We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic...

This paper develops a simulation-based solution method to solve large state space macrofinance models using machine learning. We use a neural network (NN) to approximate the expectations in the optimality conditions in the spirit of the stochastic parameterized expectations algorithm (PEA)....

We present an explicit framework for horizon based investing that results in superior expected risk adjusted returns and lower turnover. The framework includes a means of empirically determining an expected horizon and its confidence intervals, an alpha model structure linking horizon with the...

Chapter 1 Introduction -- Chapter 2 Efficient markets -- Chapter 3 Equity premium -- Chapter 4 The dividend ratio model -- Chapter 5 Bond valuation -- Chapter 6 Yield curves -- Chapter 7 Term structure models -- Chapter 8 Real estate market -- Chapter 9 Derivative securities -- Chapter 10...