Maximizing returns under capped risks: An optimization framework for options trading
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Author(s)Emre AriLink to ORCID Index: https://orcid.org/0000-0003-2995-0176
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Alp UstundagLink to ORCID Index: https://orcid.org/0000-0003-2151-4759
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Mahmut Sami SivriLink to ORCID Index: https://orcid.org/0000-0002-9391-1801
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DOIhttp://dx.doi.org/10.21511/imfi.22(2).2025.17
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Article InfoVolume 22 2025, Issue #2, pp. 206-217
- 23 Views
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9 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Precise risk management is crucial in options trading, especially in strategies with limited risk and capped profit potential. The Short Iron Condor is a widely adopted strategy due to its structured risk-reward profile. It provides traders with controlled exposure in low-volatility markets while maintaining defined profit and loss parameters. This paper deals with developing an optimization framework using a mixed-integer programming model to evaluate key factors influencing return efficiency, including maximum loss limits, price confidence intervals, and holding periods. Using 2023 options data for 14 U.S. equities and 9 ETFs, filtered and selected using Out of the Money Strategy (OTM), 324 option contracts from as many snapshots as possible, the study analyzes 324 trading scenarios with maturities ranging from 5 to 20 days. Results indicate that increasing the maximum loss limit raises total return but reduces return efficiency. A $100 loss limit generates an average return of $30 with a 40.7% return on investment, while a $900 limit increases returns to $131 but lowers return on investment to 18.8%. These findings demonstrate that higher risk exposure does not always enhance return efficiency in capped-risk strategies. The proposed framework provides actionable insights for traders aiming to refine strategy selection within well-defined risk constraints. Risk managers can utilize these findings to sustain stable investment portfolios, while algorithmic trading systems may integrate this optimization model for automated strategy refinements and real-time adjustments. This study enhances decision-making in options trading, portfolio risk management, and financial strategy development.
- Keywords
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JEL Classification (Paper profile tab)G11, G12, G14, G15
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References52
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Tables5
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Figures2
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- Figure 1. Short iron condor strategy
- Figure 2. Maximum loss vs ROI
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- Table 1. US stocks and ETFs
- Table 2. Experimental design setups
- Table 3. Comparison of loss limits, average return, and ROI
- Table 4. Comparison of price confidence interval, average return, and ROI
- Table 5. Days to maturity-based comparisons
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Energy efficiency and green solutions in sustainable development: evidence from the Norwegian maritime industry
Viktoriia Koilo
doi: http://dx.doi.org/10.21511/ppm.18(4).2020.24
Problems and Perspectives in Management Volume 18, 2020 Issue #4 pp. 289-302 Views: 1110 Downloads: 260 TO CITE АНОТАЦІЯThe maritime industry plays a special role in Norway. In recent years, it became subject to increasingly stronger requirements to reduce emissions. However, the most important is that the Norwegian maritime industry in several areas can deliver and further develop technology and products that provide lower emissions, nationally and globally. Going forward, technology development will be more important with time. Thus, it is important to find out what impact it will have on the industry’s sustainable development and estimate the efficiency of new technologies.
This paper primarily aims to find a new optimization tool, which allows monitoring progress in the maritime industry towards sustainable development.
The present study reveals many new possible zero-emission solutions in the maritime industry, such as battery-electric architectures, ammonia, hydrogen, biofuel, and liquefied natural gas (LNG), liquefied petroleum gas (LPG), autonomous ships, etc. Moreover, it was highlighted that without active coordination between governance, academia, and industry, it is impossible to achieve international climate commitments and associated targets for reducing the emissions in the maritime industry.
In addition, in this study, a twofold model was proposed: the first part is the calculation of the Sustainable Development Index (SDI), and the last one is mathematical modeling, where the optimization variable carbon dioxide (CO2) emissions and Sustainable Development Index (SDI) should be maximized.
The investigation results prove that the model should be tested, and further research in this area is needed.Acknowledgment
The research is supported by a grant from the Research Based Innovation “SFI Marine Operation in Virtual Environment (SFI-MOVE)” (Project no: 237929) in Norway.
