Portfolio Optimisation in the Cryptocurrency Market: Hybrid Integration of Markowitz and Ridge Methods

Year 2025, Volume: 75 Issue: 1, 207 - 221, 14.07.2025

Rüya Kaplan Yıldırım , Turgay Münyas , Gülden Kadooğlu Aydın

https://doi.org/10.26650/ISTJECON2024-1643134

Abstract

Constructing an effective asset allocation strategy requires building well-diversified portfolios that maintain robust performance beyond the sample data. The classical Markowitz portfolio optimisation, while widely used, is known to suffer from issues such as estimation errors and sensitivity to multicollinearity, which can significantly distort the allocation process and reduce performance reliability. In order to surmount the aforementioned challenges, the incorporation of Machine Learning echniques, specifically Ridge regression, into the portfolio creation process has been effected. This has resulted in the provision of a hybrid model that combines the strengths of Markowitz optimisation and Ridge regression. The integration of these approaches within the hybrid model serves to mitigate the prediction risks while maintaining the diversification benefits inherent to the Markowitz framework. The model was trained using an 80/20 split and cross-validation was employed to prevent overfitting. The findings indicate that this integrated approach attains the maximum Sharpe ratio, thereby significantly enhancing risk-adjusted returns and portfolio stability when applied to cryptoasset returns. The findings emphasise the merits of integrating classical optimisation methodologies with machine learning to develop more robust and adaptive asset allocation strategies. By analysing the impact of high-volatility cryptoassets on portfolio performance, it makes important contributions to both the literature and practical portfolio strategies for investors.

