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Big Data and Machine Learning in Quantitative Investment

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  • Tony Guida

    (EM - EMLyon Business School)

Abstract

Get to know the ‘why' and ‘how' of machine learning and big data in quantitative investment. Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it's a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The 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. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine 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.

Suggested Citation

  • Tony Guida, 2019. "Big Data and Machine Learning in Quantitative Investment," Post-Print hal-02298299, HAL.
    • Handle: RePEc:hal:journl:hal-02298299
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    Cited by:

    1. Sihang Chen & Weiqi Luo & Chao Yu, 2021. "Reinforcement Learning with Expert Trajectory For Quantitative Trading," Papers 2105.03844, arXiv.org.
    2. Robert Gk{e}barowski & Pawe{l} O'swik{e}cimka & Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z, 2019. "Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis," Papers 1906.07491, arXiv.org, revised Oct 2019.

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