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Abstract
The modeling of stock prices for telecommunications companies in Indonesia (TBIG.JK, TLKM.JK, XL.JK, ISAT.JK, TOWR.JK) is examined in this study by considering three time periods: 1 year, 5 years, and 10 years. The analysis results indicate that the distribution of stock prices for each company and time period varies, with some stocks exhibiting a distribution close to normal while others show high kurtosis. These findings suggest that the assumption of normal distribution may not be appropriate for all cases, making it essential to select a stock price prediction model that takes into account the specific distribution characteristics for each company and time period
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Copyright (c) 2024 Hubbi Muhammad, Pramesti Melyna Mustofa
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References
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References
A. Hoyyi, D. Rosadi, and Abdurakhman, “Daily stock prices prediction using variance gamma model,” Journal of Mathematical and Computational Science, vol. 11, no. 2, pp. 1888–1903, 2021, doi: 10.28919/jmcs/5469.
I. A. Ilyas, E. Puspita, and D. Rachmatin, “Prediksi Harga Saham Menggunakan Model Jump Diffusion,” EurekaMatika, vol. 6, no. 1, 2018.
E. F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” J Finance, vol. 25, no. 2, pp. 383–417, 1970, doi: https://doi.org/10.2307/2325486.
J. Yang, M. Zhang, S. Feng, X. Zhang, and X. Bai, “A hierarchical deep model integrating economic facts for stock movement prediction,” Eng Appl Artif Intell, vol. 133, Jul. 2024, doi: 10.1016/j.engappai.2024.108320.
D. B. Madan and E. Seneta, “The Variance Gamma (V.G.) Model for Share Market Returns,” The Journal of Business, vol. 63, no. 4, p. 511, Jan. 1990, doi: 10.1086/296519.
D. B. Madan, P. P. Carr, and E. C. Chang, “The Variance Gamma Process and Option Pricing,” European Finance Review, vol. 2, pp. 79–105, 1998.
D. J. Higham, An Intoduction to Financial Option Valuation: Mathematics, Stochastics and Computation. Cambridge: Cambridge University Press, 2004. [Online]. Available: www.cambridge.org.
A. N. Avramidis, P. L’ecuyer, and P.-A. Tremblay, “Efficient Simulation of Gamma and Variance-Gamma Processes,” in Proceedings of the 2003 Winter Simulation Conference, 2003, pp. 319–326. doi: 10.1109/WSC.2003.1261439.
B. H. P. Paskalia, R. C. Nugraha, and D. B. Wirawan, “Variance Gamma Model and Its Development for Stocks Call Option Prices Estimation,” International Journal of Financial and Investment Studies (IJFIS), vol. 3, no. 1, pp. 43–51, Aug. 2022, doi: 10.9744/ijfis.3.1.43-51.
S. Romida Harahap, “Deteksi Dini Krisis Nilai Tukar Indosesia: Identifikasi Variabel Makro Ekonomi,” JEJAK Journal of Economics and Policy, vol. 6, no. 1, pp. 17–28, 2013, doi: 10.15294/jejak.v6i1.3745.
J. Hull, Options, Futures and Other Derivatives, 5th ed. New Jersey : Prentice Hall Int., 2003.
L. J. Bain and M. Engelhardt, Introduction to Probability and Mathematical Statistics, Second Edition. Brooks/Cole, 1992.
S. A. Klugman, H. H. Panjer, and G. E. Willmot, Loss Models: From Data to Decisions. New Jersey: John Wiley & Sons, Inc., 2012.
P. Jorion, “American Finance Association Wiley Predicting Volatility in the Foreign Exchange Market,” Source: The Journal of Finance, vol. 50, no. 2, pp. 507–528, 1995, doi: http://www.jstor.orgURL:http://www.jstor.org/stable/2329417.
E. F. Fama and K. R. French, “Common risk factors in the returns on stocks and bonds,” Journal of Financial Economic , vol. 33, pp. 3–56, 1993.
K. G. Rouwenhorst, “Local return factors and turnover in emerging stock markets,” Journal of Finance, vol. 54, no. 4, pp. 1439–1464, 1999, doi: 10.1111/0022-1082.00151.