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Financial analytics with R[electronic resource] :building a laptop laboratory for data science /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	332.0285513
書名/作者:	Financial analytics with R : building a laptop laboratory for data science // Mark J. Bennett, Dirk L. Hugen.
作者:	Bennett, Mark J.
其他作者:	Hugen, Dirk L.
出版者:	Cambridge : : Cambridge University Press,, 2016.
面頁冊數:	xvi, 377 p. : : digital ;; 24 cm.
附註:	Title from publisher's bibliographic system (viewed on 27 Oct 2016).
標題:	Finance - Mathematical models
標題:	Finance - Databases.
標題:	R (Computer program language)
ISBN:	9781316584460
ISBN:	9781107150751
摘要、提要註:	Are you innately curious about dynamically inter-operating financial markets? Since the crisis of 2008, there is a need for professionals with more understanding about statistics and data analysis, who can discuss the various risk metrics, particularly those involving extreme events. By providing a resource for training students and professionals in basic and sophisticated analytics, this book meets that need. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.
電子資源:	https://doi.org/10.1017/CBO9781316584460

Financial analytics with R[electronic resource] :building a laptop laboratory for data science /
Bennett, Mark J.

Financial analytics with Rbuilding a laptop laboratory for data science /[electronic resource] :Mark J. Bennett, Dirk L. Hugen. - Cambridge :Cambridge University Press,2016. - xvi, 377 p. :digital ;24 cm.

Title from publisher's bibliographic system (viewed on 27 Oct 2016).

Are you innately curious about dynamically inter-operating financial markets? Since the crisis of 2008, there is a need for professionals with more understanding about statistics and data analysis, who can discuss the various risk metrics, particularly those involving extreme events. By providing a resource for training students and professionals in basic and sophisticated analytics, this book meets that need. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

ISBN: 9781316584460Subjects--Topical Terms:

710432
Finance
--Mathematical models

LC Class. No.: HG104 / .B46 2016

Dewey Class. No.: 332.0285513

Financial analytics with R[electronic resource] :building a laptop laboratory for data science /
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520 $a Are you innately curious about dynamically inter-operating financial markets? Since the crisis of 2008, there is a need for professionals with more understanding about statistics and data analysis, who can discuss the various risk metrics, particularly those involving extreme events. By providing a resource for training students and professionals in basic and sophisticated analytics, this book meets that need. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.
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