Key Points:
– Financial crises are characterized by internal vulnerabilities that worsen over time and trigger a crisis.
– Classical methods, such as logistic regressions, have been used to predict financial crises.
– Machine learning algorithms offer new approaches, such as unsupervised learning and clustering.
– While machine learning has its weaknesses, it offers significant benefits for forecasting financial crises.
Financial crises come in various forms, including sovereign defaults, bank runs, and currency crises. These crises share a common characteristic of internal vulnerabilities that worsen until a triggering event causes a financial crisis.
Identifying the specific trigger for a financial crisis can be challenging, so monitoring the evolution of internal vulnerabilities is crucial. These internal vulnerabilities are the explanatory variables in crisis models and often serve as the response variable in historical crisis episodes.
The classical approach to modeling financial crises involves using logistic regressions to estimate the probability of a crisis. Explanatory variables are linked to the response variable using a non-linear link function. This approach relies on the definition of a financial crisis and utilizes maximum likelihood estimation to determine the relationship between explanatory variables and the response variable.
Machine learning algorithms offer alternative methods for modeling financial crises. One technique is unsupervised learning, which does not require a response variable. Clustering is an example of unsupervised learning, where data points are grouped in a meaningful way. Clustering can be applied to both dependent and independent variables to derive insights from different clusters.
Machine learning algorithms have the potential to significantly improve the estimation of financial crisis probabilities. While machine learning has its limitations, such as the challenge of splitting time series into training and test sets, the benefits outweigh these shortcomings.
It’s important to invest in the capabilities of machine learning algorithms, as they can enhance the accuracy and efficiency of forecasting financial crises.
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All posts are the opinion of the author and do not constitute investment advice. The views expressed may not necessarily reflect those of CFA Institute or the author’s employer.
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