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Forecast Performance of the Taiwan Weighted Stock Index: Update and Expansion 台湾加权股票指数之预测表现:更新与扩充
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_0006
Deng Ji, Hsiao-yin Chen, Cheng-Few Lee
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引用次数: 0
A Time-Series Bootstrapping Simulation Method to Distinguish Sell-Side Analysts’ Skill from Luck 一种区分卖方分析师技能与运气的时间序列自举模拟方法
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_0055
C. Su, Hanxiong Zhang
{"title":"A Time-Series Bootstrapping Simulation Method to Distinguish Sell-Side Analysts’ Skill from Luck","authors":"C. Su, Hanxiong Zhang","doi":"10.1142/9789811202391_0055","DOIUrl":"https://doi.org/10.1142/9789811202391_0055","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127672042","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: 4
Alternative Methods to Deal with Measurement Error 处理测量误差的替代方法
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1007/978-1-4939-9429-8_7
Cheng-Few Lee, Hong-Yi Chen, John C. Lee
{"title":"Alternative Methods to Deal with Measurement Error","authors":"Cheng-Few Lee, Hong-Yi Chen, John C. Lee","doi":"10.1007/978-1-4939-9429-8_7","DOIUrl":"https://doi.org/10.1007/978-1-4939-9429-8_7","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125739570","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
Product Market Competition and CEO Pay Benchmarking 产品市场竞争与CEO薪酬基准
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_0045
Ivan E. Brick, Darius Palia
{"title":"Product Market Competition and CEO Pay Benchmarking","authors":"Ivan E. Brick, Darius Palia","doi":"10.1142/9789811202391_0045","DOIUrl":"https://doi.org/10.1142/9789811202391_0045","url":null,"abstract":"This chapter examines the impact of product market competition on the benchmarking of a CEO’s compensation to their counterparts in peer companies. Using a large sample of US firms, we find a significantly greater effect of CEO pay benchmarking in more competitive industries than in less competitive industries. Using three proxies for managerial talent that have been used by Albuquerque et al. (2013), we find that CEO benchmarking is more pronounced in competitive markets wherein managerial talent is more valuable. This suggests that pay benchmarking and product market competition are complements. The above results are not due to industry homogeneity.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128712997","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
Large-Sample Theory 大样本理论
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_0115
Sunil S. Poshakwale, Anandadeep Mandal
{"title":"Large-Sample Theory","authors":"Sunil S. Poshakwale, Anandadeep Mandal","doi":"10.1142/9789811202391_0115","DOIUrl":"https://doi.org/10.1142/9789811202391_0115","url":null,"abstract":"In this chapter, we discuss large sample theory that can be applied under conditions that are quite likely to be met in large samples even when the Gauss–Markov conditions are broken. There are two reasons for using large sample theory. First, there may be some problems that corrupt our estimators in small samples but tends to diminish down as the sample gets bigger. Thus, if we cannot get a perfect small sample estimator, we will usually want to choose the one that will be best in large samples. Second, in some circumstances, the theory used to derive the properties of estimators in small samples just does not work, and working out the properties of the estimators can be impossible. This makes it very hard to choose between alternative estimators. In these circumstances we judge different estimators on their “large sample properties” because their “small (or finite) sample properties” are unknown.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130175419","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
Evolution Strategy-Based Adaptive Lq Penalty Support Vector Machines with Gauss Kernel for Credit Risk Analysis 基于进化策略的高斯核自适应Lq惩罚支持向量机信用风险分析
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_0044
Jianping Li, Gang Li, Dongxia Sun, Cheng-Few Lee
{"title":"Evolution Strategy-Based Adaptive Lq Penalty Support Vector Machines with Gauss Kernel for Credit Risk Analysis","authors":"Jianping Li, Gang Li, Dongxia Sun, Cheng-Few Lee","doi":"10.1142/9789811202391_0044","DOIUrl":"https://doi.org/10.1142/9789811202391_0044","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124924753","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
FRONT MATTER 前页
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_fmatter04
Cheng-Few Lee, John C. Lee
{"title":"FRONT MATTER","authors":"Cheng-Few Lee, John C. Lee","doi":"10.1142/9789811202391_fmatter04","DOIUrl":"https://doi.org/10.1142/9789811202391_fmatter04","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124052541","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
FRONT MATTER 前页
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_fmatter02
Cheng-Few Lee, John C. Lee
{"title":"FRONT MATTER","authors":"Cheng-Few Lee, John C. Lee","doi":"10.1142/9789811202391_fmatter02","DOIUrl":"https://doi.org/10.1142/9789811202391_fmatter02","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121819482","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
Itô’s Calculus and the Derivation of the Black–Scholes Option-Pricing Model Itô的微积分及Black-Scholes期权定价模型的推导
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_0027
George Chalamandaris, A. Malliaris
{"title":"Itô’s Calculus and the Derivation of the Black–Scholes Option-Pricing Model","authors":"George Chalamandaris, A. Malliaris","doi":"10.1142/9789811202391_0027","DOIUrl":"https://doi.org/10.1142/9789811202391_0027","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121024305","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
The Effects of the Sample Size, the Investment Horizon and the Market Conditions on the Validity of Composite Performance Measures: A Generalization 样本规模、投资期限和市场条件对综合绩效指标有效性的影响:一个概括
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning Pub Date : 2020-08-21 DOI: 10.1142/9789811202391_0068
Sonnan Chen, Cheng-Few Lee
{"title":"The Effects of the Sample Size, the Investment Horizon and the Market Conditions on the Validity of Composite Performance Measures: A Generalization","authors":"Sonnan Chen, Cheng-Few Lee","doi":"10.1142/9789811202391_0068","DOIUrl":"https://doi.org/10.1142/9789811202391_0068","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114227691","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
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