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Essentials of financial economicsa hands-on approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essentials of financial economicsby Michael Donadelli, Michele Costola, Ivan Gufler.
其他題名:	a hands-on approach /
作者:	Donadelli, Michael.
其他作者:	Costola, Michele.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xxi, 243 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Finance.
電子資源:	https://doi.org/10.1007/978-3-031-86189-5
ISBN:	9783031861895$q(electronic bk.)

Essentials of financial economicsa hands-on approach /
Donadelli, Michael.

Essentials of financial economicsa hands-on approach /[electronic resource] :by Michael Donadelli, Michele Costola, Ivan Gufler. - Cham :Springer Nature Switzerland :2025. - xxi, 243 p. :ill. (chiefly color), digital ;24 cm. - Springer texts in business and economics,2192-4341. - Springer texts in business and economics..

Choice under Uncertainty -- Modern Portfolio Theory -- The Capital Asset Pricing Model -- Empirical Analysis of the CAPM -- The Consumption CAPM -- Arbitrage Pricing Theory and Multi-factor Models -- Empirical Cross-Sectional Asset Pricing -- The Black-Litterman Model -- Event-Study Analysis.

This textbook offers a comprehensive guide to key topics in financial economics, seamlessly blending theoretical insights with practical applications. It covers essential areas such as portfolio allocation, asset pricing, empirical finance, and behavioral finance, providing students with a solid conceptual foundation through a combination of theory and real-world examples. Core topics include mean-variance portfolio theory, linear factor models for asset pricing, consumption-based asset pricing, the Black-Litterman asset allocation model, empirical cross-sectional asset pricing, and event studies. With a strong emphasis on hands-on implementation, the book integrates programming languages such as MATLAB, Python, Julia, and R, enabling students to apply financial models effectively. The book begins with a concise and standard review of decision-making under uncertainty, gradually advancing to topics such as intertemporal consumption choices and their impact on asset prices, before concluding with empirical tools for capturing market sentiment. By bridging fundamental and advanced finance concepts, it equips students with the necessary tools to navigate the financial landscape. Theoretical models are presented with transparency, avoiding the "black box" issue by clearly explaining mathematical derivations. This structured approach enhances learning and empowers students to utilize the provided code for key financial tasks, including portfolio management, risk analysis, and market sentiment analysis.

ISBN: 9783031861895$q(electronic bk.)

Standard No.: 10.1007/978-3-031-86189-5doiSubjects--Topical Terms:

183252
Finance.

LC Class. No.: HG173

Dewey Class. No.: 332

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520 $a This textbook offers a comprehensive guide to key topics in financial economics, seamlessly blending theoretical insights with practical applications. It covers essential areas such as portfolio allocation, asset pricing, empirical finance, and behavioral finance, providing students with a solid conceptual foundation through a combination of theory and real-world examples. Core topics include mean-variance portfolio theory, linear factor models for asset pricing, consumption-based asset pricing, the Black-Litterman asset allocation model, empirical cross-sectional asset pricing, and event studies. With a strong emphasis on hands-on implementation, the book integrates programming languages such as MATLAB, Python, Julia, and R, enabling students to apply financial models effectively. The book begins with a concise and standard review of decision-making under uncertainty, gradually advancing to topics such as intertemporal consumption choices and their impact on asset prices, before concluding with empirical tools for capturing market sentiment. By bridging fundamental and advanced finance concepts, it equips students with the necessary tools to navigate the financial landscape. Theoretical models are presented with transparency, avoiding the "black box" issue by clearly explaining mathematical derivations. This structured approach enhances learning and empowers students to utilize the provided code for key financial tasks, including portfolio management, risk analysis, and market sentiment analysis.
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