Machine learning in finance : (Record no. 66277)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02737nam a22002297a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210319b2020 ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783030410674 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 330.0285 DIX-M |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Dixon, Matthew F. |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning in finance : |
| Remainder of title | from theory to practice / |
| Statement of responsibility, etc. | Matthew F. Dixon, Igor Halperin and Paul Bilokon |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Switzerland |
| Name of publisher, distributor, etc. | Springer |
| Date of publication, distribution, etc. | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 548 p. |
| 365 ## - TRADE PRICE | |
| Price type code | EU |
| Price amount | 89.99. |
| 500 ## - GENERAL NOTE | |
| General note | 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.<br/><br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Finance--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Finance--Mathematical models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Engineering mathematics |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Halperin, Igor |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bilokon, Paul |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Total Renewals | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 330 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 19/03/2021 | 6 | 1 | 330.0285 DIX-M | 41871 | 08/04/2025 | 18/03/2025 | 19/03/2021 | Books |