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Correlation Scenarios and Correlation Stress Testing 相关场景和相关压力测试
CompSciRN: Bayesian Probability (Topic) Pub Date : 2021-07-14 DOI: 10.2139/ssrn.3886469
N. Packham, F. Woebbeking
{"title":"Correlation Scenarios and Correlation Stress Testing","authors":"N. Packham, F. Woebbeking","doi":"10.2139/ssrn.3886469","DOIUrl":"https://doi.org/10.2139/ssrn.3886469","url":null,"abstract":"We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or highest density regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.","PeriodicalId":436699,"journal":{"name":"CompSciRN: Bayesian Probability (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125754529","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
Santa Claus, UFOs, and Widespread Voter Fraud: Bayesian Gullibility and Disinformation from a High-Trust Source 圣诞老人、不明飞行物和广泛的选民欺诈:贝叶斯轻信和来自高信任来源的虚假信息
CompSciRN: Bayesian Probability (Topic) Pub Date : 2021-03-15 DOI: 10.2139/ssrn.3804086
J. B. Heaton
{"title":"Santa Claus, UFOs, and Widespread Voter Fraud: Bayesian Gullibility and Disinformation from a High-Trust Source","authors":"J. B. Heaton","doi":"10.2139/ssrn.3804086","DOIUrl":"https://doi.org/10.2139/ssrn.3804086","url":null,"abstract":"Disinformation may be an insoluble problem in many cases because belief in disinformation rests mostly on high trust in the information source and not on the plausibility of the information itself. I use a Bayesian framework to illustrate this problem and develop a simple notion of Bayesian gullibility, the ratio of the likelihood ratio to the prior odds on deception versus candor. The Bayesian framework suggests that efforts to counter disinformation should focus on a limited discrediting of the information source, since a general discrediting or a focus on the evidence in isolation is unlikely to sway the information consumer who has succumbed to a disinformation campaign by a high-trust information source.","PeriodicalId":436699,"journal":{"name":"CompSciRN: Bayesian Probability (Topic)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127185542","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
Predicting Litigation Risk via Machine Learning 通过机器学习预测诉讼风险
CompSciRN: Bayesian Probability (Topic) Pub Date : 2020-12-01 DOI: 10.2139/ssrn.3740954
G. Lee, James Naughton, Xin Zheng, Dexin Zhou
{"title":"Predicting Litigation Risk via Machine Learning","authors":"G. Lee, James Naughton, Xin Zheng, Dexin Zhou","doi":"10.2139/ssrn.3740954","DOIUrl":"https://doi.org/10.2139/ssrn.3740954","url":null,"abstract":"We demonstrate the value of machine learning in accounting through a detailed examination of litigation risk, an important and frequently used estimate in the literature. We evaluate a comprehensive set of twelve machine learning techniques and benchmark their performance against the logistic regression models in Kim and Skinner (2012). These models improve the prediction of litigation risk, with hourglass-shaped and convolutional neural networks the most effective. The improvements are substantial, and are driven by increased precision, the most salient attribute of litigation estimates in the accounting literature. We also produce firm-year litigation risk estimates for use in future research from a convolutional neural network model that uses recursive feature elimination on a pool of 68 possible parameters. Overall, our results suggest that the joint consideration of economically-meaningful predictors and machine learning techniques maximize the effectiveness of accounting estimates.","PeriodicalId":436699,"journal":{"name":"CompSciRN: Bayesian Probability (Topic)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126675167","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
The Perception of Climate Sensitivity: Revealing Priors From Posteriors 气候敏感性的感知:从后见后见
CompSciRN: Bayesian Probability (Topic) Pub Date : 2020-07-18 DOI: 10.2139/ssrn.3748883
Masako Ikefuji, J. Magnus
{"title":"The Perception of Climate Sensitivity: Revealing Priors From Posteriors","authors":"Masako Ikefuji, J. Magnus","doi":"10.2139/ssrn.3748883","DOIUrl":"https://doi.org/10.2139/ssrn.3748883","url":null,"abstract":"A Bayesian typically uses data and a prior to produce a posterior. In practice, the data and the posterior are often observed but not the prior. We shall follow the opposite route, using data and the posterior information to reveal the prior. We then apply this theory to (equilibrium) climate sensitivity as reported by the Intergovernmental Panel on Climate Change in an attempt to get some insight into their prior beliefs. It appears that the IPCC scientists have agreed a priori on a value for the climate equilibrium between 3 and 4 degrees Celsius, while judging the occurrence of a real disaster much more likely than the previous report predicts.","PeriodicalId":436699,"journal":{"name":"CompSciRN: Bayesian Probability (Topic)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126790572","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
