000 04487cam a2200469 i 4500
001 000q0275
003 WSP
007 cr cnu|||unuuu
008 200910s2021 si ob 001 0 eng d
040 _a WSPC
_b eng
_e rda
_c WSPC
010 _z 2020041545
020 _a9781786349378
_q(ebook)
020 _z9781786349361
_q(hardback)
020 _z9781786349644
_q(paperback)
050 0 0 _aHG106
_b.N52 2021
072 7 _aBUS
_x027010
_2bisacsh
072 7 _aCOM
_x094000
_2bisacsh
072 7 _aMAT
_x003000
_2bisacsh
082 0 0 _a332.0285/631
_223
100 1 _aNi, Hao
_c(Lecturer in mathematics),
_eauthor.
_9178264
245 1 3 _aAn introduction to machine learning in quantitative finance /
_cby Hao Ni (University College London, UK), Xin Dong (Citadel Securities LLC, UK), Jinsong Zheng (Huatai Securities, China) and Guangxi Yu (SWS Research, China).
264 1 _aSingapore ;
_aNew Jersey :
_bWorld Scientific,
_c2021.
300 _a1 online resource (xxiv, 238 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bn
_2rdamedia
338 _aonline resource
_bnc
_2rdacarrier
490 1 _aAdvanced textbooks in mathematics
504 _aIncludes bibliographical references and index.
505 0 _aPreface -- About the authors -- Acknowledgments -- Disclaimer -- Listings -- Overview of machine learning and financial applications -- Supervised learning -- Linear regression and regularization -- Tree-based models -- Neural networks -- Cluster analysis -- Principal component analysis -- Reinforcement learning -- Case study in finance : home credit default risk -- Bibliography -- Index.
520 _a"In today's world, we are increasingly exposed to the words "machine learning" (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it. An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authors Provide a systematic and rigorous introduction to supervised, unsupervised and reinforcement learning by establishing essential definitions and theorems. Dive into various types of neural networks, including artificial nets, convolutional nets, recurrent nets and recurrent reinforcement learning. Summarize key contents of each section in the tables as a cheat sheet. Include ample examples of financial applications. Showcase how to tackle an exemplar ML project on financial data end-to-end. Supplement Python codes of all the methods/examples in a GitHub repository. Featured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data! The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git that contains supplementary Python codes of all methods/examples. Featured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!"--Publisher's website.
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
650 0 _aFinance
_xMathematical models.
_914236
650 0 _aMachine learning.
_91831
655 0 _aElectronic books.
_93294
700 1 _aDong, Xin,
_eauthor.
_9178265
700 1 _aZheng, Jinsong,
_eauthor.
_9178266
700 1 _aYu, Guangxi,
_eauthor.
_9178267
830 0 _aAdvanced textbooks in mathematics.
_9178268
856 4 0 _uhttps://www.worldscientific.com/worldscibooks/10.1142/q0275#t=toc
_zAccess to full text is restricted to subscribers.
942 _cEBK
999 _c97727
_d97727