marketing, underscoring the extensive opportunities in these areas. However, some limitations regarding ML applications in the business and finance domains remain, including issues related to linguistic information processes, interpretability, data quality, generalization, and the oversights related to social networks and causal relationships. Thus, addressing these challenges is a promising avenue for future research.
本研究全面回顾了机器学习（ML）在商业和金融领域的应用。首先，它介绍了最常用的 ML 技术，并探讨了它们在市场营销、股票分析、需求预测和能源营销中的各种应用。特别是，这篇综述批判性地分析了 100 多篇文章，并揭示了深度学习技术的强烈倾向，如深度神经网络、卷积神经网络和递归神经网络。本综述表明，ML 技术，尤其是深度学习，在增强商业决策过程和实现更准确、更高效的金融结果预测方面展现出巨大的潜力。特别是，ML 技术在加密货币、金融犯罪检测和市场营销方面展现出了广阔的研究前景，凸显了这些领域的巨大商机。然而，ML 在商业和金融领域的应用仍存在一些局限性，包括与语言信息处理、可解释性、数据质量、泛化有关的问题，以及与社交网络和因果关系有关的疏漏。因此，应对这些挑战是未来研究的一个大有可为的途径。
{"title":"Machine learning in business and finance: a literature review and research opportunities","authors":"Hanyao Gao, Gang Kou, Haiming Liang, Hengjie Zhang, Xiangrui Chao, Cong-Cong Li, Yucheng Dong","doi":"10.1186/s40854-024-00629-z","DOIUrl":"https://doi.org/10.1186/s40854-024-00629-z","url":null,"abstract":"This study provides a comprehensive review of machine learning (ML) applications in the fields of business and finance. First, it introduces the most commonly used ML techniques and explores their diverse applications in marketing, stock analysis, demand forecasting, and energy marketing. In particular, this review critically analyzes over 100 articles and reveals a strong inclination toward deep learning techniques, such as deep neural, convolutional neural, and recurrent neural networks, which have garnered immense popularity in financial contexts owing to their remarkable performance. This review shows that ML techniques, particularly deep learning, demonstrate substantial potential for enhancing business decision-making processes and achieving more accurate and efficient predictions of financial outcomes. In particular, ML techniques exhibit promising research prospects in cryptocurrencies, financial crime detection, and marketing, underscoring the extensive opportunities in these areas. However, some limitations regarding ML applications in the business and finance domains remain, including issues related to linguistic information processes, interpretability, data quality, generalization, and the oversights related to social networks and causal relationships. Thus, addressing these challenges is a promising avenue for future research.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"4 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1186/s40854-023-00579-y
Kun Guo, Yuxin Kang, Qiang Ji, Dayong Zhang
Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.
{"title":"Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers","authors":"Kun Guo, Yuxin Kang, Qiang Ji, Dayong Zhang","doi":"10.1186/s40854-023-00579-y","DOIUrl":"https://doi.org/10.1186/s40854-023-00579-y","url":null,"abstract":"Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"4 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1186/s40854-024-00660-0
Tristan Lim
This study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities, to improve the liquidity provision of real-world AMMs. The proposed architecture augments Uniswap V3, a cryptocurrency AMM protocol, by using a novel market equilibrium pricing to reduce divergence and slippage losses. Furthermore, the proposed architecture involves a predictive AMM capability, for which a deep hybrid long short-term memory (LSTM) and Q-learning reinforcement learning framework is used. It seeks to improve market efficiency through obtaining more accurate forecasts of liquidity concentration ranges, where liquidity starts moving to expected concentration ranges prior to asset price movement; thus, liquidity utilization is improved. The augmented protocol framework is expected to have practical real-world implications through (1) reducing divergence loss for liquidity providers; (2) reducing slippage for crypto-asset traders; and (3) improving capital efficiency for liquidity provision for the AMM protocol. The proposed architecture is empirically benchmarked against the well-established Uniswap V3 AMM architecture. The preliminary findings indicate that the novel AMM framework offers enhanced capital efficiency, reduced divergence loss, and diminished slippage, which could potentially address several of the challenges inherent to AMMs.
{"title":"Predictive crypto-asset automated market maker architecture for decentralized finance using deep reinforcement learning","authors":"Tristan Lim","doi":"10.1186/s40854-024-00660-0","DOIUrl":"https://doi.org/10.1186/s40854-024-00660-0","url":null,"abstract":"This study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities, to improve the liquidity provision of real-world AMMs. The proposed architecture augments Uniswap V3, a cryptocurrency AMM protocol, by using a novel market equilibrium pricing to reduce divergence and slippage losses. Furthermore, the proposed architecture involves a predictive AMM capability, for which a deep hybrid long short-term memory (LSTM) and Q-learning reinforcement learning framework is used. It seeks to improve market efficiency through obtaining more accurate forecasts of liquidity concentration ranges, where liquidity starts moving to expected concentration ranges prior to asset price movement; thus, liquidity utilization is improved. The augmented protocol framework is expected to have practical real-world implications through (1) reducing divergence loss for liquidity providers; (2) reducing slippage for crypto-asset traders; and (3) improving capital efficiency for liquidity provision for the AMM protocol. The proposed architecture is empirically benchmarked against the well-established Uniswap V3 AMM architecture. The preliminary findings indicate that the novel AMM framework offers enhanced capital efficiency, reduced divergence loss, and diminished slippage, which could potentially address several of the challenges inherent to AMMs.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"44 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1186/s40854-024-00609-3
Christian Urom, Gideon Ndubuisi, Hela Mzoughi, Khaled Guesmi
This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.
