Dampak pandemi Covid-19 terhadap portofolio saham bisnis logistik transportasi laut di Indonesia
Article Sidebar
Sea transportation,
Covid-19,
Stock portfolio,
Monte carlo simulation,
GRDP, Investment, Spatial, Spatial Durbin Model
Main Article Content
Abstract
The Covid-19 pandemic has had an impact on the global economy, particularly in the logistics industry, which includes Indonesia. The goal of this study is to identify the portfolio performance of the top five Indonesian companies in the logistics sector, particularly sea transportation, and analyze the impact before and during the pandemic using a Monte Carlo simulation of the optimal risk asset portfolio two years before the Covid-19 pandemic and two years during the pandemic. Covid-19. Before (during) the Covid-19 pandemic, the optimal portfolio weight value of BULL.JK 4,99% (0,05%), PSSI.JK 92,01% (34,65%), SMDR.JK 1,40% (31,12%), TMAS.JK 1,29% (30,42%) dan WINS.JK 0,31% (3,77%). Based on the sharpe ratio, the performance of the transportation sector stock portfolio provided a high return compared to risk during the Covid-19 pandemic, from 41.71% to 297.59%, with a level of volatility that tends to be stable before and after the Covid-19 pandemic, from 41.65% to 44.56%. Based on these findings, it can be concluded that the logistics sector in sea transportation can improve performance during the Covid-19 pandemic and is worthy of investment as an investor consideration.
Article Details
References
Badan Pusat Statistik. (2021, February 5). Berita Resmi Statistik. Pertumbuhan Ekonomi Indonesia
Triwulan IV-2020.
Baillie, R., & DeGennaro, R. (1990). Stock Returns and Volatility. The Journal of Financial and
Quantitative Analysis, 25(2), 203.
Bodie, Z., Kane, A., & Marcus, A. (2011). Investments (9th ed). New York: McGraw-Hill.
Fauziyah, & Ersyafdi. (2021). Dampak Covid 19 pada Pasar Saham di Berbagai Negara. Forum
Ekonomi.
Lewinson. (2020). Python for Finance Cookbook. Packt Publishing.
Manurung, A. H. (2000, Nopember 11). Usahawan. Mengukur Kinerja Portofolio, XXIX, pp. 41-46.
Mariana, C., & Patria, H. (2021). Are Electric Vehicle Stocks in ASEAN Countries Investible during
the Covid-19 Pandemic? IOP Conference Series: Earth and Environmental Science,
Forthcoming Paper.
Markowitz, H. (1952). Portfolio Selection. J. Finance, 7, 77-91.
Omane-Adjepong, M., & Alagidede, I. P. (2020, December). Exploration of safe havens for Africa’s
stock markets: A test case under COVID-19 crisis. Financ. Res. Lett.
Rubinstein, Y., & Kroese, D. (2004). The Cross-Entropy Method: A Unified Approach to
Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Information
Science & Statistics.
Sandoval, L., & Franca, I. (2012). Correlation of financial markets in times of crisis. Phys. A Stat.
Mech. its Appl.,, 391, 187-208.
M.E. Berberler, U. Nuriyev, A. Yildirim, A software for the onedimensional cutting stock problem, J.
King Saud Univ. – Sci. 23 (2011) 69–76,
S.A.H. Zaidi, Z. Wei, A. Gedikli, M.W. Zafar, F. Hou, Y. Iftikhar, The impact of globalization, natural
resources abundance, and human capital on financial development: Evidence from thirty-one
OECD countries, Resources Policy 64 (2019) 101476.
Mahboubeh Shadabfar, Longsheng Cheng, Probabilistic approach for optimal portfolio selection using
a hybrid Monte Carlo simulation and Markowitz model. Alexandria Engineering Journal (2020)
xxx, xxx–xxx, Production and hosting by Elsevier B.V.
J. Hursti, M.V. Maula, Acquiring financial resources from foreign equity capital markets: An
examination of factors influencing foreign initial public offerings, J. Bus. Ventur. 22 (2007) 833–
S. Hove, A.T. Mama, F.T. Tchana, Monetary policy and commodity terms of trade shocks in emerging
market economies, Econ. Model. 49 (2015) 53–71,
Musdalifah Aziz, Zainal Ilmi, Yundi Permadi Hakim, Dio Caisar Darma, Muhammad Qodri, Monte
Carlo Simulation’ Predicting on the Movement of Investments – During the Covid Pandemic in
Indonesia, Jurnal Dinamika Manajemen, 12 (2) 2021, 262-274.
H. Markowitz, Portfolio selection, J. Finance 7 (1952) 77–91,
W.F. Sharpe, A simplified model for portfolio analysis, Manage. Sci. 9 (1963) 277–293.
Jaksa Cvitanic, Levon Goukasiana, Fernando Zapaterob, Monte Carlo computation of optimal
portfolios in complete markets, Journal of Economic Dynamics & Control 27 (2003) 971 – 986,
Elsevier Science B.V.
Chijoo Lee, Financing method for real estate and infrastructure development using Markowitz’s
portfolio selection model and the Monte Carlo simulation, Engineering, Construction and
Architectural Management Vol. 26 No. 9, 2019 pp. 2008-2022, Emerald Publishing Limited,
Phelim P. Boyle, Options: A Monte Carlo Approach, Journal of Financial Economics 4 (1977) 323-338.