JEL Classification : G11 , G15 , O16

Keywords

Hybrid method , Crypto assets , Markowitz optimisation , Ridge method

References

• Abensur, E. O., & de Carvalho, W. P. (2022). Improving portfolio selection by balancing liquidity-risk-return: Evidence from stock markets. Theoretical Economics Letters, 12(2), 479-497. google scholar
• Al-Yahyaee, K. H., Mensi, W., Al-Jarrah, I. M. W., Hamdi, A., & Kang, S. H. (2019). Volatility forecasting, downside risk, and diversification benefits of Bitcoin and oil and international commodity markets: A comparative analYsis with Yellow metal. The North American Journal of Economics and Finance, 49, 104-120. google scholar
• Al-Yahyaee, K. H., Mensi, W., Al-Jarrah, I. M., Hamdi, A., & Kang, S. H. (2019). Volatility forecasting, downside risk, and diversification benefits of Bitcoin and oil and international commodity markets: A comparative analYysis with Yellow metal. The North American Journal of Economics and Finance, 50, 104-120. google scholar
• Arı, A., & Önder, H. (2013). Farklı Veri Yapılarında Kullanilabilecek Regresyon Yöntemleri. Anadolu Tarım Bilimleri Dergisi, 28(3), 168-174. google scholar
• Asih, K. N., Achsani, N. A., Novianti, T., & Manurung, A. H. (2024). The role of ESG-based assets in generating the dYnamic optimal portfolio in Indonesia. Cogent Business & Management, 11(1), 2382919. google scholar
• Baek, C. and Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1), 30-34. google scholar
• Blay, K. A. (2024). From portfolio selection to portfolio choice: Remembering Harry Markowitz. Journal of Portfolio Management, 50(8). google scholar
• Bouri, E., Gupta, R., Tiwari, A. K. and Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95. google scholar
• Brauneis, A., & Mestel, R. (2019). Cıyptocurrency-portfolios in a mean-variance framework. Finance Research Letters, 28, 259-264. 4.2 google scholar
• Chambers, N., Hamzacebi, C. and BaYramoglu, F. M. (2016). GreY sYstem theory supported Markowitz portfolio optimisation during high volatility periods. The Journal of Grey System, 28(4), 79-95. google scholar
• Chao, X., Tao, X., & Zeng, L. (2019, April). Application of Markowitz’s portfolio theory in obtaining the best portfolio in the stock market. In The First International Symposium on Management and Social Sciences (ISMSS 2019) (pp. 119-122). Atlantis Press. google scholar
• Corbet, S., Meegan, A., Larkin, C., Lucey, B. and Yarovaya, L. (2018). Exploring the dynamic relationships between crYptocurrencies and other financial assets. Economics Letters, 165, 28-34. google scholar
• DickeY, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. google scholar
• DickeY, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057-1072. google scholar
• DYhrberg, A. H. (2016). Hedging capabilities of Bitcoin: Is it the virtual gold? Finance Research Letters, 16, 139-144. google scholar
• Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 55(2), 251-276. 4.2 google scholar
• Feng, W., Wang, Y. and Zhang, Z. (2018). Can cryptocurrencies be a safe haven? A tail risk perspective analysis. Applied Economics, 50(44),4745-4762. google scholar
• Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111-120. google scholar
• Grujic, M., & Soja, T. (2022). Portfolio diversification with Bitcoin: Evidence from institutional investors' perspective. EMC Review— Economy and Market Communication Review, 23(i), 111-125. google scholar
• Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio diversification with virtual currency: Evidence from Bitcoin. International Review of Financial Analysis, 63, 431-437. google scholar
• Gujarati, D. N. (1995). Basic econometrics (3rd ed.). McGraw-Hill. google scholar
• Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12 (i), 55-67. google scholar
• Jankovâ, Z. (2019). Comparison of portfolios using the Markowitz and downside risk theories on the Czech stock market. İn Innovation Management, Entrepreneurship and Sustainability (IMES 2019) (pp. 291-303). VYsokâ skola ekonomickâ v Praze. google scholar
• Kamil, A. A., Fei, C. Y., & Kok, L. K. (2006). Portfolio analYsis based on Markowitz model. Journal of Statistics and Management Systems, 9(3), 519-536. google scholar
• Konuşkan, A., Teker, T., Ömürbek, V., & Bekci, İ. (2019). Kripto Paraların Fiyatları Arasındaki İlişkinin Tespitine Yönelik Bir Araştırma. Süleyman Demirel Üniversitesi iktisadi ve idari Bilimler Fakültesi Dergisi, 24(2), 311-318. google scholar
• Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analYsis. PLoS One, 10(4), e0123923. google scholar
• Liu, Y., & TsYVİnski, A. (2021). Risks and returns of cıyptocurrencY. The Review of Financial Studies, 34(6), 2689-2727 google scholar
• Markowitz, H. (1952). The utility of wealth. Journal of Political Economy, 60(2), 151-158. google scholar
• Mazanec, J. (2021). Portfolio optimalization on digital currency market. Journal of Risk and Financial Management, 14(4), 160. google scholar
• Platanakis, E., & Urquhart, A. (2019). Portfolio management with cryptocurrencies: The role of estimation risk. Economics Letters, 177, 76-80. google scholar
• Raisa, M. L., & Cristian, M. M. (2021). Using Markowitz theory in the construction of a financial portfolio: Application on Bucharest Stock Exchange. Revista Economicâ, 100, 98-110. google scholar
• Saggu, A., Ante, L., & Kopiec, K. (2025). Uncertain regulations, definite impacts: The impact of the US Securities and Exchange Commission's regulatory interventions on crypto assets. Finance Research Letters, 72, 106413. google scholar
• Savage, S., & Ball, B. (2024). The Markowitzatron: From modern portfolio theory to modern petroleum theorY. Journal of Portfolio Management, 50(8). google scholar
• SisaY, K. (2024). Wellbeing, productivitY, and food securitY effects of multiple livelihood diversifications: Insight from Kaffa Zone, Ethiopia. Cogent Social Sciences, 10(1), 2282211. https://doi.org/ 10.1080/23311886.2023.2282211 google scholar
• Skrinjaric, T., & Sostaric, N. (2014). The complementarity of Markov chains methodology and Markowitz portfolio optimisation model. Ekonomska miso i praksa, 23(i), 353-370. google scholar
• Soja, T., & Senarathne, C. W. (2019). Bitcoin in portfolio diversification: The perspective of a global investor. Bankarstvo, 48(4), 44-63. google scholar
• Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288. google scholar
• Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31-43). Elsevier. google scholar
• Yıldırım, M. (2019). Blok zincir teknolojisi, kripto paralar ve ülkelerin kripto paralara Yaklaşımları. Bartın Üniversitesi iktisadi ve idari Bilimler Fakültesi Dergisi, 10(20), 265-277. google scholar
• Yiğiter, Ş.Y., & Akkaynak, B. (2017). Modern portföy teorisi: Alternatif Yatırım araçları ile bir uygulama. Kahramanmaraş Sütçü imam Üniversitesi Sosyal Bilimler Dergisi, 14(2), 285-300. google scholar