Journal of Finance and Data Science最新文献_第8页

Vine copula based dependence modeling in sustainable finance 基于Vine copula的可持续金融依赖模型

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-11-17 DOI: 10.1016/j.jfds.2022.11.003

Claudia Czado , Karoline Bax , Özge Sahin , Thomas Nagler , Aleksey Min , Sandra Paterlini

{"title":"Vine copula based dependence modeling in sustainable finance","authors":"Claudia Czado , Karoline Bax , Özge Sahin , Thomas Nagler , Aleksey Min , Sandra Paterlini","doi":"10.1016/j.jfds.2022.11.003","DOIUrl":"https://doi.org/10.1016/j.jfds.2022.11.003","url":null,"abstract":"<div><p>Climate change and sustainability have become societal focal points in the last decade. Consequently, companies have been increasingly characterized by non-financial information, such as environmental, social, and governance (ESG) scores, based on which companies can be grouped into ESG classes. While many scholars have questioned the relationship between financial performance and risks of assets belonging to different ESG classes, the question about dependence among ESG classes is still open. Here, we focus on understanding the dependence structures of different ESG class indices and the market index through the lens of copula models. After a thorough introduction to vine copula models, we explain how cross-sectional and temporal dependencies can be captured by models based on vine copulas, more specifically, using ARMA-GARCH and stationary vine copula models. Using real-world ESG data over a long period with different economic states, we find that assets with medium ESG scores tend to show weaker dependence to the market, while assets with extremely high or low ESG scores tend to show stronger, non-Gaussian dependence.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages 309-330"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918822000162/pdfft?md5=71b8f6c64ad7740fa01252911013727d&pid=1-s2.0-S2405918822000162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92078386","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

Big data, accounting information, and valuation 大数据、会计信息、估值

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-04-20 DOI: 10.1016/j.jfds.2022.04.003

Doron Nissim

引用次数: 0

Forecasting earnings and returns: A review of recent advancements 预测收益和回报:回顾最近的进展

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-05-16 DOI: 10.1016/j.jfds.2022.04.004

Jeremiah Green , Wanjia Zhao

引用次数: 5

Credit scoring methods: Latest trends and points to consider 信用评分方法:要考虑的最新趋势和要点

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-08-07 DOI: 10.1016/j.jfds.2022.07.002

Anton Markov, Zinaida Seleznyova, Victor Lapshin

引用次数: 13

Trading the FX volatility risk premium with machine learning and alternative data 使用机器学习和替代数据交易外汇波动风险溢价

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-07-15 DOI: 10.1016/j.jfds.2022.07.001

Thomas Dierckx , Jesse Davis , Wim Schoutens

{"title":"Trading the FX volatility risk premium with machine learning and alternative data","authors":"Thomas Dierckx , Jesse Davis , Wim Schoutens","doi":"10.1016/j.jfds.2022.07.001","DOIUrl":"https://doi.org/10.1016/j.jfds.2022.07.001","url":null,"abstract":"<div><p>In this study, we show how both machine learning and alternative data can be successfully leveraged to improve and develop trading strategies. Starting from a trading strategy that harvests the EUR/USD volatility risk premium by selling one-week straddles every weekday, we present a machine learning approach to more skillfully time new trades and thus prevent unfavorable ones. To this end, we build probability-calibrated Random Forests on various predictors, extracted from both traditional market data and financial news, to predict the closing Sharpe ratio of short one-week delta-hedged straddles. We then demonstrate how the output of these calibrated machine learning models can be used to engineer intuitive new trading strategies. Ultimately, we show that our proposed strategies outperform the original strategy on risk-based performance measures. Moreover, the features that we derived from financial news articles significantly improve the performance of the approach.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages 162-179"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918822000083/pdfft?md5=b58dced6034ed9a3c7acc83ec4f3a4fd&pid=1-s2.0-S2405918822000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92057697","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

Measuring tail risks 衡量尾部风险

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-11-06 DOI: 10.1016/j.jfds.2022.11.001

