We survey the recent literature on learning in financial markets. Our main theme is that many
financial market phenomena that appear puzzling at first sight are easier to understand once …

This paper reviews the literature on Bayesian portfolio analysis. Information about events,
macro conditions, asset pricing theories, and security-driving forces can serve as useful …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

In financial and actuarial modeling and other areas of application, stochastic differential
equations with jumps have been employed to describe the dynamics of various state …

During the seven years that elapsed between the first and second editions of the present
book, considerable progress was achieved in the area of financial modelling and pricing of …

We solve the dynamic mean-variance portfolio problem and derive its time-consistent
solution using dynamic programming. Previous literature, in contrast, only determines either …

We propose a method for optimal portfolio selection using a Bayesian decision theoretic
framework that addresses two major shortcomings of the traditional Markowitz approach: the …

We study portfolio choice when labor income and dividends are cointegrated. Economically
plausible calibrations suggest young investors should take substantial short positions in the …

Publisher Summary This chapter focuses on the econometric treatment of portfolio choice
problems. The goal is to describe, discuss, and illustrate through examples the different …