A fast and precise prediction of stock market crashes is an important aspect of economic growth, and fiscal and monetary systems because it facilitates the government in the application of suitable policies. The study “Forecasting Stock Market Crashes via real-time recession probabilities: a quantum computing approach”, published in the Journal Fractals-Complex Geometry Patterns and Scaling in Nature and Society co-authored by our researcher David Alaminos, provides a comparison of quantum forecast methods and stock market declines and, therefore, a new prediction model of stock market crashes via real-time recession probabilities with the power to accurately estimate future global stock market downturn scenarios is achieved.

Many works have examined the behavior of the fall of stock markets and have built models to predict them. Nevertheless, there are limitations to the available research, and the literature calls for more investigation on the topic, as currently the accuracy of the models remains low and they have only been extended for the largest economies.

Using a 104-country sample, allowing the sample compositions to take into account the regional diversity of the alert warning indicators, the authors obtain a robust model using several alternative techniques on the sample under study, being Quantum Boltzmann Machines which have obtained very good prediction results due to their ability to remember features and develop long-term dependencies from time series and sequential data.

“Our model has large policy implications for the appropriate macroeconomic policy response to downside risks, offering tools to help achieve financial stability at the international level”, concluded Alaminos.