QUANTITATIVE WEALTH AND INVESTMENT MANAGEMENT (QWIM) ADVANCED PORTFOLIO DIVERSIFICATION
Abstract
Within the realm of Portfolio Diversification, the classic Mean-Variance (MV) Analysis framework faces scrutiny for its stringent assumptions and the high propensity for creating portfolios that are heavily concentrated in a limited selection of assets. Our study explores various Advanced Portfolio Diversification (APD) techniques Hierarchical Clustering (HC) with Hierarchical Principal Component Analysis (HPCA) and Dynamic Time Warping (DTW) to address the inherent estimation challenges associated with MV. These innovative methods refine the diversification process by utilizing the hierarchical and centroid clustering network approaches, thereby mitigating most of the problems incurred by the MV framework and problematic portfolios. When tested against various diversification models and anchoring strategies, these diversified portfolio management methodologies demonstrate superior performance over the traditional Mean-Variance (MV) strategy in the long-term horizon, across multiple risk-adjusted evaluation metrics. The reasons for this result are twofold: (1) the more diverse weight allocation of HC models, and (2) the flexibility of HC models in selecting different risk measures.
References
Avellaneda, M. (2020). Hierarchical pca and applications to portfolio management. Revista mexicana de econom´ıa y finanzas, 15(1):1–16.
Braga, M. D., Nava, C. R., and Zoia, M. G. (2023). Kurtosis-based risk parity: methodology and portfolio effects. Quantitative Finance, 23(3):453–469.
Chen, Z.-p. and Zhao, C.-e. (2003). Sensitivity to estimation errors in mean-variance models. Acta Mathematicae Applicatae Sinica, 19(2):255–266.
Chua, D. B., Kritzman, M., and Page, S. (2009). The myth of diversification. The Journal of Portfolio Management, 36(1):26–35.
Eichner, T. (2008). Mean variance vulnerability. Management Science, 54(3):586–593.
Fusai, G., Mignacca, D., Nardon, A., and Human, B. (2020). Equally diversified or equally weighted? Available at SSRN 3628585.
Ibanez, F. A. (2023). Diversified spectral portfolios: An unsupervised learning approach to diversification. Journal of Financial Data Science, 5(2).
Jaeger, M., Krugel, S., Marinelli, D., Papenbrock, J., and Schwendner, P. (2021). Under-¨ standing machine learning for diversified portfolio construction by explainable ai. Markus Jaeger, Stephan Krugel, Dimitri Marinelli, Jochen Papenbrock and Peter Schwendner. Inter-¨ pretable Machine Learning for Diversified Portfolio Construction. The Journal of Financial Data Science Summer.
Kinlaw, W. B., Kritzman, M., and Turkington, D. (2023). Co-occurrence: A new perspective on portfolio diversification.
Lim, T. and Ng, H. (2022). Financial portfolio management based on shaped-based unsupervised machine learning: A dynamic time warping baycenter averaging approach to international markets and periods of downside event risks. The Journal of Investing.
Lim, T. and Ong, C. S. (2021). Portfolio diversification using shape-based clustering. Journal of Financial Data Science, 3(1):111.
Lopez de Prado, M. (2016). Building diversified portfolios that outperform out-of-sample. Journal of Portfolio Management.
Ma, Y., Han, R., and Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165:113973.
Markowitz, H. M. (1989). Mean—variance analysis. Finance, pages 194–198.
Martellini, L. and Milhau, V. (2018). Proverbial baskets are uncorrelated risk factors! a factor-based framework for measuring and managing diversification in multi-asset investment solutions. Journal of Portfolio Management, 44(2):8–22.
Millea, A. and Edalat, A. (2022). Using deep reinforcement learning with hierarchical risk parity for portfolio optimization. International Journal of Financial Studies, 11(1):10.
Pfitzinger, J. and Katzke, N. (2023). Perspectives on optimal hierarchical portfolio selection. Perspectives on Optimal Hierarchical Portfolio Selection (April 4, 2023).
Raffinot, T. (2018). The hierarchical equal risk contribution portfolio. Available at SSRN 3237540.
Schwendner, P., Papenbrock, J., Jaeger, M., and Krugel, S. (2021). Adaptive seriational risk¨ parity and other extensions for heuristic portfolio construction using machine learning and graph theory. The Journal of Financial Data Science, 3(4):65–83.
Serur, J. A. and Avellaneda, M. (2020). Hierarchical pca and modeling asset correlations. Available at SSRN 3903460.
Song, W.-M., Di Matteo, T., and Aste, T. (2012). Hierarchical information clustering by means of topologically embedded graphs. PloS one, 7(3):e31929.
Sood, S., Papasotiriou, K., Vaiciulis, M., and Balch, T. (2023). Deep reinforcement learning for optimal portfolio allocation: A comparative study with mean-variance optimization. FinPlan 2023, page 21.
Tibshirani, R., Walther, G., and Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2):411–423.
Tsiang, S.-C. (1989). The rationale of the mean-standard deviation analysis, skewness preference, and the demand for money. In Finance Constraints and the Theory of Money, pages 221–248. Elsevier.
Yuan, M. and Zhou, G. (2022). Why naive diversification is not so naive, and how to beat it? Journal of Financial and Quantitative Analysis, pages 1–32.
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