Kan Chen , Tuoyuan Cheng

{"title":"Measuring tail risks","authors":"Kan Chen , Tuoyuan Cheng","doi":"10.1016/j.jfds.2022.11.001","DOIUrl":"10.1016/j.jfds.2022.11.001","url":null,"abstract":"<div><p>Value-at-Risk (VaR) and Expected Shortfall (ES) are common high quantile-based risk measures adopted in financial regulations and risk management. In this paper, we propose a tail risk measure based on the most probable maximum size of risk events (MPMR) that can occur over a length of time. MPMR underscores the dependence of the tail risk on the risk management time frame. Unlike VaR and ES, MPMR does not require specifying a confidence level. We derive the risk measure analytically for several well-known distributions. In particular, for the case where the size of the risk event follows a power law or Pareto distribution, we show that MPMR also scales with the number of observations <em>n</em> (or equivalently the length of the time interval) by a power law, MPMR(<em>n</em>) ∝ <em>n</em><sup><em>η</em></sup>, where <em>η</em> is the scaling exponent (SE). The scale invariance allows for reasonable estimations of long-term risks based on the extrapolation of more reliable estimations of short-term risks. The scaling relationship also gives rise to a robust and low-bias estimator of the tail index (TI) <em>ξ</em> of the size distribution, <em>ξ</em> = 1/<em>η</em>. We demonstrate the use of this risk measure for describing the tail risks in financial markets as well as the risks associated with natural hazards (earthquakes, tsunamis, and excessive rainfall).</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages 296-308"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918822000149/pdfft?md5=22762ee9804242ec67f2a03b85dba7c0&pid=1-s2.0-S2405918822000149-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80981564","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: 2

A causal approach to test empirical capital structure regularities 实证资本结构规律检验的因果方法

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-09-16 DOI: 10.1016/j.jfds.2022.09.002

Simone Cenci , Stephen Kealhofer

引用次数: 0

Term structure of interest rates with short-run and long-run risks 具有短期和长期风险的利率期限结构

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-09-22 DOI: 10.1016/j.jfds.2022.09.001

Olesya V. Grishchenko , Zhaogang Song , Hao Zhou

引用次数: 0

Machine learning for cryptocurrency market prediction and trading 加密货币市场预测和交易的机器学习

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-12-14 DOI: 10.1016/j.jfds.2022.12.001

Patrick Jaquart, Sven Köpke, Christof Weinhardt

{"title":"Machine learning for cryptocurrency market prediction and trading","authors":"Patrick Jaquart, Sven Köpke, Christof Weinhardt","doi":"10.1016/j.jfds.2022.12.001","DOIUrl":"https://doi.org/10.1016/j.jfds.2022.12.001","url":null,"abstract":"<div><p>We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest cryptocurrencies. Our results show that all employed models make statistically viable predictions, whereby the average accuracy values calculated on all cryptocurrencies range from 52.9% to 54.1%. These accuracy values increase to a range from 57.5% to 59.5% when calculated on the subset of predictions with the 10% highest model confidences per class and day. We find that a long-short portfolio strategy based on the predictions of the employed LSTM and GRU ensemble models yields an annualized out-of-sample Sharpe ratio after transaction costs of 3.23 and 3.12, respectively. In comparison, the buy-and-hold benchmark market portfolio strategy only yields a Sharpe ratio of 1.33. These results indicate a challenge to weak form cryptocurrency market efficiency, albeit the influence of certain limits to arbitrage cannot be entirely ruled out.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages 331-352"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918822000174/pdfft?md5=cf49eff9de16cb4b7a287b73a7b86b12&pid=1-s2.0-S2405918822000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92078387","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: 4

A new measure of corporate bond liquidity using survival analysis 用生存分析方法衡量公司债券流动性的新方法

Journal of Finance and Data Science Pub Date : 2022-11-01 Epub Date: 2022-05-13 DOI: 10.1016/j.jfds.2022.05.001

Kaihua Cai , Peter Yesley

引用次数: 0