Pub Date : 2020-08-21DOI: 10.1142/9789811202391_0011
Sheng-Syan Chen, Cheng-Few Lee, Keshab Shresth
{"title":"Hedge Ratio and Time Series Analysis","authors":"Sheng-Syan Chen, Cheng-Few Lee, Keshab Shresth","doi":"10.1142/9789811202391_0011","DOIUrl":"https://doi.org/10.1142/9789811202391_0011","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"10 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":"124628127","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: 10.1142/9789812701213_0003
Anthony Kozberg
AbstractThis paper utilizes path analysis, an approach common in behavioral and natural science literatures but relatively unseen in finance and accounting, to improve inferences drawn from a combined database of financial and non-financial information. Focusing on the revenue generating activities of Internet firms, this paper extends the literature on Internet valuation while addressing the potentially endogenous and multicollinear nature of the Internet activity measures applied in their tests. Results suggest that both SG&A and R&D have significant explanatory power over the web activity measures, suggestive that these expenditures represent investments in product quality. Evidence from the path analysis also indicates that both accounting and non-financial measures, in particular SG&A and pageviews, are significantly associated with firm revenues. Finally, this paper suggests other areas of accounting research which could benefit from a path analysis approach.
{"title":"Using Path Analysis to Integrate Accounting and Non-Financial Information: The Case for Revenue Drivers of Internet Stocks","authors":"Anthony Kozberg","doi":"10.1142/9789812701213_0003","DOIUrl":"https://doi.org/10.1142/9789812701213_0003","url":null,"abstract":"AbstractThis paper utilizes path analysis, an approach common in behavioral and natural science literatures but relatively unseen in finance and accounting, to improve inferences drawn from a combined database of financial and non-financial information. Focusing on the revenue generating activities of Internet firms, this paper extends the literature on Internet valuation while addressing the potentially endogenous and multicollinear nature of the Internet activity measures applied in their tests. Results suggest that both SG&A and R&D have significant explanatory power over the web activity measures, suggestive that these expenditures represent investments in product quality. Evidence from the path analysis also indicates that both accounting and non-financial measures, in particular SG&A and pageviews, are significantly associated with firm revenues. Finally, this paper suggests other areas of accounting research which could benefit from a path analysis approach.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"108 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":"124146826","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_bmatter
Cheng-Few Lee, John C. Lee
{"title":"BACK MATTER","authors":"Cheng-Few Lee, John C. Lee","doi":"10.1142/9789811202391_bmatter","DOIUrl":"https://doi.org/10.1142/9789811202391_bmatter","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"22 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":"121565727","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_fmatter01
Cheng-Few Lee, John C. Lee
{"title":"FRONT MATTER","authors":"Cheng-Few Lee, John C. Lee","doi":"10.1142/9789811202391_fmatter01","DOIUrl":"https://doi.org/10.1142/9789811202391_fmatter01","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":"133197503","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_0052
Jow-Ran Chang, Mao-Wei Hung, Cheng-Few Lee, Hsin-Min Lu
We study the heteroskedasticity and jump behavior of the Thai baht using models of the square root stochastic volatility with or without jumps. The Bayesian factor is used to evaluate the explanatory power of competing models. The results suggest that in our sample, the square root stochastic volatility model with independent jumps in the observation and state equations (SVIJ) has the best explanatory power for the 1996 Asian financial crisis. Using the estimation results of the SVIJ model, we are able to link the major events of the Asian financial crisis to jump behavior in either volatility or observation.
{"title":"The Jump Behavior of a Foreign Exchange Market: Analysis of the Thai Baht","authors":"Jow-Ran Chang, Mao-Wei Hung, Cheng-Few Lee, Hsin-Min Lu","doi":"10.1142/9789811202391_0052","DOIUrl":"https://doi.org/10.1142/9789811202391_0052","url":null,"abstract":"We study the heteroskedasticity and jump behavior of the Thai baht using models of the square root stochastic volatility with or without jumps. The Bayesian factor is used to evaluate the explanatory power of competing models. The results suggest that in our sample, the square root stochastic volatility model with independent jumps in the observation and state equations (SVIJ) has the best explanatory power for the 1996 Asian financial crisis. Using the estimation results of the SVIJ model, we are able to link the major events of the Asian financial crisis to jump behavior in either volatility or observation.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"69 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":"131038559","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_fmatter03
Cheng-Few Lee, John C. Lee
{"title":"FRONT MATTER","authors":"Cheng-Few Lee, John C. Lee","doi":"10.1142/9789811202391_fmatter03","DOIUrl":"https://doi.org/10.1142/9789811202391_fmatter03","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"79 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":"122003018","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: 10.1142/9789814343589_0018
Cheng-Few Lee, Joseph E. Finnerty, John C. Lee, Alice C. Lee, Donald H. Wort
AbstractThe following sections are included:Call and Put OptionsOne-Period Option Pricing ModelTwo-Period Option Pricing ModelUsing Microsoft Excel to Create the Binomial Option TreesBlack–Scholes Option Pricing ModelRelationship between the Binomial Option Pricing Model and the Black–Scholes Option Pricing ModelDecision Tree Black–Scholes CalculationSummaryQuestions and ProblemsAppendex 18A: Excel VBA Code — Binomial Option Pricing ModelBibliography
{"title":"Decision Tree and Microsoft Excel Approach for Option Pricing Model","authors":"Cheng-Few Lee, Joseph E. Finnerty, John C. Lee, Alice C. Lee, Donald H. Wort","doi":"10.1142/9789814343589_0018","DOIUrl":"https://doi.org/10.1142/9789814343589_0018","url":null,"abstract":"AbstractThe following sections are included:Call and Put OptionsOne-Period Option Pricing ModelTwo-Period Option Pricing ModelUsing Microsoft Excel to Create the Binomial Option TreesBlack–Scholes Option Pricing ModelRelationship between the Binomial Option Pricing Model and the Black–Scholes Option Pricing ModelDecision Tree Black–Scholes CalculationSummaryQuestions and ProblemsAppendex 18A: Excel VBA Code — Binomial Option Pricing ModelBibliography","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":"133398721","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_5
Cheng-Few Lee, Hong-Yi Chen, John C. Lee
{"title":"Econometric Approach to Financial Analysis, Planning, and Forecasting","authors":"Cheng-Few Lee, Hong-Yi Chen, John C. Lee","doi":"10.1007/978-1-4939-9429-8_5","DOIUrl":"https://doi.org/10.1007/978-1-4939-9429-8_5","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":"123554812","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 : 2019-09-01DOI: 10.1142/s0219091519500218
Huei-Wen Teng, Michael Lee
Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.
{"title":"Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default","authors":"Huei-Wen Teng, Michael Lee","doi":"10.1142/s0219091519500218","DOIUrl":"https://doi.org/10.1142/s0219091519500218","url":null,"abstract":"Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116873380","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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