Comparative Multidimensional Risk Analysis of Stock and Cryptocurrency Returns Using Factor Analysis and Logistic Regression

Mohammad Sufa Ammar Habibi; Amri Muhaimin; Muhammad Nasrudin

doi:10.15408/jti.v19i2.50858

Authors

Mohammad Sufa Ammar Habibi Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya, Indonesia
Amri Muhaimin Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya, Indonesia
Muhammad Nasrudin Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya, Indonesia

DOI:

https://doi.org/10.15408/jti.v19i2.50858

Keywords:

Comparative risk analysis, Stocks, Cryptocurrency assets, Factor analysis, Logistic regression, Portfolio optimization

Abstract

This study presents a comparative multidimensional risk analysis of IDX30 stocks and CMC100 cryptocurrencies to examine whether both asset classes exhibit fundamentally different empirical risk-return structures beyond conventional single-indicator measures such as volatility or average return. Daily log returns of 30 IDX30 stocks and 30 CMC100 cryptocurrencies from July 2023 to February 2026 were analyzed using eleven statistical indicators representing distribution moments and extreme tail behavior. Factor analysis was employed to reduce these indicators into latent risk dimensions, followed by binary logistic regression to classify the two asset classes. Portfolio optimization was conducted using the Mean Absolute Deviation approach, while extreme downside risk was evaluated using Historical Value at Risk. The analysis extracted two dominant latent factors, namely the Tail Factor and the Moment Factor, explaining 85.70% of the total variance. Logistic regression identified the Tail Factor as the main distinguishing variable, with confusion matrix evaluation correctly classifying 56 of 60 assets and achieving an accuracy rate of 93.33%. Portfolio optimization results show that a diversified portfolio combining stocks and cryptocurrencies achieved the highest Sharpe ratio, indicating superior risk-return efficiency compared with single-asset portfolios. However, the portfolio remained exposed to significant tail risk under extreme market conditions.

References

[1] L. Liestyowati, E. Sudarmanto, H. Ramadhani, S. Rijal, and T. W. Nurdiani, “Tren Investasi Aset Digital: Studi tentang Perilaku Investor Muda terhadap Cryptocurrency di Tengah Perubahan Pasar Keuangan di Kota Bandung,” Jurnal Akuntansi Dan Keuangan West Science, vol. 2, no. 03, pp. 142–149, Sep. 2023, doi: 10.58812/jakws.v2i03.639.

[2] X. Zhang, “The Impact of Macroeconomic Factors on Stock Valuation,” Highlights in Business, Economics and Management, vol. 41, pp. 513–519, Oct. 2024, doi: 10.54097/4y6fm697.

[3] D. Sharma, D. Pant, and A. Kumar, “Cryptocurrency: An Overview of its History, Technology and Future Prospects,” International Journal of Advanced Research in Science, Communication and Technology, vol. 3, no. 3, pp. 427–430, Apr. 2023, doi: 10.48175/IJARSCT-9232.

[4] V. Dobrynskaya and M. Dubrovskiy, “Cryptocurrencies Meet Equities: Risk Factors and Asset Pricing Relationships,” SSRN Electronic Journal, 2021, doi: 10.2139/ssrn.3877248.

[5] T. Lan and M. Frömmel, “Risk factors in cryptocurrency pricing,” International Review of Financial Analysis, vol. 105, p. 104389, Sep. 2025, doi: 10.1016/j.irfa.2025.104389.

[6] P. Bruhn and D. Ernst, “Assessing the Risk Characteristics of the Cryptocurrency Market: A GARCH-EVT-Copula Approach,” Journal of Risk and Financial Management, vol. 15, no. 8, Aug. 2022, doi: 10.3390/jrfm15080346.

[7] H. Ahmadi, “Is Cryptocurrency Risky as An Investment Instrument? Analysis of Return and Risk with A Comparison of Sharia Stocks,” International Journal of Islamic Business Ethics, vol. 8, no. 1, p. 40, Mar. 2023, doi: 10.30659/ijibe.8.1.40-53.

[8] Z. Z. Hamzah, Mujito, Herman, and W. A. Basudani, “Comparative Performance Analysis of Bitcoin, LQ45 Stocks, and Antam Gold as Investment Options,” Jurnal Ilmiah Manajemen Kesatuan, vol. 13, no. 5, pp. 3903–3914, Oct. 2025, doi: 10.37641/jimkes.v13i5.3827.

