The literature on electoral cycles has developed in two distinct phases. The first one considered the existence of non-rational (naive) voters whereas the second one considered fully rational voters. In our perspective, an intermediate approach is more interesting, i.e. one that considers...

This paper analyzes the relationship between the expectational stability of rational expectations equilibria and the possible convergence of adaptive learning processes. Results obtained using recursive least squares lead to the conjecture that a correspondence between these both selection...

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We come to three empirical conclusions. First, the pure out-of-sample fit of the models is often poor: we find that most R^2 measures are negative,...

This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when...

In this paper we describe an application of machine learning algorithms to the problem of intraday forecasting of a large stock index (EUROSTOXX 50), where we use aggregated high-frequency sentiment in news about equities in the index as the main predictor. We utilize an ingenious combination of...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

Machine Learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices...

In this paper we show how risk-averse reinforcement learning can be used to hedge options. We apply a state-of-the-art risk-averse algorithm: Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment, considering realistic factors such as discrete time and transaction...

Since Hinton, Osindero, and Teh (2006) developed the fast learning algorithm, deep learning has been a set of powerful tools that has recently achieved impressive performance across a wide spectrum of industries as well as in academia. For the macroeconomic and financial variables, however, more...

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the...