283 citations · 802 across the 44 of their papers we have counts for
8 papers · 1 filter
Modelling Equity Transaction Networks as Bursty Processes
Isobel Seabrook, Paolo Barucca, Fabio Caccioli
Trade executions for major stocks come in bursts of activity, which can be partly attributed to the presence of self- and mutual excitations endogenous to the system. In this paper…
Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement
Fatih Ozhamaratli, Paolo Barucca
The transition from defined benefit to defined contribution pension plans shifts the responsibility for saving toward retirement from governments and institutions to the individual…
Variational Heteroscedastic Volatility Model
Zexuan Yin, Paolo Barucca
We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial…
Deep Recurrent Modelling of Granger Causality with Latent Confounding
Zexuan Yin, Paolo Barucca
Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer pote…
Neural Generalised AutoRegressive Conditional Heteroskedasticity
Zexuan Yin, Paolo Barucca
We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in…
Sequential asset ranking in nonstationary time series
Gabriel Borrageiro
We create a ranking algorithm, the naive Bayes asset ranker. Our algorithm computes the posterior probability that individual assets will be ranked higher than other portfolio cons…
Structural importance and evolution: an application to financial transaction networks
Isobel Seabrook, Paolo Barucca, Fabio Caccioli
A fundamental problem in the study of networks is the identification of important nodes. This is typically achieved using centrality metrics, which rank nodes in terms of their pos…
The Recurrent Reinforcement Learning Crypto Agent
Gabriel Borrageiro, Nick Firoozye, Paolo Barucca
We demonstrate a novel application of online transfer learning for a digital assets trading agent. This agent uses a powerful feature space representation in the form of an echo st…