[9] A. H. L. Rachmawati and D. I. A. Maruddani, “Pembentukan Portofolio Saham Optimal Dengan Mean Absolute Deviation Pada Data Saham Jakarta Islamic Index,” Jurnal Gaussian, vol. 11, no. 2, pp. 237–247, 2022, [Online]. Available: https://ejournal3.undip.ac.id/index.php/gaussian/

[10] A. N. Pratama, N. Satyahadewi, and E. Sulistianingsih, “Analysis Of Optimal Portfolio Formation On Idx30 Indexed Stock With The Mean Absolute Deviation Method,” Barekeng, vol. 18, no. 3, pp. 1753–1764, Sep. 2024, doi: 10.30598/barekengvol18iss3pp1753-1764.

[11] B. Castillo-Brais, Á. León, and J. Mora, “Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?,” Mathematics, vol. 10, no. 22, Nov. 2022, doi: 10.3390/math10224329.

[12] D. Likitratcharoen, P. Chudasring, C. Pinmanee, and K. Wiwattanalamphong, “The Efficiency of Value-at-Risk Models during Extreme Market Stress in Cryptocurrencies,” Sustainability, vol. 15, no. 5, Mar. 2023, doi: 10.3390/su15054395.

[13] I. N. W. Negara, Y. A. R. Langi, and T. Manurung, “Analisis Portofolio Saham Model Mean – Variance Markowitz Menggunakan Metode Lagrange,” d’CARTESIAN, vol. 9, no. 2, p. 173, Jan. 2021, doi: 10.35799/dc.9.2.2020.29656.

[14] Md. Siraj-Ud-Doulah, “An Alternative Measures of Moments Skewness Kurtosis and JB Test of Normality,” Journal of Statistical Theory and Applications, vol. 20, no. 2, p. 219, 2021, doi: 10.2991/jsta.d.210525.002.

[15] J. H. T. Kim and H. Kim, “Estimating Skewness and Kurtosis for Asymmetric Heavy-Tailed Data: A Regression Approach,” Mathematics, vol. 13, no. 16, p. 2694, Aug. 2025, doi: 10.3390/math13162694.

[16] A. Karagiorgis, A. Ballis, and K. Drakos, “The Skewness-Kurtosis plane for cryptocurrencies’ universe,” International Journal of Finance and Economics, vol. 29, no. 2, pp. 2543–2555, Apr. 2024, doi: 10.1002/ijfe.2795.

[17] W. Liu, X. Liang, and G. Cui, “Common risk factors in the returns on cryptocurrencies,” Economic Modelling, vol. 86, pp. 299–305, Mar. 2020, doi: 10.1016/j.econmod.2019.09.035.

[18] Z. Liu, X. Shi, P. Zhai, S. Wu, Z. Ding, and Y. Zhou, “Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach,” Resources Policy, vol. 74, p. 102381, Dec. 2021, doi: 10.1016/j.resourpol.2021.102381.

[19] F. Iqbal, M. Zahid, and D. Koutmos, “Cryptocurrency Trading and Downside Risk,” Risks, vol. 11, no. 7, p. 122, Jul. 2023, doi: 10.3390/risks11070122.

[20] I. Yousaf, Y. Riaz, and F. Li, “Bitcoin and crypto-mining stocks: A quantile connectedness approach,” Green Finance, vol. 7, no. 3, pp. 450–480, 2025, doi: 10.3934/GF.2025017.

[21] M. I. Contreras-Valdez, S. Sahu, J. A. Núñez-Mora, and R. J. Santillán-Salgado, “Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails,” Risks, vol. 12, no. 3, 2024, doi: 10.3390/risks.

[22] M. Tavakol and A. Wetzel, “Factor Analysis: a means for theory and instrument development in support of construct validity,” International Journal of Medical Education, vol. 11, pp. 245–247, Nov. 2020, doi: 10.5116/ijme.5f96.0f4a.

[23] S. D. You and M.-J. Hung, “Comparative Study of Dimensionality Reduction Techniques for Spectral-Temporal Data,” Information, vol. 12, no. 1, 2020, doi: 10.3390/info12.

