{"product_id":"machine-learning-and-data-sciences-for-financial-markets-a-guide-to-contemporary-practices-hardback-9781316516195","title":"Machine Learning and Data Sciences for Financial Markets; A Guide to Contemporary Practices (Hardback) 9781316516195","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning and Data Sciences for Financial Markets\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Guide to Contemporary Practices\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eLearn how cutting-edge AI and data science techniques are integrated in financial markets from leading experts in the industry.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAgostino Capponi (Edited by), Charles-Albert Lehalle (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781316516195, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 1 June 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e741 pages\u003cbr\u003e26 x 18.3 x 3.7 cm, 1.67 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices' comes at a critical time in the financial markets. The amount of machine readable data available to practitioners, the power of the statistical models they can build, and the computational power available to train them keeps growing exponentially. AI and machine learning are increasingly embedded into every aspect of the investing process. The common curriculum, however, both in finance and in applications of machine learning, lags behind. This book provides an excellent and very thorough overview of the state of the art in the field, with contributions by key researchers and practitioners. The monumental work done by the editors and reviewers shows in the wide diversity of current topics covered – from deep learning for solving partial differential equations to transformative breakthroughs in NLP. This book, which I cannot recommend highly enough, will be useful to any practitioner or student who wishes to familiarize themselves with the current state of the art and build their careers and research on a solid foundation.' Gary Kazantsev, Bloomberg and Columbia University\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eLeveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eInteracting with Investors and Asset Owners: Part I. Robo-advisors and Automated Recommendation: 1. Introduction to Part I. Robo-advising as a technological platform for optimization and recommendations\u003cbr\u003e 2. New frontiers of robo-advising: consumption, saving, debt management, and taxes\u003cbr\u003e 3. Robo-advising: less AI and more XAI? Augmenting algorithms with humans-in-the-loop\u003cbr\u003e 4. Robo-advisory: from investing principles and algorithms to future developments\u003cbr\u003e 5. Recommender systems for corporate bond trading\u003cbr\u003e Part II. How Learned Flows Form Prices: 6. Introduction to Part II. Price impact: information revelation or self-fulfilling prophecies?\u003cbr\u003e 7. Order flow and price formation\u003cbr\u003e 8. Price formation and learning in equilibrium under asymmetric information\u003cbr\u003e 9. Deciphering how investors' daily flows are forming prices\u003cbr\u003e Towards Better Risk Intermediation: Part III. High Frequency Finance: 10. Introduction to Part III\u003cbr\u003e 11. Reinforcement learning methods in algorithmic trading\u003cbr\u003e 12. Stochastic approximation applied to optimal execution: learning by trading\u003cbr\u003e 13. Reinforcement learning for algorithmic trading\u003cbr\u003e Part IV. Advanced Optimization Techniques: 14. Introduction to Part IV. Advanced optimization techniques for banks and asset managers\u003cbr\u003e 15. Harnessing quantitative finance by data-centric methods\u003cbr\u003e 16. Asset pricing and investment with big data\u003cbr\u003e 17. Portfolio construction using stratified models\u003cbr\u003e Part V. New Frontiers for Stochastic Control in Finance: 18. Introduction to Part V. Machine learning and applied mathematics: a game of hide-and-seek?\u003cbr\u003e 19. The curse of optimality, and how to break it?\u003cbr\u003e 20. Deep learning for mean field games and mean field control with applications to finance\u003cbr\u003e 21. Reinforcement learning for mean field games, with applications to economics\u003cbr\u003e 22. Neural networks-based algorithms for stochastic control and PDEs in finance\u003cbr\u003e 23. Generative adversarial networks: some analytical perspectives\u003cbr\u003e Connections with the Real Economy: Part VI. Nowcasting with Alternative Data: 24. Introduction to Part VI. Nowcasting is coming\u003cbr\u003e 25. Data preselection in machine learning methods: an application to macroeconomic nowcasting with Google search data\u003cbr\u003e 26. Alternative data and ML for macro nowcasting\u003cbr\u003e 27. Nowcasting corporate financials and consumer baskets with alternative data\u003cbr\u003e 28. NLP in finance\u003cbr\u003e 29. The exploitation of recurrent satellite imaging for the fine-scale observation of human activity\u003cbr\u003e Part VII. Biases and Model Risks of Data-Driven Learning: 30. Introduction to Part VII. Towards the ideal mix between data and models\u003cbr\u003e 31. Generative Pricing model complexity: the case for volatility-managed portfolios\u003cbr\u003e 32. Bayesian deep fundamental factor models\u003cbr\u003e 33. Black-box model risk in finance\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Applied mathematics [\u003ca title=\"See our other books on Applied mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Applied%20mathematics%20%5BPBW%5D%22\"\u003ePBW\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46000155427096,"sku":"9781316516195","price":85.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781316516195i.jpg?v=1696681716","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/machine-learning-and-data-sciences-for-financial-markets-a-guide-to-contemporary-practices-hardback-9781316516195","provider":"Freshly Printed Books","version":"1.0","type":"link"}