AI and Financial Markets
Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. D...
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| Year of Publication: | 2020 |
| Language: | English |
| Physical Description: | 1 electronic resource (230 p.) |
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| 041 | 0 | |a eng | |
| 100 | 1 | |a Hamori, Shigeyuki |4 edt | |
| 245 | 1 | 0 | |a AI and Financial Markets |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (230 p.) | ||
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| 520 | |a Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets. | ||
| 546 | |a English | ||
| 650 | 7 | |a Economics, finance, business & management |2 bicssc | |
| 653 | |a algorithmic trading | ||
| 653 | |a Stop Loss | ||
| 653 | |a Turtle | ||
| 653 | |a ATR | ||
| 653 | |a community finances | ||
| 653 | |a fiscal flexibility | ||
| 653 | |a individualized financial arrangements | ||
| 653 | |a sustainable financial services | ||
| 653 | |a price momentum | ||
| 653 | |a hidden markov model | ||
| 653 | |a asset allocation | ||
| 653 | |a blockchain | ||
| 653 | |a BlockCloud | ||
| 653 | |a Artificial Intelligence | ||
| 653 | |a consensus algorithms | ||
| 653 | |a exchange rates | ||
| 653 | |a fundamentals | ||
| 653 | |a prediction | ||
| 653 | |a random forest | ||
| 653 | |a support vector machine | ||
| 653 | |a neural network | ||
| 653 | |a deep reinforcement learning | ||
| 653 | |a financial market simulation | ||
| 653 | |a agent based simulation | ||
| 653 | |a artificial market | ||
| 653 | |a simulation | ||
| 653 | |a CAR regulation | ||
| 653 | |a portfolio | ||
| 653 | |a contract for difference | ||
| 653 | |a CfD | ||
| 653 | |a reinforcement learning | ||
| 653 | |a RL | ||
| 653 | |a neural networks | ||
| 653 | |a long short-term memory | ||
| 653 | |a LSTM | ||
| 653 | |a Q-learning | ||
| 653 | |a deep learning | ||
| 653 | |a uncertainty | ||
| 653 | |a economic policy | ||
| 653 | |a text mining | ||
| 653 | |a topic model | ||
| 653 | |a yield curve | ||
| 653 | |a term structure of interest rates | ||
| 653 | |a machine learning | ||
| 653 | |a autoencoder | ||
| 653 | |a interpretability | ||
| 776 | |z 3-03936-224-0 | ||
| 776 | |z 3-03936-225-9 | ||
| 700 | 1 | |a Takiguchi, Tetsuya |4 edt | |
| 700 | 1 | |a Hamori, Shigeyuki |4 oth | |
| 700 | 1 | |a Takiguchi, Tetsuya |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:46:53 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
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