{"product_id":"big-data-and-machine-learning-in-quantitative-investment-isbn-9781119522195","title":"Big Data and Machine Learning in Quantitative Investment","description":"\u003cp\u003e\u003cb\u003eGet to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eBig Data and Machine Learning in Quantitative Investment \u003c\/i\u003eis not just about demonstrating the maths or the coding. Instead, it’s a book \u003ci\u003eby\u003c\/i\u003e practitioners \u003ci\u003efor\u003c\/i\u003e practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance.\u003c\/p\u003e \u003cp\u003eThe book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning.\u003c\/p\u003e \u003cp\u003e•    Gain a solid reason to use machine learning\u003c\/p\u003e \u003cp\u003e•    Frame your question using financial markets laws\u003c\/p\u003e \u003cp\u003e•    Know your data\u003cbr\u003e\u003cbr\u003e•    Understand how machine learning is becoming ever more sophisticated\u003c\/p\u003e \u003cp\u003eMachine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.\u003c\/p\u003e \u003cp\u003eCHAPTER 1 Do Algorithms Dream About Artificial Alphas? 1\u003cbr\u003e\u003ci\u003eBy Michael Kollo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 2 Taming Big Data 13\u003cbr\u003e\u003ci\u003eBy Rado Lipuš and Daryl Smith\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 3 State of Machine Learning Applications in Investment Management 33\u003cbr\u003e\u003ci\u003eBy Ekaterina Sirotyuk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 4 Implementing Alternative Data in an Investment Process 51\u003cbr\u003e\u003ci\u003eBy Vinesh Jha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 5 Using Alternative and Big Data to Trade Macro Assets 75\u003cbr\u003e\u003ci\u003eBy Saeed Amen and Iain Clark\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 6 Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales 95\u003cbr\u003e\u003ci\u003eBy Giuliano De Rossi, Jakub Kolodziej and Gurvinder Brar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 7 Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework 129\u003cbr\u003e\u003ci\u003eBy Tony Guida and Guillaume Coqueret\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 8 A Social Media Analysis of Corporate Culture 149\u003cbr\u003e\u003ci\u003eBy Andy Moniz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 9 Machine Learning and Event Detection for Trading Energy Futures 169\u003cbr\u003e\u003ci\u003eBy Peter Hafez and Francesco Lautizi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 10 Natural Language Processing of Financial News 185\u003cbr\u003e\u003ci\u003eBy M. Berkan Sesen, Yazann Romahi and Victor Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 11 Support Vector Machine-Based Global Tactical Asset Allocation 211\u003cbr\u003e\u003ci\u003eBy Joel Guglietta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 12 Reinforcement Learning in Finance 225\u003cbr\u003e\u003ci\u003eBy Gordon Ritter\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 13 Deep Learning in Finance: Prediction of Stock Returns with Long Short-Term Memory Networks 251\u003cbr\u003e\u003ci\u003eBy Miquel N. Alonso, Gilberto Batres-Estrada and Aymeric Moulin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eBiography 279\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eTONY GUIDA\u003c\/b\u003e is a senior investment manager in quantitative equity at the investment manager of a major UK pension fund in London, where he manages multifactor systematic equity portfolios. During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research analyst at UNIGESTION. He is a former member of the research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences. Tony is chair of machineByte ThinkTank EMEA.   \u003c\/p\u003e\u003cp\u003ePraise for \u003cb\u003eBig Data and Machine Learning in Quantitative Investment\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\"Alternative data and machine learning are about to become essential components of the modern investment process. This excellent book offers practitioners a rich collection of case studies written by some of the most capable quants in the world today. It will be on our shelves here at Quandl for sure.\" \u003cb\u003e—Tammer Kamel,\u003c\/b\u003e CEO and founder, Quandl, Toronto \u003c\/p\u003e\u003cp\u003e\"Tony Guida has managed to cover an impressive list of recent topics in Financial Machine Learning and Big Data, such as deep learning, reinforcement learning or natural language processing, in this book. It is accessible and rich with real-world applications, written in readable style. It will appeal to quants, students and regulators at all levels, and will undoubtedly become a reference textbook, one of the few not to be missed by anybody interested in Machine Learning and Big Data applications.\" \u003cb\u003e—Ahcene Gareche,\u003c\/b\u003e Head of Quantitative Strategies, AXA IM Chorus, Hong Kong \u003c\/p\u003e\u003cp\u003e\"Artificial intelligence and machine learning, big and alternative data, are unequivocally buzz words of our times and quantitative finance is not exempt from that. However, not all datasets are necessarily useful for financial applications and not all ML techniques can be applied on a \"plug-and-play\" basis. Importantly, the industry needs specialised guidance on how different datasets and ML techniques should be used for quantitative investments. The new book, edited by Tony Guida, is here to address this need by providing a diverse collection of 13 self-contained chapters written by practitioners who offer different perspectives and use cases of big data and ML techniques in finance and investments. Some chapters are more philosophical, providing guidance and perspective. Others are more practical focusing either on the manipulation of big data or on the specifics of particular ML approaches when employed for financial applications. All in all, for the investment professional who is either experienced or new entrant in the ML\/big data in quantitative investing space, Tony Guida has made a remarkable attempt to provide a holistic view of the landscape. It is worth a read.\" \u003cb\u003e—Nick Baltas,\u003c\/b\u003e Head of R\u0026amp;D - Systematic Trading Strategies, Goldman Sachs, London\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988810186981,"sku":"NP9781119522195","price":52.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119522195.jpg?v=1761781679","url":"https:\/\/k12savings.com\/products\/big-data-and-machine-learning-in-quantitative-investment-isbn-9781119522195","provider":"K12savings","version":"1.0","type":"link"}