While Bayesian methods have attracted considerable interest in actuarial science, they are yet to be embraced in large-scaled insurance predictive modeling applications, due to inefficiencies of Bayesian estimation procedures. The paper presents an efficient method that parallelizes Bayesian...

During the COVID-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional...

This paper presents a new Bayesian methodology for predicting a turning point in an economic system. The methodology utilizes information-theoretic measurements for assessing likelihood functions for a turning point. This methodology shows that the total information of a likelihood function...

Stock market is considered too uncertain to be predictable. Many individuals have developed methodologies or models to increase the probability of making a profit in their stock investment. The overall hit rates of these methodologies and models are generally too low to be practical for...

The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, investors, creditors, and governments are all interested...