IDEAS home Printed from
   My bibliography  Save this paper

Artificial intelligence approach to momentum risk-taking


  • Ivan Cherednik


We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility of stock markets. Its implementation, our fully automated momentum equity trading system presented systematically, proved to be successful in extensive historical and real-time experiments. Momentum risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning with deep roots in cognitive science; its variants beyond stock markets are discussed. We begin with a new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, and the market phenomena like price targets and profit-taking. This theory generally requires Bessel and hypergeometric functions. Its discretization results in some tables of bids, which are basically expected returns for main investment horizons, the key in our trading system. The ML procedures we use are similar to those in neural networking. A preimage of our approach is the new contract card game provided at the end, a combination of bridge and poker. Relations to random processes and the fractional Brownian motion are outlined.

Suggested Citation

  • Ivan Cherednik, 2019. "Artificial intelligence approach to momentum risk-taking," Papers 1911.08448,, revised Mar 2020.
  • Handle: RePEc:arx:papers:1911.08448

    Download full text from publisher

    File URL:
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    1. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
    2. Yang, Xuebing & Zhang, Huilan, 2019. "Extreme absolute strength of stocks and performance of momentum strategies," Journal of Financial Markets, Elsevier, vol. 44(C), pages 71-90.
    3. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    4. Robert A. Korajczyk & Ronnie Sadka, 2004. "Are Momentum Profits Robust to Trading Costs?," Journal of Finance, American Finance Association, vol. 59(3), pages 1039-1082, June.
    5. Patrick Cheridito & Tardu Sepin, 2014. "Optimal Trade Execution Under Stochastic Volatility and Liquidity," Applied Mathematical Finance, Taylor & Francis Journals, vol. 21(4), pages 342-362, September.
    6. Conrad, Jennifer & Kaul, Gautam, 1998. "An Anatomy of Trading Strategies," Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 489-519.
    7. Mark Broadie & Yiping Du & Ciamac C. Moallemi, 2011. "Efficient Risk Estimation via Nested Sequential Simulation," Management Science, INFORMS, vol. 57(6), pages 1172-1194, 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. Chen, Chun-nan, 2013. "The predictability of opening returns for the returns of the trading day: Evidence from Taiwan futures market," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 272-281.
    2. Ming-Shiun Pan, 2010. "Autocorrelation, return horizons, and momentum in stock returns," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 34(3), pages 284-300, July.
    3. Szakmary, Andrew C. & Shen, Qian & Sharma, Subhash C., 2010. "Trend-following trading strategies in commodity futures: A re-examination," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 409-426, February.
    4. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    5. Svetlana Borovkova & Diego Mahakena, 2015. "News, volatility and jumps: the case of natural gas futures," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1217-1242, July.
    6. Benjamin Chabot & Eric Ghysels & Ravi Jagannathan, 2009. "Momentum Cycles and Limits to Arbitrage Evidence from Victorian England and Post-Depression US Stock Markets," NBER Working Papers 15591, National Bureau of Economic Research, Inc.
    7. Minh Phuong Doan & Vitali Alexeev & Robert Brooks, 2016. "Concurrent momentum and contrarian strategies in the Australian stock market," Australian Journal of Management, Australian School of Business, vol. 41(1), pages 77-106, February.
    8. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    9. Malay Bhattacharyya & Dileep Kumar M & Ramesh Kumar, 2009. "Optimal sampling frequency for volatility forecast models for the Indian stock markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 38-54.
    10. Kim, Abby Y. & Tse, Yiuman & Wald, John K., 2016. "Time series momentum and volatility scaling," Journal of Financial Markets, Elsevier, vol. 30(C), pages 103-124.
    11. Andrew Clare & James Seaton & Peter N Smith & Stephen Thomas, 2013. "Breaking into the blackbox: Trend following, stop losses and the frequency of trading – The case of the S&P500," Journal of Asset Management, Palgrave Macmillan, vol. 14(3), pages 182-194, June.
    12. Sina Ehsani & Juhani T. Linnainmaa, 2019. "Factor Momentum and the Momentum Factor," NBER Working Papers 25551, National Bureau of Economic Research, Inc.
    13. Heston, Steven L. & Sadka, Ronnie, 2008. "Seasonality in the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 87(2), pages 418-445, February.
    14. Lehnert, Thorsten & Wolff, Christian C, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    15. Tim Herberger & Daniel Kohlert & Andreas Oehler, 2011. "Momentum and industry-dependence: An analysis of the Swiss stock market," Journal of Asset Management, Palgrave Macmillan, vol. 11(6), pages 391-400, February.
    16. Luis Muga & Rafael Santamaria, 2007. "The momentum effect: omitted risk factors or investor behaviour? Evidence from the Spanish stock market," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 637-650.
    17. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    18. Xiafei Li & Chris Brooks & Joëlle Miffre, 2009. "Low-cost momentum strategies," Journal of Asset Management, Palgrave Macmillan, vol. 9(6), pages 366-379, February.
    19. Philippe Masset & Martin Wallmeier, 2010. "A High†Frequency Investigation of the Interaction between Volatility and DAX Returns," European Financial Management, European Financial Management Association, vol. 16(3), pages 327-344, June.
    20. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:1911.08448. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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: .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.