IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2301.04020.html
   My bibliography  Save this paper

Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence

Author

Listed:
  • Jian Guo
  • Saizhuo Wang
  • Lionel M. Ni
  • Heung-Yeung Shum

Abstract

Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2.0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3.0, applying deep learning techniques to discover complex nonlinear pricing rules. Despite its advantage in prediction, deep learning relies on extremely large data volume and labor-intensive tuning of ``black-box'' neural network models. To address these limitations, in this paper, we introduce Quant 4.0 and provide an engineering perspective for next-generation quant. Quant 4.0 has three key differentiating components. First, automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling, practicing the philosophy of ``algorithm produces algorithm, model builds model, and eventually AI creates AI''. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes, and explains complicated and hidden risk exposures. Third, knowledge-driven AI is a supplement to data-driven AI such as deep learning and it incorporates prior knowledge into modeling to improve investment decision, in particular for quantitative value investing. Moreover, we discuss how to build a system that practices the Quant 4.0 concept. Finally, we propose ten challenging research problems for quant technology, and discuss potential solutions, research directions, and future trends.

Suggested Citation

  • Jian Guo & Saizhuo Wang & Lionel M. Ni & Heung-Yeung Shum, 2022. "Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence," Papers 2301.04020, arXiv.org.
    • Handle: RePEc:arx:papers:2301.04020
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2301.04020
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    2. Ziniu Hu & Weiqing Liu & Jiang Bian & Xuanzhe Liu & Tie-Yan Liu, 2017. "Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction," Papers 1712.02136, arXiv.org, revised Feb 2019.
    3. Schnaubelt, Matthias, 2019. "A comparison of machine learning model validation schemes for non-stationary time series data," FAU Discussion Papers in Economics 11/2019, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    5. Guo, Kun & Sun, Yi & Qian, Xin, 2017. "Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 390-396.
    6. Mancini, Cecilia, 2013. "Measuring the relevance of the microstructure noise in financial data," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2728-2751.
    7. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    8. Harrison, J. Michael & Pliska, Stanley R., 1981. "Martingales and stochastic integrals in the theory of continuous trading," Stochastic Processes and their Applications, Elsevier, vol. 11(3), pages 215-260, August.
    9. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    11. Chan, Louis K C & Jegadeesh, Narasimhan & Lakonishok, Josef, 1996. "Momentum Strategies," Journal of Finance, American Finance Association, vol. 51(5), pages 1681-1713, December.
    12. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    13. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    14. J. Michael Harrison & Stanley R. Pliska, 1981. "Martingales and Stochastic Integrals in the Theory of Continous Trading," Discussion Papers 454, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    15. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    16. Ningyuan Fan & Zhi-Ping Fan & Yongli Li & Meng Li, 2022. "Does the lead-lag effect exist in stock markets?," Applied Economics Letters, Taylor & Francis Journals, vol. 29(10), pages 895-900, June.
    17. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    18. Jingyuan Wang & Yang Zhang & Ke Tang & Junjie Wu & Zhang Xiong, 2019. "AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks," Papers 1908.02646, arXiv.org.
    19. Yongli Li & Tianchen Wang & Baiqing Sun & Chao Liu, 2022. "Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-36, December.
    20. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    21. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    22. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    23. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    24. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-257, August.
    25. Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "How Robust are Limit Order Book Representations under Data Perturbation?," Papers 2110.04752, arXiv.org.
    26. John Adams & Darren Hayunga & Sattar Mansi & David Reeb & Vincenzo Verardi, 2019. "Identifying and treating outliers in finance," Financial Management, Financial Management Association International, vol. 48(2), pages 345-384, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    2. Munk, Claus, 2015. "Financial Asset Pricing Theory," OUP Catalogue, Oxford University Press, number 9780198716457.
    3. Hardy Hulley, 2009. "Strict Local Martingales in Continuous Financial Market Models," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 19, July-Dece.
    4. Hardy Hulley, 2009. "Strict Local Martingales in Continuous Financial Market Models," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2009.
    5. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    6. Suresh M. Sundaresan, 2000. "Continuous‐Time Methods in Finance: A Review and an Assessment," Journal of Finance, American Finance Association, vol. 55(4), pages 1569-1622, August.
    7. Ion STANCU & Laura OBREJABRAŞOVEANU & Anamaria CIOBANU & Andrei Tudor STANCU, 2017. "Are Company Valuation Models the Same? – A Comparative Analysis between the Discounted Cash Flows (DCF), the Adjusted Net Asset, Value and Price Multiples, the Market Value Added (MVA) and the Residua," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(3), pages 5-20.
    8. repec:dau:papers:123456789/5374 is not listed on IDEAS
    9. Keith A. Lewis, 2019. "A Simple Proof of the Fundamental Theorem of Asset Pricing," Papers 1912.01091, arXiv.org.
    10. Eckhard Platen, 2005. "On The Role Of The Growth Optimal Portfolio In Finance," Australian Economic Papers, Wiley Blackwell, vol. 44(4), pages 365-388, December.
    11. Paul McCloud, 2020. "Expectation and Price in Incomplete Markets," Papers 2006.16703, arXiv.org.
    12. Sonntag, Dominik, 2018. "Die Theorie der fairen geometrischen Rendite [The Theory of Fair Geometric Returns]," MPRA Paper 87082, University Library of Munich, Germany.
    13. Fernando Rubio, 2005. "Eficiencia De Mercado, Administracion De Carteras De Fondos Y Behavioural Finance," Finance 0503028, University Library of Munich, Germany, revised 23 Jul 2005.
    14. Li, Minqiang, 2010. "Asset Pricing - A Brief Review," MPRA Paper 22379, University Library of Munich, Germany.
    15. Michael Dempsey, 2015. "Stock Markets, Investments and Corporate Behavior:A Conceptual Framework of Understanding," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number p1007, January.
    16. Da, Zhi & Guo, Re-Jin & Jagannathan, Ravi, 2012. "CAPM for estimating the cost of equity capital: Interpreting the empirical evidence," Journal of Financial Economics, Elsevier, vol. 103(1), pages 204-220.
    17. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    18. Timothy Johnson, 2015. "Reciprocity as a Foundation of Financial Economics," Journal of Business Ethics, Springer, vol. 131(1), pages 43-67, September.
    19. Carmine De Franco & Johann Nicolle & Huyên Pham, 2019. "Dealing with Drift Uncertainty: A Bayesian Learning Approach," Risks, MDPI, vol. 7(1), pages 1-18, January.
    20. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2017. "Research in finance: A review of influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 188-199.
    21. D. L. Wilcox & T. J. Gebbie, 2013. "On pricing kernels, information and risk," Papers 1310.4067, arXiv.org, revised Oct 2013.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2301.04020. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.