Social networks rely on sharing engaging content for their users. Since continued production of user-generated content is critical to their success, they have constructed a variety of tools to motivate new content creation, to facilitate user discovery of new content, and to provide attention...

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these...

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 (LSTM) networks can learn from recent market history and...