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| 010 | ▼a 2022055176 | |
| 020 | ▼a 9781032391205 ▼q (hardback) | |
| 020 | ▼a 9781032384436 ▼q (paperback) | |
| 020 | ▼z 9781003348474 ▼q (ebook) | |
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| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d DLC ▼d 211009 | |
| 042 | ▼a pcc | |
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| 082 | 0 0 | ▼a 332.0285/63 ▼2 23 |
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| 100 | 1 | ▼a Tsang, Edward. |
| 245 | 1 0 | ▼a AI for finance / ▼c Edward P.K. Tsang. |
| 250 | ▼a 1st ed. | |
| 260 | ▼a Boca Raton ; ▼a London : ▼b CRC Press, ▼c 2023. | |
| 264 | 1 | ▼a Boca Raton ; ▼a London : ▼b CRC Press, ▼c 2023. |
| 300 | ▼a xx, 105 p. ; ▼c 20 cm. | |
| 336 | ▼a text ▼b txt ▼2 rdacontent | |
| 337 | ▼a unmediated ▼b n ▼2 rdamedia | |
| 338 | ▼a volume ▼b nc ▼2 rdacarrier | |
| 490 | 1 | ▼a AI for everything series ; ▼v [v. 4] |
| 504 | ▼a Includes bibliographical references and index. | |
| 520 | ▼a "Finance students and practitioners may ask: Can machines learn everything? Where could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance. To finance students and practitioners, this book will explain the promise of AI and its limitations. It will cover knowledge representation, modelling, simulation and machine learning. It will explain the principle of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This includes algorithmic trading, forecasting, risk analysis portfolio optimization and other less well-known areas in finance. This book trades depth for readability. It aims to help readers to decide whether to invest more time into the subject. This book contains original research. For example, it explains the impact of ignoring computation in classical economics. It explains the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists. It introduces Directional Change and explains how it can be used"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Investments ▼x Data processing. |
| 650 | 0 | ▼a Finance ▼x Data processing. |
| 650 | 0 | ▼a Artificial intelligence ▼x Financial applications. |
| 830 | 0 | ▼a AI for everything series ; ▼v [v. 4]. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.3 A7918 4 | 등록번호 121265127 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.
Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.
To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This includes algorithmic trading, forecasting, risk analysis portfolio optimization and other less well-known areas in finance. Trading depth for readability, AI for Finance will help readers decide whether to invest more time into the subject.
Moving well beyond simply speeding up computation, this book tackles AI for Finance from a range of perspectives including business, technology, research, and students. Covering aspects like algorithms, big data, and machine learning, this book answers these and many other questions.
정보제공 :
목차
1. AI-Finance Synergy, 2. Machine Learning Knows No Boundaries?, 3.Machine Learning in Finance, 4. Modelling, Simulation and Machine Learning, 5. Portfolio Optimization, 6. Financial Data: Beyond Time Series, 7. Over the Horizon