Many actuarial science researchers on stochastic modeling and forecasting of systematic mortality risk use Cairns … be implemented by Kenyan insurance firms when modeling and forecasting systematic mortality risk helpful in the pricing …-Blake-Dowd (CBD) Model (2006) due to its ability to consider the cohort effects. A three-factor stochastic mortality model has three …

In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time … series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to … predictive ability are brie y reviewed. Finally, we discuss application of machine learning in economics and finance and provide …

of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models … shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al … parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine …

We present a deep reinforcement learning framework for an automatic trading of contracts for difference (CfD) on … indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent long short-term memory … but instead uses a reinforcement learning agent to learn an overall lucrative trading policy. Therefore, we simulate a …

Evidence from Africa -- Part 2: Financial Risk Prediction using Machine Learning -- 5. Using Outlier Modification Rule for … Support Vector Machine and Logit Support Vector Machine -- 7. Predicting Corporate Failure using Ensemble Extreme Learning …-Series Forecasting -- 9. An Ensemble LGBM (Light Gradient Boosting Machine) Approach for Crude Oil Price Prediction -- 10. Model …