Big Data and Machine Learning in Quantitative Investment (Wiley Finance) - Tapa dura

Acerca del autor

TONY GUIDA 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.

De la contraportada

Praise for Big Data and Machine Learning in Quantitative Investment

"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."
—Tammer Kamel, CEO and founder, Quandl, Toronto

"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."
—Ahcene Gareche, Head of Quantitative Strategies, AXA IM Chorus, Hong Kong

"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."
—Nick Baltas, Head of R&D - Systematic Trading Strategies, Goldman Sachs, London

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Machine learning continues to advance in more sophisticated ways in order to analyse aspects of life big data can record, from our physical well-being to our spending habits and driving behaviour. However, financial markets are a different kind of animal altogether, and while they may seem data rich, their complexity is extremely hard to be understood by algorithms alone. Big Data and Machine Learning in Quantitative Investment gives you a seat at the practitioners' table to discuss why and how machine learning is most effectively used in finance.

Senior quant Tony Guida assembled this all-star team of authors coming from buy-side, sell-side and quantitative research. His goal was to create a fresh, multidimensional resource of proven best practices for tactfully optimizing the benefits of machine learning in quantitative investing.

Algorithms don't understand market laws better than you do, but inside, you'll see precisely where and how they can empower you.