A fast and precise prediction of stock market crashes is an important aspect of economic growth,
fiscal and monetary systems because it facilitates the government in the application of suitable
policies. 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. This study 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. A 104-country
sample has been used, allowing the sample compositions to take into account the regional diver-
sity of the alert warning indicators. To obtain a robust model, several alternative techniques
have been employed 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 impli-
cations for the appropriate macroeconomic policy response to downside risks, offering tools to
help achieve financial stability at the international level
