Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Relevance Vector Machines (RVM) were used to predict daily returns for an FX carry basket. Market observable exogenous variables known to have a relationship with the basket along with lags of the basket's return were used as...

In this paper we propose a novel application of Gaussian processes (GPs) to financial asset allocation. Our approach is deeply rooted in Stochastic Portfolio Theory (SPT), a stochastic analysis framework introduced by Robert E. Fernholz that aims at flexibly analysing the performance of certain...

This study aims to describe the size distribution of Portuguese firms, as measured by annual sales and total assets, between 2006 and 2012, giving an economic interpretation for the evolution of the distribution along the time. Three distributions are fitted to data: the lognormal, the Pareto...

Parameter uncertainty has been a recurrent subject treated in the financial literature. The normative portfolio selection approach considers two main kinds of decision rules: expected expected utility maximization and mean-variance criterion. Assuming that the mean-variance criterion is a good...

I use a model of human causal learning, Causal Support (Tenenbaum & Griffiths, 2001), to derive a meaningful measure of Cognitive Distance – the degree to which two people differ in their opinions about the workings of the world. Next, I amend this measure to quantify the notion of Cognitive...

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and...

Operations Management endeavors to identify polices that improve real-world operating systems. However, these systems often consist of actors that do not behave fully rationally and to improve performance managers must predict how such boundedly-rational actors will behave. Approaches to this...

This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined...

In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second...

In this paper we consider optimal consumption and strategic asset allocation decisions of an investor with a finite planning horizon. A Q-learning approach is used to maximize the expected utility of consumption. The first part of the paper presents conceptually the implementation of Q-learning...