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Journal of Finance and Data Science最新文献

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The inaugural Journal of Finance and Data Science Conference was held successfully in Beijing 首届《金融与数据科学杂志》大会在北京成功举办
Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2024-02-03 DOI: 10.1016/j.jfds.2024.100119
{"title":"The inaugural Journal of Finance and Data Science Conference was held successfully in Beijing","authors":"","doi":"10.1016/j.jfds.2024.100119","DOIUrl":"https://doi.org/10.1016/j.jfds.2024.100119","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000047/pdfft?md5=4984821b801a870625893c3a19f9f174&pid=1-s2.0-S2405918824000047-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139737164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets 使用单元稳健关联度量和聚类方法的投资组合优化，并应用于高度波动的市场
Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-04-19 DOI: 10.1016/j.jfds.2023.100097
Emmanuel Jordy Menvouta , Sven Serneels , Tim Verdonck
{"title":"Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets","authors":"Emmanuel Jordy Menvouta ,&nbsp;Sven Serneels ,&nbsp;Tim Verdonck","doi":"10.1016/j.jfds.2023.100097","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100097","url":null,"abstract":"<div><p>This paper introduces the minCluster portfolio, which is a portfolio optimization method combining the optimization of downside risk measures, hierarchical clustering and cellwise robustness. Using cellwise robust association measures, the minCluster portfolio is able to retrieve the underlying hierarchical structure in the data. Furthermore, it provides downside protection by using tail risk measures for portfolio optimization. We show through simulation studies and a real data example that the minCluster portfolio produces better out-of-sample results than mean-variances or other hierarchical clustering based approaches. Cellwise outlier robustness makes the minCluster method particularly suitable for stable optimization of portfolios in highly volatile markets, such as portfolios containing cryptocurrencies.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100097"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49874017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
What do we learn from stock price reactions to China's first announcement of anti-corruption reforms? 我们从中国首次宣布反腐改革后的股价反应中学到了什么?
Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-03-04 DOI: 10.1016/j.jfds.2023.100096
Chen Lin , Randall Morck , Bernard Yeung , Xiaofeng Zhao
{"title":"What do we learn from stock price reactions to China's first announcement of anti-corruption reforms?","authors":"Chen Lin ,&nbsp;Randall Morck ,&nbsp;Bernard Yeung ,&nbsp;Xiaofeng Zhao","doi":"10.1016/j.jfds.2023.100096","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100096","url":null,"abstract":"<div><p>China's markets gained 3.86% around December 4, 2012, when the Party announced anti-corruption reforms. State-owned enterprises (SOEs) with higher past entertainment and travel costs (<em>ETC</em>) gained more. NonSOEs gained in more liberalized provinces, especially those with high past <em>ETC</em>, productivity, growth opportunities, and external financing. NonSOEs lost in the least liberalized provinces, especially those with high past <em>ETC</em>. These findings support investors' expect reduced official corruption to create value overall, reduce SOE waste, lower bureaucratic barriers to efficient resource allocation where markets function, and impede business in unliberalized provinces, where “getting things done” still requires investment in greasing bureaucratic gears.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100096"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49874018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Does one size fit all? Comparing the determinants of the FinTech market segments expansion 一个尺码适合所有人吗?比较金融科技细分市场扩张的决定因素
Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-01-12 DOI: 10.1016/j.jfds.2023.01.002
Mikhail Stolbov , Maria Shchepeleva
{"title":"Does one size fit all? Comparing the determinants of the FinTech market segments expansion","authors":"Mikhail Stolbov ,&nbsp;Maria Shchepeleva","doi":"10.1016/j.jfds.2023.01.002","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.01.002","url":null,"abstract":"<div><p>The paper aims to indentify and compare the determinants of the overall FinTech market expansion and its major segments – cryptocurrency and peer-to-peer lending markets – in a dataset, which covers 64 countries and 51 potentially relevant factors. To this end, we apply a battery of state-of-the-art variable selection techniques from machine learning, comprising Bayesian model averaging (BMA), least absolute shrinkage and selection operator (LASSO), variable selection using random forests (VSURF) as well as spike-and-slab regression. We document substantial heterogeneity of the pivotal determinants across the FinTech market as a whole and its major segments. Thus, specific rather than general policy measures are needed to foster the development of standalone FinTech market segments. Moreover, our findings suggest that most countries don't need to seek a universal specialization in FinTech activities, concentrating on the segment where they have a competitive edge in terms of the pivotal determinants which drive its expansion.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49874019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Expert aggregation for financial forecasting 专家汇总财务预测
Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-11-27 DOI: 10.1016/j.jfds.2023.100108
Carl Remlinger , Clémence Alasseur , Marie Brière , Joseph Mikael
{"title":"Expert aggregation for financial forecasting","authors":"Carl Remlinger ,&nbsp;Clémence Alasseur ,&nbsp;Marie Brière ,&nbsp;Joseph Mikael","doi":"10.1016/j.jfds.2023.100108","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100108","url":null,"abstract":"<div><p>Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of models in a single approach without making any assumption about the models. In this paper, a Bernstein Online Aggregation (BOA) procedure is applied to the construction of long-short strategies built from individual stock return forecasts coming from different machine learning models. The online mixture of experts leads to attractive portfolio performances even in non-stationary environments. The inclusion of neural networks experts in the aggregation contributes to a better average return, while Ordinary Least Squares with Huber Loss experts contribute to lower risk. The aggregation outperforms individual algorithms, offering a higher portfolio Sharpe ratio, lower shortfall, with a similar turnover. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918823000247/pdfft?md5=65d3b507a8c88e3a7a8f29685f31fdb9&pid=1-s2.0-S2405918823000247-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139099853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The great wall of debt: Real estate, political risk, and Chinese local government financing cost 债务长城:房地产、政治风险和中国地方政府融资成本
Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-04-25 DOI: 10.1016/j.jfds.2023.100098
Andrew Ang , Jennie Bai , Hao Zhou
{"title":"The great wall of debt: Real estate, political risk, and Chinese local government financing cost","authors":"Andrew Ang ,&nbsp;Jennie Bai ,&nbsp;Hao Zhou","doi":"10.1016/j.jfds.2023.100098","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100098","url":null,"abstract":"<div><p>Chengtou bond is the only asset with market prices that can capture the funding cost of Chinese local government debt. In contrast to the U.S. municipal bonds, Chengtou bonds are issued by private corporations but implicitly guaranteed by local and the central governments, which are reflected by novel risk characteristics—real estate GDP and political risk. One standard deviation increase in local real estate GDP (political risk) corresponds to 10 (9) basis points decrease (increase) in bond yields, respectively. However, conditional on political risk, real estate GDP actually increases bond yields, suggesting that only local governments with low political risk can enjoy the low funding costs driven by high real estate growth.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49874016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Stock pledged loans and market crash risk: Evidence from China 股票质押贷款和市场崩溃风险:来自中国的证据
Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-09-09 DOI: 10.1016/j.jfds.2023.100104
Feng Li , Jun Qian , Haofei Wang , Julie Lei Zhu
{"title":"Stock pledged loans and market crash risk: Evidence from China","authors":"Feng Li ,&nbsp;Jun Qian ,&nbsp;Haofei Wang ,&nbsp;Julie Lei Zhu","doi":"10.1016/j.jfds.2023.100104","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100104","url":null,"abstract":"<div><p>Stock pledged loans have become prevalent among large shareholders of listed firms in China. The <em>largest</em> shareholder pledges a greater fraction of her holdings as collateral for credit when the firm is in growth industries, less profitable, <em>not</em> state owned, and has higher leverage. Stock performance of highly pledged firms is indistinguishable from that of firms with low pledge ratios in 2017. During 2018, however, highly pledged firms have worse stock returns and operating performance, and experienced ‘contagion’ – the crash risk of one highly pledged stock spreading to others. Using a regulatory reform in 2013 that allowed securities companies to provide stock pledged loans, we find that obtaining these personal loans had <em>no</em> adverse effects on the firms when the pledge ratio was low. Overall, forced sales of pledged stocks and worsened agency conflict are responsible for the poor performance of highly pledged firms during the 2018 bear market.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240591882300020X/pdfft?md5=be8ce95c83d9d2d0e69e97a4f0bfc370&pid=1-s2.0-S240591882300020X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92046209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sustainable investing and the cross-section of returns and maximum drawdown 可持续投资和回报的横截面和最大的缩减
Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-12-06 DOI: 10.1016/j.jfds.2022.11.002
Lisa R. Goldberg , Saad Mouti
{"title":"Sustainable investing and the cross-section of returns and maximum drawdown","authors":"Lisa R. Goldberg ,&nbsp;Saad Mouti","doi":"10.1016/j.jfds.2022.11.002","DOIUrl":"10.1016/j.jfds.2022.11.002","url":null,"abstract":"<div><p>We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and neural networks. We find that the most important predictors tended to be consistent across models, and that non-linear models had better predictive power than linear models. Predictive power was higher in calm periods than in stressed periods. Environmental, social, and governance indicators marginally impacted the predictive power of non-linear models in our data, despite their negative correlation with maximum drawdown and positive correlation with returns. Upon exploring whether ESG variables are captured by some models, we find that ESG data contribute to the prediction nonetheless.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages 353-387"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918822000150/pdfft?md5=f4705e80b3e149e5335f70f6854e6a3e&pid=1-s2.0-S2405918822000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125212694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Persistence in factor-based supervised learning models 基于因素的监督学习模型的持久性
Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2021-11-10 DOI: 10.1016/j.jfds.2021.10.002
Guillaume Coqueret
{"title":"Persistence in factor-based supervised learning models","authors":"Guillaume Coqueret","doi":"10.1016/j.jfds.2021.10.002","DOIUrl":"https://doi.org/10.1016/j.jfds.2021.10.002","url":null,"abstract":"<div><p>In this paper, we document the importance of <em>memory</em> in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we report that persistent features play a prominent role in these models. When applied to portfolio choice, we find that investors are always better off predicting <em>annual</em> returns, even when rebalancing at lower frequencies (monthly or quarterly). Our results remain robust to transaction costs and risk scaling, thus providing useful indications to quantitative asset managers.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages 12-34"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918821000143/pdfft?md5=9bc8449bca65c4e4f6c987a143626342&pid=1-s2.0-S2405918821000143-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136838422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Accounting in an age of big data 大数据时代的会计
Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2023-02-18 DOI: 10.1016/j.jfds.2023.01.001
Kai Du
{"title":"Accounting in an age of big data","authors":"Kai Du","doi":"10.1016/j.jfds.2023.01.001","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.01.001","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages A1-A2"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918823000016/pdfft?md5=0427f2e224bfd7770e91acf9a3afe412&pid=1-s2.0-S2405918823000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92078385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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