16 citations · 32 across the 6 of their papers we have counts for
6 papers
Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
Sven Goluža, Tomislav Kovačević, Stjepan Begušić +1
The sequential nature of decision-making in financial asset trading aligns naturally with the reinforcement learning (RL) framework, making RL a common approach in this domain. How…
Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series
Lucija Žignić, Stjepan Begušić, Zvonko Kostanjčar
Estimation of high-dimensional covariance matrices in latent factor models is an important topic in many fields and especially in finance. Since the number of financial assets grow…
Deep reinforcement learning with positional context for intraday trading
Sven Goluža, Tomislav Kovačević, Tessa Bauman +1
Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making, where an agent makes decisions based on its trading strategy developed from market observa…
Statistical arbitrage portfolio construction based on preference relations
Fredi Šarić, Stjepan Begušić, Andro Merćep +1
Statistical arbitrage methods identify mispricings in securities with the goal of building portfolios which are weakly correlated with the market. In pairs trading, an arbitrage op…
Deep Reinforcement Learning for Robust Goal-Based Wealth Management
Tessa Bauman, Bruno Gašperov, Stjepan Begušić +1
Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as…
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model
Bruno Gašperov, Zvonko Kostanjčar
The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trai…