Stock and Structured Warrant Portfolio Optimization Using Black-Litterman Model and Binomial Method
In recent years, the number of Indonesian investors has rapidly increased during the COVID-19 pandemic which happened all around the world. There have been a massive number of influencers in social media who were promoting investment. Although stocks and warrants are interesting choices, mutual funds still become the main ones for beginners. Therefore, this research focuses on the development of a stock portfolio model using the Black-Litterman method which involves the investor’s views towards the stock returns. The research refers to one of the largest equity funds in Indonesia, that is Sucorinvest Equity Fund, by using the top ten of its stocks that are majority in the fund (as of April 28, 2023). Furthermore, this research also constructs a structured warrant portfolio, but it is separated from the initially constructed stock portfolio. Structured warrants could be an appropriate choice for low-budget investors. It was newly introduced in Indonesia in September 2022 so it is interesting to be observed. Based on the results and the implemented assumptions, the return obtained from the stock portfolio is superior to the observed fund’s return. Meanwhile, call structured warrant portfolio using the existing product in the market yields a negative return, because the exercise price and warrant offered price were too high. Thus, structured warrants could be considered overpriced at the moment, so the chance of obtaining profit is extremely small. Due to its similar properties to call and put options, we propose the warrant pricing and use it in simulations, so in the future, structured warrants may become an attractive instrument for the investors.
Aakarshachug, Deep Learning, Introduction to Long Short Term Memory. Retrieved (May 2023) from GeeksforGeeks: https://www.geeksforgeeks.org/deep-learning-introduction-to-long-short-term-memory.
Batavia Prosperindo Aset Manajemen. Batavia Dana Kas Maxima. Retrieved (May 2023) from Batavia Prosperindo Aset Manajemen: https://bpam.co.id/userfiles/uploads/files/FFS-SDKM-ID.pdf.
Benninga, S. Financial modeling. MIT press, 2014.
Bodie, Z. Investments. New York: McGraw-Hill Education, 2018.
Bursa Efek Indonesia (Indonesia Stock Exchange). Informasi Structured Warrant (Structured Warrant Information). Retrieved (September 2022) from idx.co.id: https://www.idx.co.id/id/data-pasar/structured-warrant-sw/informasi-structured-warrant.
Fadly, S. R., 2021. Aktivitas Pasar Modal Indonesia Di Era Pandemi (Indonesian Capital Market Activites in Pandemic Era). Retrieved from Kementerian Keuangan Republik Indonesia (Finance Ministry of Indonesia): https://www.djkn.kemenkeu.go.id/kpknl-kupang/baca-artikel/13817/Aktivitas-Pasar-Modal-Indonesia-Di-Era-Pandemi.html.
Febrianti, W., Sidarto, K. A., and Sumarti, N. Approximate solution for barrier option pricing using adaptive differential evolution with learning parameter. MENDEL 28, 2 (2022), 76–82.
Idzorek, T. A step-by-step guide to the blacklitterman model: Incorporating user-specified confidence levels. In Forecasting expected returns in the financial markets. Elsevier, 2007, pp. 17–38.
Martin, R. A., 2018. Black-Litterman Allocation. Retrieved from PyPortfolioOpt: https: //pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html.
Martin, R. A., 2018. Post-processing weights. Retrieved from PyPortfolioOpt: https://pyportfolioopt.readthedocs.io/en/latest/Postprocessing.html.
Petukhina, A., Klochkov, Y., Hardle, W. K., and Zhivotovskiy, N. Robustifying markowitz. Journal of Econometrics (2023).
Ross, S. M. An elementary introduction to mathematical finance. Cambridge University Press, 2011.
Sahamkhadam, M., Stephan, A., and Ostermark, R. Copula-based black–litterman portfolio optimization. European Journal of Operational Research 297, 3 (2022), 1055–1070.
Santos, A. A., and Torrent, H. S. Markowitz meets technical analysis: Building optimal portfolios by exploiting information in trend-following signals. Finance Research Letters 49 (2022), 103063.
Simos, T. E., Mourtas, S. D., and Katsikis, V. N. Time-varying black–litterman portfolio optimization using a bio-inspired approach and neuronets. Applied Soft Computing 112 (2021), 107767.
Stoilov, T., Stoilova, K., and Vladimirov, M. Application of modified black-litterman model for active portfolio management. Expert Systems with Applications 186 (2021), 115719.
Sucor Asset Management. Sucorinvest Equity Fund. Retrieved (May 2023) from Sucorinvestam: https://www.sucorinvestam.com/Pdf/FFS_SEF.pdf.
Sumarti, N. Simulations of a dynamical portfolio consist of stocks and options for investment during the covid-19 pandemic. In International Seminar on New Paradigm and Innovation on Natural Sciences and its Application (ISNPINSA), Bandung: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung (2022).
Tae, J., 2020. Dissecting LSTMs. Retrieved from Github: https://jaketae.github.io/study/dissecting-lstm.
Topaloglou, N., Vladimirou, H., and Zenios, S. A. Optimizing international portfolios with options and forwards. Journal of Banking & Finance 35, 12 (2011), 3188–3201.
Copyright (c) 2023 MENDEL
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
MENDEL open access articles are normally published under a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ . Under the CC BY-NC-SA 4.0 license permitted 3rd party reuse is only applicable for non-commercial purposes. Articles posted under the CC BY-NC-SA 4.0 license allow users to share, copy, and redistribute the material in any medium of format, and adapt, remix, transform, and build upon the material for any purpose. Reusing under the CC BY-NC-SA 4.0 license requires that appropriate attribution to the source of the material must be included along with a link to the license, with any changes made to the original material indicated.