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An Introduction to Machine Learning in Quantitative Finance

Author

Listed:
  • Hao Ni

    (University College London, UK)

  • Xin Dong

    (Citadel Securities LLC, UK)

  • Jinsong Zheng

    (Huatai Securities, China)

  • Guangxi Yu

    (SWS Research, China)

Abstract

In today's world, we are increasingly exposed to the words "machine learning" (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.

Suggested Citation

  • Hao Ni & Xin Dong & Jinsong Zheng & Guangxi Yu, 2021. "An Introduction to Machine Learning in Quantitative Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number q0275.
    • Handle: RePEc:wsi:wsbook:q0275
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    File URL: https://www.worldscientific.com/worldscibooks/10.1142/q0275
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    Citations

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    Cited by:

    1. Caratozzolo, Vincenzo & Misuri, Alessio & Cozzani, Valerio, 2022. "A generalized equipment vulnerability model for the quantitative risk assessment of horizontal vessels involved in Natech scenarios triggered by floods," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Claudia ANTAL-VAIDA, 2021. "Basic Hyperparameters Tuning Methods for Classification Algorithms," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 25(2), pages 64-74.

    More about this item

    Keywords

    Machine Learning; Quantitative Finance; Supervised Learning Algorithms; Reinforcement Learning; Python; Un-Supervised Learning; Financial Case Study; Statistics; Neural Network; Linear Models; Tree-Based Models;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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