Interpretable Machine Learning for Diversified Portfolio Construction

Jaeger, Markus; Krügel, Stephan; Marinelli, Dimitri; Papenbrock, Jochen; Schwendner, Peter

doi:10.2139/ssrn.3730144

Cited by 5 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, HEW can be modified to also account for the asset volatilities. Jaeger et al (2021) confirm the robustness of HRP for a multi-asset futures universe. It minimizes the variance, but not in too concentrated a way, as can be observed with the minimum variance approach.…”

Section: Origins Of the Hierarchical Approachessupporting

confidence: 67%

“…Recent approaches for simulating realistic financial correlation matrixes explicitly address hierarchy as stylized facts (see Huettner and Mai 2019;Marti 2019;Jaeger et al 2021;Papenbrock et al 2021). Modeling the correlation hierarchy of markets has also been used for the recognition of market regimes (Papenbrock and Schwendner 2015).…”

Section: Fall 2021mentioning

confidence: 99%

“…We rely on a multi-asset universe of equity index, sovereign bond, and commodity futures from May 3, 2000 to June 30, 2020 with a daily frequency, as by Jaeger et al (2021) and Papenbrock et al (2021). Exhibit 2 shows the Bloomberg tickers, asset classes, currency, and names of the 17 futures markets.…”

Section: Empirical Studymentioning

confidence: 99%

“…The overall ambition to develop robust and transparent investment strategies requires several additional building blocks based on intelligent analytics. Together with the procedure to compute synthetic correlation matrixes provided by Papenbrock et al (2021) and the procedure to deliver local and global explanations using the SHAP framework discussed by Jaeger et al (2021), this article addresses variations of the allocation step. Thus, it complements the resulting workflow of a triple AI approach for robust, adaptive, risk-based portfolio construction.…”

Section: Outlook: Synthetic Data and Explainable MLmentioning

confidence: 99%

See 3 more Smart Citations

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

Schwendner

Papenbrock²,

Jaeger³

et al. 2021

JFDS

Self Cite

View full text Add to dashboard Cite

Section: Origins Of the Hierarchical Approachessupporting

confidence: 67%

Section: Fall 2021mentioning

confidence: 99%

Section: Empirical Studymentioning

confidence: 99%

Section: Outlook: Synthetic Data and Explainable MLmentioning

confidence: 99%

See 2 more Smart Citations

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

Schwendner

Papenbrock²,

Jaeger³

et al. 2021

JFDS

Self Cite

View full text Add to dashboard Cite

“…The "non-linear" reaction of markets is amplified by pro-cyclical risk management systems comparing the current realised volatility with long-term historical volatility and forcing to unwind positions in stressed situations (Packham et al, 2017). These risk management systems are prevalent both in banks in the form of (conditional) value-atrisk and at investment funds in the form of target volatility concepts (Jaeger et al, 2020).…”

Section: Challenges Related To the Number Of Participantsmentioning

confidence: 99%

The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets

Posth

Kotlarz

Misheva

et al. 2021

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

The central research question to answer in this study is whether the AI methodology of Self-Play can be applied to financial markets. In typical use-cases of Self-Play, two AI agents play against each other in a particular game, e.g., chess or Go. By repeatedly playing the game, they learn its rules as well as possible winning strategies. When considering financial markets, however, we usually have one player—the trader—that does not face one individual adversary but competes against a vast universe of other market participants. Furthermore, the optimal behaviour in financial markets is not described via a winning strategy, but via the objective of maximising profits while managing risks appropriately. Lastly, data issues cause additional challenges, since, in finance, they are quite often incomplete, noisy and difficult to obtain. We will show that academic research using Self-Play has mostly not focused on finance, and if it has, it was usually restricted to stock markets, not considering the large FX, commodities and bond markets. Despite those challenges, we see enormous potential of applying self-play concepts and algorithms to financial markets and economic forecasts.

show abstract