{"title":"Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations","authors":"Christian Urom, Gideon Ndubuisi, Hela Mzoughi, Khaled Guesmi","doi":"10.1186/s40854-024-00609-3","DOIUrl":"https://doi.org/10.1186/s40854-024-00609-3","url":null,"abstract":"This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"28 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1186/s40854-024-00663-x
Michael Cary
Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, “wagmi” (We’re all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts’ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (“wagmi”). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.
{"title":"Herding and investor sentiment after the cryptocurrency crash: evidence from Twitter and natural language processing","authors":"Michael Cary","doi":"10.1186/s40854-024-00663-x","DOIUrl":"https://doi.org/10.1186/s40854-024-00663-x","url":null,"abstract":"Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, “wagmi” (We’re all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts’ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (“wagmi”). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"41 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1186/s40854-024-00661-z
José Campino, Bruna Rodrigues
Cryptocurrency adoption has gained significant attention across various fields owing to its disruptive potential and associated challenges. However, companies' adoption of cryptocurrencies remains relatively low. This study aims to comprehensively examine the factors influencing cryptocurrency adoption, their interrelationships, and their relative importance. To achieve this objective, we employ a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach coupled with network analysis tools. By adopting a practical approach rather than a purely theoretical one, our unique contribution lies in the valuable insights derived from experienced Chief Financial Officers (CFOs) of various companies with experience in both traditional finance and cryptocurrencies. Furthermore, the unique blend of analytical rigor and industry expertise supports the study's relevance, offering nuanced insights that are not only academically robust but also immediately applicable in the corporate landscape. Our findings highlight the paramount importance of safety in transactions and trust in the chosen platform for companies considering cryptocurrency adoption. Additionally, criteria such as faster transactions without geographical limitations, lower transaction fees, seamless integration with existing systems, and potential cost savings are identified as crucial drivers. Both the DEMATEL approach and network analysis reveal strong interconnections among the criteria, emphasizing their interdependence and, notably, their reliance on transactional safety. Furthermore, our causes and effects analysis indicates that CFOs perceive company-led cryptocurrency adoption to positively impact the broader cryptocurrency market.
{"title":"From CFOs to crypto: exploratory study unraveling factors in corporate adoption","authors":"José Campino, Bruna Rodrigues","doi":"10.1186/s40854-024-00661-z","DOIUrl":"https://doi.org/10.1186/s40854-024-00661-z","url":null,"abstract":"Cryptocurrency adoption has gained significant attention across various fields owing to its disruptive potential and associated challenges. However, companies' adoption of cryptocurrencies remains relatively low. This study aims to comprehensively examine the factors influencing cryptocurrency adoption, their interrelationships, and their relative importance. To achieve this objective, we employ a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach coupled with network analysis tools. By adopting a practical approach rather than a purely theoretical one, our unique contribution lies in the valuable insights derived from experienced Chief Financial Officers (CFOs) of various companies with experience in both traditional finance and cryptocurrencies. Furthermore, the unique blend of analytical rigor and industry expertise supports the study's relevance, offering nuanced insights that are not only academically robust but also immediately applicable in the corporate landscape. Our findings highlight the paramount importance of safety in transactions and trust in the chosen platform for companies considering cryptocurrency adoption. Additionally, criteria such as faster transactions without geographical limitations, lower transaction fees, seamless integration with existing systems, and potential cost savings are identified as crucial drivers. Both the DEMATEL approach and network analysis reveal strong interconnections among the criteria, emphasizing their interdependence and, notably, their reliance on transactional safety. Furthermore, our causes and effects analysis indicates that CFOs perceive company-led cryptocurrency adoption to positively impact the broader cryptocurrency market.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"5 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.1186/s40854-024-00610-w
Xiaozhen Jing, Dezhong Xu, Bin Li, Tarlok Singh
We propose a new predictor—the innovation in the daily return minimum in the U.S. stock market ( $$Delta {MIN}^{US}$$ )—for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to July 2022, we find that $$Delta {MIN}^{US}$$ have strong predictive power for returns in most international stock markets: $$Delta {MIN}^{US}$$ negatively predicts the next-month stock market returns. The results remain robust after controlling for a number of macroeconomic predictors and conducting subsample and panel data analyses, indicating that $$Delta {MIN}^{US}$$ has significant predictive power and it outperforms other variables in international markets. Notably, $$Delta {MIN}^{US}$$ demonstrates excellent predictive power even during the periods driven by financial upheavals (e.g., Global Financial Crisis and European Sovereign Debt Crisis). Both panel regressions and out-of-sample tests also support the robust predictive performance of $$Delta {MIN}^{US}$$ . The predictive power, however, disappears during the non-financial crisis caused by COVID-19 pandemic, which is originated from the health sector rather than the financial sector. The results provide a new perspective on U.S. extreme indicator in stock market return predictability.
