The design of financial trading systems (FTSs) is a subject of high interest both for the academic environment and for the professional one due to the promises by machine learning methodologies. In this paper we consider the Reinforcement Learning-based policy evaluation approach known as...

In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gaussian processes and Bayesian optimization. Gaussian processes can be seen as...

This paper presents a stylized model of interaction among boundedly rational heterogeneousagents in a multi-asset financial market to examine how agents' impatience, extrapolation, andswitching behaviours can affect cross-section market stability. Besides extrapolation and performance based...

In this paper we review some standard and more recent filtering techniques, based on Random Matrix Theory (RMT), that can reduce the “empirical” noise and slightly improve standard Markowitz model's predictions based on dynamics of a covariance matrix

We consider the problem of maximizing expected utility for a power investor who can allocate his wealth in a stock, a defaultable security, and a money market account. The dynamics of these security prices are governed by geometric Brownian motions modulated by a hidden continuous time finite...

Purpose of this paper was to research portfolio optimization problem on Croatian capital market using Markowitz theory. Research systematically investigated the selection of securities, and defined the importance of using fundamental analysis when selecting the best combination of securities....

Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based...

The purpose of this paper is to solve a stochastic control problem consisting of optimizing the management of a trading system. Two model free machine learning algorithms based on Reinforcement Learning method are compared: the Q-Learning and the SARSA ones. Both these models optimize their...

This paper deals with the problem of the cost of uncertainty associated with the utility maximization problem in a complete market with multiple risky assets and unobservable dividends. This leads naturally to a partial information setup from which filtering techniques can be applied. Using a...

Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based...