Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios

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

doi:10.2139/ssrn.3663220

Cited by 6 publications

(9 citation statements)

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“…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%

“…A market generator to create synthetic time series or synthetic correlation data for Monte Carlo simulation, both serving as appropriate input to the HRP variations. Examples are matrix evolutions (Papenbrock et al 2021) and CorrGAN (Marti 2019). 2.…”

Section: Outlook: Synthetic Data and Explainable MLmentioning

confidence: 99%

“…3. Explainable ML to explain and understand the risk and performance attributions of single strategies and the drivers of a performance ranking in terms of features of the synthetic time series, as described by Jaeger et al (2021) and Papenbrock et al (2021), using a model-agnostic explanation method such as the SHAP framework.…”

Section: Outlook: Synthetic Data and Explainable MLmentioning

confidence: 99%

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Section: Fall 2021mentioning

confidence: 99%

Section: Empirical Studymentioning

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Explainable artificial intelligence (XAI) in finance: a systematic literature review

Černevičienė,

Kabašinskas

2024

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As the range of decisions made by Artificial Intelligence (AI) expands, the need for Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific outcomes of complex and opaque financial models requires a thorough justification to improve risk assessment, minimise the loss of trust, and promote a more resilient and trustworthy financial ecosystem. This Systematic Literature Review (SLR) identifies 138 relevant articles from 2005 to 2022 and highlights empirical examples demonstrating XAI's potential benefits in the financial industry. We classified the articles according to the financial tasks addressed by AI using XAI, the variation in XAI methods between applications and tasks, and the development and application of new XAI methods. The most popular financial tasks addressed by the AI using XAI were credit management, stock price predictions, and fraud detection. The three most commonly employed AI black-box techniques in finance whose explainability was evaluated were Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. Most of the examined publications utilise feature importance, Shapley additive explanations (SHAP), and rule-based methods. In addition, they employ explainability frameworks that integrate multiple XAI techniques. We also concisely define the existing challenges, requirements, and unresolved issues in applying XAI in the financial sector.

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Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios

Cited by 6 publications

References 0 publications

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

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

cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope

Explainable artificial intelligence (XAI) in finance: a systematic literature review

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