Forecasting Market Crashes with Machine Learning Techniques

Predicting market direction is challenging, and forecasting market crashes is even more difficult, yet this remains a growing area of research. We previously discussed market correction prediction, and Reference [1] continues this line of inquiry by examining how machine learning can be used to predict market crashes within the Adaptive Market Hypothesis framework.

The study considers three categories of factors:

1. Internal factors, such as technical indicators designed to capture endogenous market dynamics, including momentum, trend strength, and money flow arising from investor behavior and adaptive learning;
2. External factors, including macroeconomic and commodity variables that proxy for systematic, exogenous risks affecting fundamental valuations; and
3. Volatility features that quantify market fear and uncertainty.

The authors evaluate the performance of three predictive models—logistic regression, random forest, and a long short-term memory (LSTM) network. They pointed out,

The findings of this thesis suggest that while market crashes remain inherently difficult to forecast with perfect accuracy, they are not entirely random events. Meaningful predictive signals do exist, but their detection requires a careful consideration of model choice and complexity. The primary conclusion is not that one model is universally superior, but that different models reveal different facets of predictability, presenting a practical trade-off for risk managers and investors.

The Logistic Regression model, with its high recall, serves as an excellent "earlywarning system." Its strength lies in its sensitivity; it is highly effective at flagging periods of potential danger, making it suitable for risk monitoring applications where the cost of a missed event is catastrophic. Its primary drawback is the high rate of false positives, which would make it costly to use as a direct trading signal.

The LSTM network, conversely, represents a more refined and balanced predictor. By matching the high recall of the logistic model while offering improved precision, it provides a more reliable signal. This suggests that incorporating the temporal dimension of financial data is a key avenue for enhancing predictive power. The practical implication is that while linear relationships capture the brute force of market panic, sequence modeling is required to understand the more subtle, evolving patterns that precede it. The choice between these models is therefore a strategic one, contingent on the specific application and the user’s tolerance for different types of error.

In short, the study concludes that market crashes are difficult to forecast but not entirely random, and different models capture different aspects of predictability. Logistic regression functions well as a high-recall early warning tool, while LSTM models provide more balanced signals.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Michele Della Mura, Predicting Stock Market Crashes, A Comparative Analysis of Econometric and Machine Learning Models, Politecnico di Torino, 2025

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