Quantitative Corporate Finance
Autor John B. Guerard Jr., Anureet Saxena, Mustafa Gultekinen Limba Engleză Hardback – 21 noi 2020
Now in its second edition, the book covers a wide range of topics in corporate finance, from time series modeling and regression analysis to multi-factor risk models and the Capital Asset Pricing Model. Guerard, Gultekin and Saxena build significantly on the first edition of the text, but retain the core chapters on cornerstone topics such as mergers and acquisitions, regulatory environments, bankruptcy and various other foundational concepts of corporate finance.
New to the second edition are examinations of APT portfolio selection and time series modeling and forecasting through SAS, SCA and OxMetrics programming, FactSet fundamental data templates. This is intended to be a graduate-level textbook, and could be used as a primary text in upper level MBA and Financial Engineering courses, as well as a supplementary text for graduate courses in financial data analysis and financial investments.
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Specificații
ISBN-13: 9783030435462
ISBN-10: 3030435466
Pagini: 606
Ilustrații: XXI, 611 p. 42 illus., 28 illus. in color.
Dimensiuni: 155 x 235 x 43 mm
Greutate: 1.06 kg
Ediția:2nd ed. 2021
Editura: Springer
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030435466
Pagini: 606
Ilustrații: XXI, 611 p. 42 illus., 28 illus. in color.
Dimensiuni: 155 x 235 x 43 mm
Greutate: 1.06 kg
Ediția:2nd ed. 2021
Editura: Springer
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Chapter 1. Introduction: Capital Formation, Risk, and the Corporation.- Chapter 2. The Corporation and Other Forms of Business Organization.- Chapter 3. The Corporation Balance Sheet.- Chapter 4. The Annual Operating Statements: The Income Statement and Cash Flow Statement.- Chapter 5. Financing Current Operations and Efficiency Ratio Analysis.- Chapter 6. Financing Current Operations and the Cash Budget.- Chapter 7. Capital and New Issue Markets.- Chapter 8. The Equity of the Corporation: Common and Preferred Stock.- Chapter 9. Long-Term Debt.- Chapter 10. Debt, Equity, the Optimal Financial Structure and the Cost of Funds.- Chapter 11. Investing in Assets: Theory of Investment Decision Making.- Chapter 12. Regression Analysis and Estimating Regression Models.- Chapter 13. Time Series Modeling and the Forecasting Effectiveness of the U.S. Leading Economic Indicators.- Chapter 14. Risk and Return of Equity and the Capital Asset Pricing Model.- Chapter 15. Multi-Factor Risk Models and Portfolio Construction and Management.- Chapter 16. Options.- Chapter 17. Real Options.- Chapter 18. Mergers and Acquisitions.- Chapter 19. Liquidation, Failure, Bankruptcy, and Reorganization.- Chapter 20. Corporation Growth and Economic Growth and Stability.- Chapter 21. International Business Finance.- Chapter 22. Management-Stockholder Relations: Is Optimal Behavior All that is Necessary?.
Notă biografică
John B. Guerard, Jr., Ph.D. is Director of Quantitative Research at McKinley Capital Management, in Anchorage, Alaska. Dr. Guerard has published several monographs, including Corporate Financial Policy and R&D Management (Wiley, 2006, second edition), Quantitative Corporate Finance (Springer, 2007, with Eli Schwartz, second edition in preparation), The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques (Springer, 2010), and Introduction to Financial Forecasting in Investment Analysis (Springer, 2013), and Portfolio and Investment Analysis using SAS (SAS Press, 2019, with Ganlin Xu). John serves an Associate Editor of the Journal of Investing and The International Journal of Forecasting. Dr. Guerard has published research in The International Journal of Forecasting, Management Science, the Journal of Forecasting, Journal of Investing, Research in Finance, the IBM Journal of Research and Development, Research Policy, and the Journal of the Operational Research Society.
Anureet Saxena, Ph.D. in Operations Research, Carnegie Mellon, with publications in Mathematical Programming, the Journal of Portfolio Management, Journal of Investing, and Frontiers in Applied Mathematical and Statistics.
Mustafa Gultekin, Ph.D. in Finance, New York University, has published in the Journal of Finance, Journal of Financial Economics, the Journal of Financial and Quantitative Analysis, Management Science, and Research in Finance.
Textul de pe ultima copertă
This textbook presents a comprehensive treatment of the legal arrangement of the corporation, the instruments and institutions through which capital can be raised, the management of the flow of funds through the individual firm, and the methods of dividing the risks and returns among the various contributors of funds.
Now in its second edition, the book covers a wide range of topics in corporate finance, from time series modeling and regression analysis to multi-factor risk models and the Capital Asset Pricing Model. Guerard, Gultekin and Saxena build significantly on the first edition of the text, but retain the core chapters on cornerstone topics such as mergers and acquisitions, regulatory environments, bankruptcy and various other foundational concepts of corporate finance.
New to the second edition are examinations of APT portfolio selection and time series modeling and forecasting through SAS, SCA and OxMetrics programming, FactSet fundamental data templates. This is intended to be a graduate-level textbook, and could be used as a primary text in upper level MBA and Financial Engineering courses, as well as a supplementary text for graduate courses in financial data analysis and financial investments.
Caracteristici
Provides new instruction and empirical evidence portfolio selection, including commercially-available statistically-based systems, such as APT and Axioma
Examines stock beta estimations and stock selection modeling using robust regression with SAS
Offers enhanced time series modeling and forecasting using SAS, SCA and OxMetrics
Reports company data and ratios using FactSet fundamental data templates
Examines stock beta estimations and stock selection modeling using robust regression with SAS
Offers enhanced time series modeling and forecasting using SAS, SCA and OxMetrics
Reports company data and ratios using FactSet fundamental data templates