國立虎尾科技大學 |

Machine Learning in Finance = From Theory to Practice /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning in Finance/ by Matthew F. Dixon, Igor Halperin, Paul Bilokon.
其他題名:	From Theory to Practice /
作者:	Dixon, Matthew F.
其他作者:	Bilokon, Paul.
面頁冊數:	XXV, 548 p. 97 illus., 83 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics, general. -
電子資源:	https://doi.org/10.1007/978-3-030-41068-1
ISBN:	9783030410681

Machine Learning in Finance = From Theory to Practice /
Dixon, Matthew F.

Machine Learning in FinanceFrom Theory to Practice /[electronic resource] :by Matthew F. Dixon, Igor Halperin, Paul Bilokon. - 1st ed. 2020. - XXV, 548 p. 97 illus., 83 illus. in color.online resource.

Chapter 1. Introduction -- Chapter 2. Probabilistic Modeling -- Chapter 3. Bayesian Regression & Gaussian Processes -- Chapter 4. Feed Forward Neural Networks -- Chapter 5. Interpretability -- Chapter 6. Sequence Modeling -- Chapter 7. Probabilistic Sequence Modeling -- Chapter 8. Advanced Neural Networks -- Chapter 9. Introduction to Reinforcement learning -- Chapter 10. Applications of Reinforcement Learning -- Chapter 11. Inverse Reinforcement Learning and Imitation Learning -- Chapter 12. Frontiers of Machine Learning and Finance.

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

ISBN: 9783030410681

Standard No.: 10.1007/978-3-030-41068-1doiSubjects--Topical Terms:

671463
Statistics, general.

LC Class. No.: QA276-280

Dewey Class. No.: 330.015195

Machine Learning in Finance = From Theory to Practice /
LDR:04009nam a22004095i 4500 001 1024321
003 DE-He213
005 20200702135200.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030410681 $9 978-3-030-41068-1
024 7 $a 10.1007/978-3-030-41068-1 $2 doi
035 $a 978-3-030-41068-1
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 330.015195 $2 23
100 1 $a Dixon, Matthew F. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1320452
245 1 0 $a Machine Learning in Finance $h [electronic resource] : $b From Theory to Practice / $c by Matthew F. Dixon, Igor Halperin, Paul Bilokon.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXV, 548 p. 97 illus., 83 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1. Introduction -- Chapter 2. Probabilistic Modeling -- Chapter 3. Bayesian Regression & Gaussian Processes -- Chapter 4. Feed Forward Neural Networks -- Chapter 5. Interpretability -- Chapter 6. Sequence Modeling -- Chapter 7. Probabilistic Sequence Modeling -- Chapter 8. Advanced Neural Networks -- Chapter 9. Introduction to Reinforcement learning -- Chapter 10. Applications of Reinforcement Learning -- Chapter 11. Inverse Reinforcement Learning and Imitation Learning -- Chapter 12. Frontiers of Machine Learning and Finance.
520 $a This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
650 2 4 $a Statistics, general. $3 671463
650 2 4 $a Applications of Mathematics. $3 669175
650 1 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
650 0 $a Engineering mathematics. $3 562757
650 0 $a Applied mathematics. $3 1069907
650 0 $a Statistics . $3 1253516
700 1 $a Bilokon, Paul. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1320454
700 1 $a Halperin, Igor. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1320453
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030410674
776 0 8 $i Printed edition: $z 9783030410698
776 0 8 $i Printed edition: $z 9783030410704
856 4 0 $u https://doi.org/10.1007/978-3-030-41068-1
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)