[24] E. Szafraniec-Siluta, R. Ardan, A. Strzelecka, and D. Zawadzka, “Application of factor analysis to reduce the dimensionality of the determinants of equity capital return on European Union farms,” in Procedia Computer Science, Elsevier B.V., 2023, pp. 4433–4442. doi: 10.1016/j.procs.2023.10.441.

[25] A. K. Shalahuddin and E. D. Arfianto, “Impact Of Past Return, Turnover, Volatility, Bid-Ask Spread On Disposition Effect (Evidence On Small Capital Stock Listed In Indonesian Stock Exchange In 2016-2020),” DIPONEGORO JOURNAL OF MANAGEMENT, vol. 11, no. 1, 2022, [Online]. Available: https://ejournal3.undip.ac.id/index.php/djom/index

[26] T. G. Fischer, C. Krauss, and A. Deinert, “Statistical Arbitrage in Cryptocurrency Markets,” Journal of Risk and Financial Management, vol. 12, no. 1, Mar. 2019, doi: 10.3390/jrfm12010031.

[27] H. Konno and H. Yamazaki, “Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market,” Management Science, vol. 37, no. 5, pp. 519–531, May 1991, doi: 10.1287/mnsc.37.5.519.

[28] W. S. Lam, W. H. Lam, and S. H. Jaaman, “Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model,” Entropy, vol. 23, no. 10, p. 1266, Sep. 2021, doi: 10.3390/e23101266.

[29] M. Anugrahayu and U. Azmi, “Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping,” Jurnal Matematika, Statistika dan Komputasi, vol. 20, no. 1, pp. 164–183, Sep. 2023, doi: 10.20956/j.v20i1.27755.

[30] M. P. A. Saputra, Sukono, and D. Chaerani, “Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method,” Risks, vol. 10, no. 1, p. 10, Jan. 2022, doi: 10.3390/risks10010010.

[31] D. Likitratcharoen, P. Chudasring, C. Pinmanee, and K. Wiwattanalamphong, “The Efficiency of Value-at-Risk Models during Extreme Market Stress in Cryptocurrencies,” Sustainability, vol. 15, no. 5, p. 4395, Mar. 2023, doi: 10.3390/su15054395.

[32] P. Prakash, V. Sangwan, and K. Singh, “Transformational Approach to Analytical Value-at-Risk for near Normal Distributions,” Journal of Risk and Financial Management, vol. 14, no. 2, p. 51, Jan. 2021, doi: 10.3390/jrfm14020051.

[33] F. Bagheri, D. Reforgiato Recupero, and E. Sirnes, “Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation,” Data (Basel)., vol. 8, no. 8, p. 133, Aug. 2023, doi: 10.3390/data8080133.

[34] C. López-Martín, R. Arguedas-Sanz, and S. B. Muela, “A cryptocurrency empirical study focused on evaluating their distribution functions,” International Review of Economics & Finance, vol. 79, pp. 387–407, May 2022, doi: 10.1016/j.iref.2022.02.021.

[35] N. A. Zakaria, M. H. Mahbob, and M. R. Johori, “Pembentukan Instrumen Pengukuran Perancangan Strategik: Pendekatan Analisis Faktor Penerokaan,” Jurnal Komunikasi: Malaysian Journal of Communication, vol. 40, no. 3, pp. 168–185, Sep. 2024, doi: 10.17576/JKMJC-2024-4003-10.

[36] W. M. A. Ahmed and M. Al Mafrachi, “Do higher-order realized moments matter for cryptocurrency returns?,” International Review of Economics & Finance, vol. 72, pp. 483–499, Mar. 2021, doi: 10.1016/j.iref.2020.12.009.

[37] Y. Liu and A. Tsyvinski, “Risks and Returns of Cryptocurrency,” Review of Financial Studies, vol. 34, no. 6, pp. 2689–2727, May 2021, doi: 10.1093/rfs/hhaa113.

[38] Y. Liu and Y. Chen, “Skewness risk and the cross-section of cryptocurrency returns,” International Review of Financial Analysis, vol. 96, p. 103626, Nov. 2024, doi: 10.1016/j.irfa.2024.103626.