{"title":"Does the U.S. extreme indicator matter in stock markets? International evidence","authors":"Xiaozhen Jing, Dezhong Xu, Bin Li, Tarlok Singh","doi":"10.1186/s40854-024-00610-w","DOIUrl":"https://doi.org/10.1186/s40854-024-00610-w","url":null,"abstract":"We propose a new predictor—the innovation in the daily return minimum in the U.S. stock market ( $$Delta {MIN}^{US}$$ )—for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to July 2022, we find that $$Delta {MIN}^{US}$$ have strong predictive power for returns in most international stock markets: $$Delta {MIN}^{US}$$ negatively predicts the next-month stock market returns. The results remain robust after controlling for a number of macroeconomic predictors and conducting subsample and panel data analyses, indicating that $$Delta {MIN}^{US}$$ has significant predictive power and it outperforms other variables in international markets. Notably, $$Delta {MIN}^{US}$$ demonstrates excellent predictive power even during the periods driven by financial upheavals (e.g., Global Financial Crisis and European Sovereign Debt Crisis). Both panel regressions and out-of-sample tests also support the robust predictive performance of $$Delta {MIN}^{US}$$ . The predictive power, however, disappears during the non-financial crisis caused by COVID-19 pandemic, which is originated from the health sector rather than the financial sector. The results provide a new perspective on U.S. extreme indicator in stock market return predictability.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"27 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1186/s40854-024-00631-5
F. Leung, M. Law, S. K. Djeng
Modeling implied volatility (IV) is important for option pricing, hedging, and risk management. Previous studies of deterministic implied volatility functions (DIVFs) propose two parameters, moneyness and time to maturity, to estimate implied volatility. Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy. The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index (RSI) covering multiple time resolutions as a factor, as momentum is often used by investors and speculators in their trading decisions, and in contrast to volatility, RSI can distinguish between bull and bear markets. To the best of our knowledge, prior studies have not included RSI as a predictive factor in modeling IV. Instead of using a simple linear regression as in previous studies, we use a machine learning regression algorithm, namely random forest, to model a nonlinear IV. Previous studies apply DVIF modeling to options on traditional financial assets, such as stock and foreign exchange markets. Here, we study options on the largest cryptocurrency, Bitcoin, which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets. Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation. Our dataset includes short-maturity options with expiry in less than six days, as well as a full range of moneyness, both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building. Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options. The nonlinear machine learning random forest algorithm also performed better than a simple linear regression. Compared to prevailing option pricing models that employ stochastic variables, our DIVF model does not include stochastic factors but exhibits reasonably good performance. It is also easy to compute due to the availability of real-time RSIs. Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.
隐含波动率（IV）建模对于期权定价、套期保值和风险管理非常重要。以往对确定性隐含波动率函数（DIVF）的研究提出了两个参数，即货币性（moneyness）和到期时间（time to maturity）来估计隐含波动率。最近的 DIVF 模型加入了移动平均比率和相对买卖价差等因素，但未能提高建模的准确性。由于投资者和投机者在做出交易决策时经常会用到动量指标，而且与波动率相比，相对强弱指数可以区分牛市和熊市，因此本研究提供了一种通用的 DIVF 模型，即使用涵盖多个时间分辨率的相对强弱指数（RSI）作为相关资产的动量指标。据我们所知，之前的研究并未将 RSI 作为预测因素纳入 IV 模型。我们没有像以前的研究那样使用简单的线性回归，而是使用了一种机器学习回归算法，即随机森林，来建立非线性 IV 模型。以往的研究将 DVIF 模型应用于股票和外汇市场等传统金融资产的期权。在这里，我们研究的是最大的加密货币比特币的期权，由于比特币的极端波动性以及对它的研究不如传统金融资产，这给建模带来了更大的挑战。为了开发和验证模型，我们从一家领先的加密货币期权交易所收集了为期四个月的最新比特币期权链数据。我们的数据集包括到期日少于六天的短期限期权和全货币性期权，这两种期权在现有研究中通常被排除在外，因为具有这些特征的期权价格通常波动很大，给模型构建带来了挑战。我们的样本内和样本外结果表明，加入我们提出的动量指标能显著提高模型对期权定价的准确性。非线性机器学习随机森林算法的表现也优于简单的线性回归。与采用随机变量的主流期权定价模型相比，我们的 DIVF 模型不包含随机因素，但表现出相当好的性能。由于可以获得实时 RSI，该模型也很容易计算。我们的研究结果表明，我们的增强型 DIVF 模型具有显著的改进，可以很好地替代以随机因素为主的现有期权定价模型。
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