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A Machine Learning-based Recommendation System for Swaptions Strategies

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  • Adriano Soares Koshiyama
  • Nick Firoozye
  • Philip Treleaven

Abstract

Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work aims to develop a trading recommendation system, and apply this system to the so-called Mid-Curve Calendar Spread (MCCS), an exotic swaption-based derivatives package. In summary, our trading recommendation system follows this pipeline: (i) on a certain trade date, we compute metrics and sensitivities related to an MCCS; (ii) these metrics are feed in a model that can predict its expected return for a given holding period; and after repeating (i) and (ii) for all trades we (iii) rank the trades using some dominance criteria. To suggest that such approach is feasible, we used a list of 35 different types of MCCS; a total of 11 predictive models; and 4 benchmark models. Our results suggest that in general linear regression with lasso regularisation compared favourably to other approaches from a predictive and interpretability perspective.

Suggested Citation

  • Adriano Soares Koshiyama & Nick Firoozye & Philip Treleaven, 2018. "A Machine Learning-based Recommendation System for Swaptions Strategies," Papers 1810.02125, arXiv.org.
    • Handle: RePEc:arx:papers:1810.02125
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    References listed on IDEAS

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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Rebecca M. Baker & Tahani Coolen-Maturi & Frank P. A. Coolen, 2017. "Nonparametric predictive inference for stock returns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(8), pages 1333-1349, June.
    3. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    4. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Christian Spreckelsen & Hans‐Jörg Mettenheim & Michael H. Breitner, 2014. "Real‐Time Pricing and Hedging of Options on Currency Futures with Artificial Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 419-432, September.
    5. Andreas Karathanasopoulos & Konstantinos Athanasios Theofilatos & Georgios Sermpinis & Christian Dunis & Sovan Mitra & Charalampos Stasinakis, 2016. "Stock market prediction using evolutionary support vector machines: an application to the ASE20 index," The European Journal of Finance, Taylor & Francis Journals, vol. 22(12), pages 1145-1163, September.
    6. repec:pri:cepsud:91malkiel is not listed on IDEAS
    7. Zhou, Xiaocong & Nakajima, Jouchi & West, Mike, 2014. "Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 963-980.
    8. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
    9. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    10. Hoyong Choi & Philippe Mueller & Andrea Vedolin, 2017. "Bond Variance Risk Premiums," Review of Finance, European Finance Association, vol. 21(3), pages 987-1022.
    11. Philippe Mueller & Andrea Vedolin & Yu-min Yen, 2012. "Bond Variance Risk Premia," FMG Discussion Papers dp699, Financial Markets Group.
    12. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    13. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
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