大葉大學圖書館 |

Big data and machine learning in quantitative investment /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	332.60285/631
書名/作者:	Big data and machine learning in quantitative investment // Tony Guida.
作者:	Guida, Tony,
出版者:	Chichester, West Sussex : : Wiley,, c2019.
面頁冊數:	vi, 285 p. : : ill. ;; 25 cm.
標題:	Investments - Study and teaching.
標題:	Machine learning.
標題:	Big data.
ISBN:	9781119522195 (hbk.) :
書目註:	Includes bibliographical references.
內容註:	Machine generated contents note: Chapter 1: Do algorithms dream about artificial alphas? Chapter 2: Taming Big data Chapter 3: State of machine learning applications in investment management Chapter 4: Implementing alternative data in an investment Process Chapter 5: Using alternative and Big Data to trade macro assets Chapter 6: Big is beautiful: How email receipt data can help predict company sales Chapter 7: Ensemble learning applied to quant equity: gradient boosting in a multi-factor framework Chapter 8: A social media analysis of corporate culture Chapter 9: Machine Learning & Event Detection for Trading Energy Futures Chapter 10: Natural language processing of financial news Chapter 11: Support-Vector-Machine Based Global Tactical Asset Allocation Chapter 12: Reinforcement learning in finance Chapter 13: Deep learning in Finance: Prediction of stock returns with long short term memory networks Biography of contributors.
摘要、提要註:	"Get to know the "why" and "how" of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how"--

Big data and machine learning in quantitative investment /
Guida, Tony,1979-

Big data and machine learning in quantitative investment /Tony Guida. - Chichester, West Sussex :Wiley,c2019. - vi, 285 p. :ill. ;25 cm. - Wiley finance series. - Wiley finance series..

Includes bibliographical references.

Machine generated contents note: Chapter 1: Do algorithms dream about artificial alphas? Chapter 2: Taming Big data Chapter 3: State of machine learning applications in investment management Chapter 4: Implementing alternative data in an investment Process Chapter 5: Using alternative and Big Data to trade macro assets Chapter 6: Big is beautiful: How email receipt data can help predict company sales Chapter 7: Ensemble learning applied to quant equity: gradient boosting in a multi-factor framework Chapter 8: A social media analysis of corporate culture Chapter 9: Machine Learning & Event Detection for Trading Energy Futures Chapter 10: Natural language processing of financial news Chapter 11: Support-Vector-Machine Based Global Tactical Asset Allocation Chapter 12: Reinforcement learning in finance Chapter 13: Deep learning in Finance: Prediction of stock returns with long short term memory networks Biography of contributors.

"Get to know the "why" and "how" of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. •    Gain a solid reason to use machine learning •    Frame your question using financial markets laws •    Know your data •    Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how"--

ISBN: 9781119522195 (hbk.) :NTD 1,526

LCCN: 2018036097Subjects--Topical Terms:

704420
Investments
--Study and teaching.

LC Class. No.: HG4521 / .G795 2019

Dewey Class. No.: 332.60285/631

Big data and machine learning in quantitative investment /
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020 $a 9781119522195 (hbk.) : $c NTD 1,526
020 $z 9781119522218 (ePub)
020 $z 9781119522089 (ePDF)
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082 0 0 $a 332.60285/631 $2 23
100 1 $a Guida, Tony, $d 1979- $3 704419
245 1 0 $a Big data and machine learning in quantitative investment / $c Tony Guida.
260 $a Chichester, West Sussex : $b Wiley, $c c2019.
300 $a vi, 285 p. : $b ill. ; $c 25 cm.
490 1 $a Wiley finance series
504 $a Includes bibliographical references.
505 8 $a Machine generated contents note: Chapter 1: Do algorithms dream about artificial alphas? Chapter 2: Taming Big data Chapter 3: State of machine learning applications in investment management Chapter 4: Implementing alternative data in an investment Process Chapter 5: Using alternative and Big Data to trade macro assets Chapter 6: Big is beautiful: How email receipt data can help predict company sales Chapter 7: Ensemble learning applied to quant equity: gradient boosting in a multi-factor framework Chapter 8: A social media analysis of corporate culture Chapter 9: Machine Learning & Event Detection for Trading Energy Futures Chapter 10: Natural language processing of financial news Chapter 11: Support-Vector-Machine Based Global Tactical Asset Allocation Chapter 12: Reinforcement learning in finance Chapter 13: Deep learning in Finance: Prediction of stock returns with long short term memory networks Biography of contributors.
520 $a "Get to know the "why" and "how" of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. •    Gain a solid reason to use machine learning •    Frame your question using financial markets laws •    Know your data •    Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how"-- $c Provided by publisher.
520 $a "Sales handles: ACTIONABLE CONTENT: it is the only book on the subject written by practitioners for practitioners, focusing on the "why" and "how" of using machine learning and big data in finance. It is not a book on mathematical demonstration or coding HIGH-CALIBER AUTHOR TEAM with wide networks within the Quant community. Great opportunities for promotion and possibly buybacks HOT TOPIC: machine learning and artificial intelligence are of huge interest to finance institutions looking to gain an edge Marketing Decription: Each of the authors is well known and respected in the Quant Finance field; each has a wide professional network, and speaks regularly at major Quant conferences around the world. They are also members of Quant finance organisations such as Opalesque, London Quant group, Inquire, CFA Financial Journal, EDHEC Risk, QuantCon and Re-Work Deep learning"-- $c Provided by publisher.
650 0 $a Investments $x Study and teaching. $3 704420
650 0 $a Machine learning. $3 202931
650 0 $a Big data. $3 571002
830 0 $a Wiley finance series. $3 337882