Adaptive Seriational Risk Parity and Other Extensions for Heuristic Portfolio Construction Using Machine Learning and Graph Theory

Enhancing portfolio management using artificial intelligence: literature review

“…Equal Risk Contribution (ERC) against statistical bootstrapped features applying Shapley framework showing insightful explanations. Schwendner et al ( 2021 ) present a conceptual framework named Adaptive Seriational Risk Parity (ASRP) to extend HRP as an asset allocation heuristic using the SHAP framework to explain the resulting performance with features of synthetic market data. Also, referring to synthetic data, Papenbrock et al ( 2021 ) evaluates three competing machine learning methods to regress the portfolio risk spread between both allocation methods against statistical features of the synthetic correlation matrices and then discusses the local and global feature importance using the SHAP framework.…”

Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation

“…Following Mantegna (1999), Papenbrock and Schwendner (2015), and López de Prado (2016), we transform the empirical correlation matrix C into a distance matrix using the metric d C 2(1 ) = − . Alternatives to single-link-age clustering are other static or adaptive tree-based clustering algorithms or more general seriation methods as discussed in Schwendner et al (2021). For a review of correlations, hierarchies, networks, and clustering in financial markets, see Marti et al (2021).…”

“…An alternative to the single-linkage clustering step used in this article would be clustering methods based on the signatures (Bilokon, Jacquier, and McIndoe 2021) of the time series or other clustering alternatives as discussed in (Schwendner et al 2021). These approaches also could motivate portfolio construction schemes derived from signature distances (signature-HRP) rather than from correlation distances exclusively (López de Prado 2016).…”

“…It is, thus, essential to develop data-generation procedures that can capture the complexities of entire financial markets. Potential applications of synthetic financial data include hedging Kim 2021), model calibration (Cuchiero, Khosrawi, and Teichmann 2020), and portfolio construction (Jaeger et al 2021;Schwendner et al 2021). López de Prado (2020) offered an overview of synthetic data generation structured into resampling and Monte Carlo methods.…”