IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v26y2019i2p83-103.html
   My bibliography  Save this article

A derivatives trading recommendation system: The mid‐curve calendar spread case

Author

Listed:
  • Adriano S. Koshiyama
  • Nikan 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 is aimed to develop a trading recommendation system, and to apply this system to the so‐called Mid‐Curve Calendar Spread (MCCS) trade. To suggest that such approach is feasible, we used a list of 35 different types of MCCSs; a total of 11 predictive and 4 benchmark models. Our results suggest that linear regression with l1‐regularisation (Lasso) compared favourably to other approaches from a predictive and interpretability point of views.

Suggested Citation

  • Adriano S. Koshiyama & Nikan Firoozye & Philip Treleaven, 2019. "A derivatives trading recommendation system: The mid‐curve calendar spread case," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(2), pages 83-103, April.
    • Handle: RePEc:wly:isacfm:v:26:y:2019:i:2:p:83-103
    DOI: 10.1002/isaf.1445
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.1445
    Download Restriction: no
    File URL: https://libkey.io/10.1002/isaf.1445?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Chen, Yu-Lun & Gau, Yin-Feng, 2010. "News announcements and price discovery in foreign exchange spot and futures markets," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1628-1636, July.
    4. 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.
    5. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
    6. 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.
    7. 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.
    8. Marina Resta, 2016. "VaRSOM: A Tool to Monitor Markets' Stability Based on Value at Risk and Self‐Organizing Maps," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 47-64, January.
    9. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. 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.
    11. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    12. 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.
    13. repec:pri:cepsud:91malkiel is not listed on IDEAS
    14. Imane El Ouadghiri & Valérie Mignon & Nicolas Boitout, 2016. "On the impact of macroeconomic news surprises on Treasury-bond returns," Annals of Finance, Springer, vol. 12(1), pages 29-53, February.
    15. Duyvesteyn, Johan & de Zwart, Gerben, 2015. "Riding the swaption curve," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 57-75.
    16. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Vega, Clara, 2007. "Real-time price discovery in global stock, bond and foreign exchange markets," Journal of International Economics, Elsevier, vol. 73(2), pages 251-277, November.
    17. Hoyong Choi & Philippe Mueller & Andrea Vedolin, 2017. "Bond Variance Risk Premiums," Review of Finance, European Finance Association, vol. 21(3), pages 987-1022.
    18. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    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. Adriano Soares Koshiyama & Nick Firoozye & Philip Treleaven, 2018. "A Machine Learning-based Recommendation System for Swaptions Strategies," Papers 1810.02125, arXiv.org.
    2. Jinho Lee & Sungwoo Park & Jungyu Ahn & Jonghun Kwak, 2022. "ETF Portfolio Construction via Neural Network trained on Financial Statement Data," Papers 2207.01187, arXiv.org.
    3. Jinho Lee & Raehyun Kim & Yookyung Koh & Jaewoo Kang, 2019. "Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network," Papers 1902.10948, arXiv.org.
    4. Gaffeo, Edoardo & Molinari, Massimo, 2017. "Taxing financial transactions in fundamentally heterogeneous markets," Economic Modelling, Elsevier, vol. 64(C), pages 322-333.
    5. Luigi Bonatti & Lorenza Lorenzetti, 2016. "The co-evolution of tax evasion, social capital and policy responses: A theoretical approach," DEM Working Papers 2016/08, Department of Economics and Management.
    6. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    7. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    8. David M. Ritzwoller & Joseph P. Romano, 2019. "Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences," Papers 1908.01406, arXiv.org, revised Apr 2021.
    9. Baoqiang Zhan & Shu Zhang & Helen S. Du & Xiaoguang Yang, 2022. "Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 861-882, October.
    10. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    11. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    12. Jitka Veselá & Alžběta Zíková, 2022. "Are the Czech, Polish, German and Dutch markets taking a random walk? [Konají český, polský, německý a nizozemský trh náhodnou procházku?]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2022(2), pages 19-38.
    13. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    14. Nathan Jensen, 2007. "International institutions and market expectations: Stock price responses to the WTO ruling on the 2002 U.S. steel tariffs," The Review of International Organizations, Springer, vol. 2(3), pages 261-280, September.
    15. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    16. Ghada A. Altarawneh & Ahmad B. Hassanat & Ahmad S. Tarawneh & Ahmad Abadleh & Malek Alrashidi & Mansoor Alghamdi, 2022. "Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods," Economies, MDPI, vol. 10(2), pages 1-18, February.
    17. John Sabelhaus, 2005. "Alternative Methods for Projecting Equity Returns: Implications for Evaluating Social Security Reform Proposals," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 8(1), pages 43-63, March.
    18. Cristi Spulbar & Ramona Birau & Lucian Florin Spulbar, 2021. "A Critical Survey on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) Considering Their Implication on Stock Markets Behavior," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1161-1165, December.
    19. Stephen Bell & John Quiggin, 2006. "Asset Price Instability and Policy Responses: The Legacy of Liberalization," Journal of Economic Issues, Taylor & Francis Journals, vol. 40(3), pages 629-649, September.
    20. Paolo Cremonesi & Chiara Francalanci & Alessandro Poli & Roberto Pagano & Luca Mazzoni & Alberto Maggioni & Mehdi Elahi, 2018. "Social Network based Short-Term Stock Trading System," Papers 1801.05295, arXiv.org.

    More about this item

    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:wly:isacfm:v:26:y:2019:i:2:p:83-103. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.