Type S Errors in Multi-Armed Bandits 多武装强盗中的S型错误
CompSciRN: Bayesian Probability (Topic) Pub Date : 2019-10-01 DOI: 10.2139/ssrn.3464959
Markus Loecher
{"title":"Type S Errors in Multi-Armed Bandits","authors":"Markus Loecher","doi":"10.2139/ssrn.3464959","DOIUrl":"https://doi.org/10.2139/ssrn.3464959","url":null,"abstract":"A standard method to evaluate new features and changes to e.g. websites is A/B testing. A common pitfall in performing A/B testing is the habit of looking at a test while it’s running, then stopping early. Due to the implicit multiple testing, the p-values are no longer trustworthy and usually overly optimistic. We investigate the claim that Bayesian methods, unlike frequentist tests, are immune to this “peeking” problem. We demonstrate that two frequently used measures, namely posterior probability and value remaining, are severely affected by repeated testing. We further show a strong dependence on the prior probability of the parameters of interest.","PeriodicalId":436699,"journal":{"name":"CompSciRN: Bayesian Probability (Topic)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115441111","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
Broad- Versus Narrow-Sample Evidence in Disclosure Regulation Studies: A Discussion of Badia, Duro, Jorgensen, and Ormazabal (2018) 信息披露法规研究中的宽样本与窄样本证据:对Badia、Duro、Jorgensen和Ormazabal(2018)的讨论
CompSciRN: Bayesian Probability (Topic) Pub Date : 2019-08-05 DOI: 10.2139/ssrn.3432676
Hans B. Christensen
{"title":"Broad- Versus Narrow-Sample Evidence in Disclosure Regulation Studies: A Discussion of Badia, Duro, Jorgensen, and Ormazabal (2018)","authors":"Hans B. Christensen","doi":"10.2139/ssrn.3432676","DOIUrl":"https://doi.org/10.2139/ssrn.3432676","url":null,"abstract":"Broad-sample evidence that examines the effect of disclosure regulation is widespread in accounting research — often justified by its greater generalizability relative to narrow-sample evidence. Badia et al. (this issue) takes a different approach by focusing on the effect of disclosure rules in a narrow sample of U.S. and Canadian oil and gas companies. In this discussion, I argue that the contention that broad samples make research on disclosure regulation generalizable is a misconception and that the narrow-sample approach has many advantages and often advances knowledge more than broad-sample research. The key advantage of a narrow sample is that, everything else equal, we generally understand the institutional setting that generated the evidence better. Institutional details allow researchers to more precisely identify the changes that the regulation caused, assess the validity of untestable identification assumptions, and, as a field, it improves our ability to take a Bayesian approach to causal inferences. I illustrate these points by contrasting the evidence in Badia et al. to evidence from broad-sample studies on the effect of IFRS adoption and other major disclosure regulation.","PeriodicalId":436699,"journal":{"name":"CompSciRN: Bayesian Probability (Topic)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122113027","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: 7
Machine Learning in Asset Management 资产管理中的机器学习
CompSciRN: Bayesian Probability (Topic) Pub Date : 2019-07-16 DOI: 10.2139/ssrn.3420952
Derek Snow
{"title":"Machine Learning in Asset Management","authors":"Derek Snow","doi":"10.2139/ssrn.3420952","DOIUrl":"https://doi.org/10.2139/ssrn.3420952","url":null,"abstract":"This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where appropriate. The hope is that this informal paper will organically grow with future developments in machine learning and data processing techniques. Changes can be tracked on the GitHub repository. This draft paper has been repackaged for the Journal of Financial Data Science.","PeriodicalId":436699,"journal":{"name":"CompSciRN: Bayesian Probability (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121280362","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: 3
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