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Analytical Modeling and Empirical Analysis of Binary Options Strategies

Author

Listed:
  • Gurdal Ertek

    (College of Business and Economics, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Aysha Al-Kaabi

    (College of Business and Economics, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Aktham Issa Maghyereh

    (College of Business and Economics, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

Abstract

This study analyzes binary option investment strategies by developing mathematical formalism and formulating analytical models. The binary outcome of binary options represents either an increase or a decrease in a parameter, typically an asset or derivative. The investor receives only partial returns if the prediction is correct but loses all the investment otherwise. Mainstream research on binary options aims to develop the best dynamic trading strategies. This study focuses on static tactical easy-to-implement strategies and investigates the performance of such strategies in relation to prediction accuracy, payout percentage, and investment strategy decisions.

Suggested Citation

  • Gurdal Ertek & Aysha Al-Kaabi & Aktham Issa Maghyereh, 2022. "Analytical Modeling and Empirical Analysis of Binary Options Strategies," Future Internet, MDPI, vol. 14(7), pages 1-23, July.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:7:p:208-:d:856735
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    References listed on IDEAS

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    1. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    2. Dmitriy Muravyev & Neil D Pearson & Stijn Van Nieuwerburgh, 2020. "Options Trading Costs Are Lower than You Think," The Review of Financial Studies, Society for Financial Studies, vol. 33(11), pages 4973-5014.
    3. Radosław Pietrzyk & Paweł Rokita, 2018. "Determination of the Own Funds Requirements for the Risk of Binary Options," Springer Proceedings in Business and Economics, in: Krzysztof Jajuga & Hermann Locarek-Junge & Lucjan T. Orlowski (ed.), Contemporary Trends and Challenges in Finance, pages 23-32, Springer.
    4. Cong, F. & Oosterlee, C.W., 2016. "Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 64(C), pages 23-38.
    5. Wei Pan & Jide Li & Xiaoqiang Li, 2020. "Portfolio Learning Based on Deep Learning," Future Internet, MDPI, vol. 12(11), pages 1-13, November.
    6. Dimitrios Vezeris & Themistoklis Kyrgos & Christos Schinas, 2018. "Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System," JRFM, MDPI, vol. 11(3), pages 1-23, September.
    7. Kiyoshi Suzuki, 2018. "Optimal pair-trading strategy over long/short/square positions—empirical study," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 97-119, January.
    8. Vasiliki A. Basdekidou, 2017. "Nonfarm Employment Report Trading With Binary Options & Temporal Functionalities," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(19), pages 1-1.
    9. Tsai, Wei-Che, 2014. "Improved method for static replication under the CEV model," Finance Research Letters, Elsevier, vol. 11(3), pages 194-202.
    10. Masatoshi Miyake & Hiroshi Inoue & Jianming Shi & Tetsuya Shimokawa, 2014. "A Binary Option Pricing Based on Fuzziness," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(06), pages 1211-1227.
    11. Jean-François Bégin & Christian Dorion & Geneviève Gauthier, 2020. "Idiosyncratic Jump Risk Matters: Evidence from Equity Returns and Options," The Review of Financial Studies, Society for Financial Studies, vol. 33(1), pages 155-211.
    12. Gaetano Giunta & Francesco Benedetto, 2012. "Empirical Case Study of Binary Options Trading: An Interdisciplinary Application of Telecommunications Methodology to Financial Economics," International Journal of Interdisciplinary Telecommunications and Networking (IJITN), IGI Global, vol. 4(4), pages 54-63, October.
    13. Gürdal Ertek & Gül Tokdemir & Mete Sevinç & Murat Mustafa Tunç, 2017. "New knowledge in strategic management through visually mining semantic networks," Information Systems Frontiers, Springer, vol. 19(1), pages 165-185, February.
    14. Ahmet Duran & Michael Bommarito, 2011. "A profitable trading and risk management strategy despite transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 829-848.
    15. Pankaj Pandey & Einar Snekkenes, 2016. "Using Financial Instruments to Transfer the Information Security Risks," Future Internet, MDPI, vol. 8(2), pages 1-62, May.
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