Pub Date : 2020-08-21DOI: 10.1142/9789811202391_0006
Deng Ji, Hsiao-yin Chen, Cheng-Few Lee
{"title":"Forecast Performance of the Taiwan Weighted Stock Index: Update and Expansion","authors":"Deng Ji, Hsiao-yin Chen, Cheng-Few Lee","doi":"10.1142/9789811202391_0006","DOIUrl":"https://doi.org/10.1142/9789811202391_0006","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"39 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":"127112312","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}
Pub Date : 2020-08-21DOI: 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}
Pub Date : 2020-08-21DOI: 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}
Pub Date : 2020-08-21DOI: 10.1142/9789811202391_0045
Ivan E. Brick, Darius Palia
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.
{"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}
Pub Date : 2020-08-21DOI: 10.1142/9789811202391_0115
Sunil S. Poshakwale, Anandadeep Mandal
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.
{"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}
Pub Date : 2020-08-21DOI: 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}
Pub Date : 2020-08-21DOI: 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}
Pub Date : 2020-08-21DOI: 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}
Pub Date : 2020-08-21DOI: 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}
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