RT Journal Article
T1 Forecasting stock market crashes via real-time recession probabilities: a quantum computing approach.
A1 Alaminos, David
A1 Salas-Compás, María Belén
A1 Fernández-Gámez, Manuel Ángel
K1 Crisis financieras - Modelos econométricos
AB 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 suitablepolicies. Many works have examined the behavior of the fall of stock markets and have builtmodels 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 modelsremains low and they have only been extended for the largest economies. This study providesa comparison of quantum forecast methods and stock market declines and, therefore, a newprediction model of stock market crashes via real-time recession probabilities with the power toaccurately estimate future global stock market downturn scenarios is achieved. A 104-countrysample 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 techniqueshave been employed on the sample under study, being Quantum Boltzmann Machines, whichhave obtained very good prediction results due to their ability to remember features and developlong-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 tohelp achieve financial stability at the international level
PB World Scientific Publishing
YR 2022
FD 2022
LK https://hdl.handle.net/10630/32297
UL https://hdl.handle.net/10630/32297
LA eng
NO Alaminos, D., Belen Salas, M., & Fernandez-Gamez, M. A. (2022). Forecasting stock market crashes via real-time recession probabilities: a quantum computing approach. Fractals, 30(05), 2240162.
NO This research was funded by Universidad de Málaga.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026