Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.

Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks

We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks. The aim of the review is to gather in one place the relevant material from different fields, e.g. machine learning, information geometry, econophysics, statistical physics, econometrics, behavioral finance. We hope it will help researchers to use more effectively this alternative modeling of the financial time series. Decision makers and quantitative researchers may also be able to leverage its insights. Finally, we also hope that this review will form the basis of an open toolbox to study correlations, hierarchies, networks and clustering in financial markets.

A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets

Explainability of artificial intelligence methods has become a crucial issue, especially in the most regulated fields, such as health and finance. In this paper, we provide a global explainable AI method which is based on Lorenz decompositions, thus extending previous contributions based on variance decompositions. This allows the resulting Shapley-Lorenz decomposition to be more generally applicable, and provides a unifying variable importance criterion that combines predictive accuracy with explainability, using a normalised and easy to interpret metric. The proposed decomposition is illustrated within the context of a real financial problem: the prediction of bitcoin prices.

https://air.unimi.it/bitstream/2434/778573/2/EXPERT_SYSTEMS_WITH_APPLICATIONS_2020.pdf

Shapley-Lorenz eXplainable Artificial Intelligence

/pdf/fintech-risk-management-a-research-challenge-for-artificial-3kwkl1vo3q.pdf

Fintech Risk Management: A Research Challenge for Artificial Intelligence in Finance.

The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain their credit score and, therefore, to predict their future behaviour.

/pdf/explainable-machine-learning-in-credit-risk-management-44abfgkgei.pdf

Explainable Machine Learning in Credit Risk Management

The paper proposes an explainable AI model that can be used in fintech risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model employs Shapley values, so that AI predictions are interpreted according to the underlying explanatory variables. The empirical analysis of 15,000 small and medium companies asking for peer to peer lending credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain and understand their credit score and, therefore, to predict their future behavior.

/pdf/explainable-ai-in-fintech-risk-management-594tmoncav.pdf

Explainable AI in Fintech Risk Management

A processor-based analytical system accesses financial data for a group of assets over a plurality of time periods and identifies relationship characteristics for the financial data during those time periods. The system compares the relationship characteristics to compute pair-wise similarity measures, which are used to group different time periods into states. The system also accesses information identifying an investment portfolio, such as information identifying particular assets in that portfolio as well as the weightings of those assets in the portfolio. The system evaluates the performance of the portfolio in the states. The system alters the investment portfolio by altering the weightings of the assets in the investment portfolio in order to achieve a predetermined investment goal.

System and method for risk management and portfolio optimization

In this paper, we present a framework for detecting distinct correlation regimes and analyzing the emerging state dependences for a multi-asset futures portfolio from 1998 to 2013. These correlation regimes have been significantly different since the financial crisis of 2008 than they were previously; cluster tracking shows that asset classes are now less separated. We identify distinct “risk-on” and “risk-off” assets with the help of correlation networks. In addition to visualizing, we quantify these observations using suitable metrics for the clusters and correlation networks. The framework will be useful for financial risk management, portfolio construction, and asset allocation.

Handling risk-on/risk-off dynamics with correlation regimes and correlation networks

In this paper, we present a framework for detecting distinct correlation regimes and analyzing the emerging state dependences for a multi-asset futures portfolio from 1998 to 2013. These correlation regimes have been significantly different since the financial crisis of 2008 than they were previously; cluster tracking shows that asset classes are now less separated.We identify distinct “risk-on” and “risk-off” assets with the help of correlation networks. In addition to visualizing, we quantify these observations using suitable metrics for the clusters and correlation networks.The framework will be useful for financial risk management, portfolio construction, and asset allocation.

Jochen Papenbrock

Papers

Explainable Machine Learning in Credit Risk Management

Explainable AI in Fintech Risk Management

System and method for risk management and portfolio optimization

Handling risk-on/risk-off dynamics with correlation regimes and correlation networks

Handling Risk On/Risk Off Dynamics with Correlation Regimes and Correlation Networks