219 citations · 942 across the 36 of their papers we have counts for
38 papers
V-FiLLM: Verified Financial LLM Reasoning Benchmark
Alicia Larsen, Victoire Laurent, Aulia Kharis Rakhmasari +2
While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains comparatively less explored. We in…
Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization
Leonel Aguilar, Jan Nagler, Christoph Hoelscher +1
Deep neural networks can learn salient features from an in-distribution (ID) training window without identifying the data-generating structure needed for out-of-distribution (OOD)…
Towards agent-based-model informed neural networks
Nino Antulov-Fantulin
In this article, we present a framework for designing neural networks that remain consistent with the underlying principles of agent-based models. We begin by highlighting the limi…
Introducing the -Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting
German Rodikov, Nino Antulov-Fantulin
This paper introduces the -Cell, a novel Recurrent Neural Network (RNN) architecture for financial volatility modeling. Bridging traditional econometric approaches like GARCH wi…
Stretched and measured neural predictions of complex network dynamics
Vaiva Vasiliauskaite, Nino Antulov-Fantulin
Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph with non-t…
Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion
Vaibhav Krishna, Nino Antulov-Fantulin
Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-incre…
Topic Community Based Temporal Expertise for Question Routing
Vaibhav Krishna, Vaiva Vasiliauskaite, Nino Antulov-Fantulin
Question Routing in Community-based Question Answering websites aims at recommending newly posted questions to potential users who are most likely to provide "accepted answers". Mo…
Volatility-inspired -LSTM cell
German Rodikov, Nino Antulov-Fantulin
Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings. The recent advancements in n…
Can LSTM outperform volatility-econometric models?
German Rodikov, Nino Antulov-Fantulin
Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation. However, although many novel deep lear…
Information dynamics of price and liquidity around the 2017 Bitcoin markets crash
Vaiva Vasiliauskaite, Fabrizio Lillo, Nino Antulov-Fantulin
We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with di…
Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data
M. Eren Akbiyik, Mert Erkul, Killian Kaempf +2
Understanding the variations in trading price (volatility), and its response to exogenous information, is a well-researched topic in finance. In this study, we focus on finding sta…
On the accuracy of short-term COVID-19 fatality forecasts
Nino Antulov-Fantulin, Lucas Böttcher
Forecasting new cases, hospitalizations, and disease-induced deaths is an important part of infectious disease surveillance and helps guide health officials in implementing effecti…
Some Challenges in Monitoring Epidemics
Vaiva Vasiliauskaite, Nino Antulov-Fantulin, Dirk Helbing
Epidemic models often reflect characteristic features of infectious spreading processes by coupled non-linear differential equations considering different states of health (such as…
Implicit energy regularization of neural ordinary-differential-equation control
Lucas Böttcher, Nino Antulov-Fantulin, Thomas Asikis
Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and…
On the impact of publicly available news and information transfer to financial markets
Metod Jazbec, Barna Pásztor, Felix Faltings +2
We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information,…
Neural Ordinary Differential Equation Control of Dynamics on Graphs
Thomas Asikis, Lucas Böttcher, Nino Antulov-Fantulin
We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous time non-linear dynamical systems on graphs, which we represent…
Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets
Nino Antulov-Fantulin, Tian Guo, Fabrizio Lillo
We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from diffe…
Time-varying volatility in Bitcoin market and information flow at minute-level frequency
Irena Barjašić, Nino Antulov-Fantulin
In this paper, we analyze the time-series of minute price returns on the Bitcoin market through the statistical models of generalized autoregressive conditional heteroskedasticity…
Unifying continuous, discrete, and hybrid susceptible-infected-recovered processes on networks
Lucas Böttcher, Nino Antulov-Fantulin
Waiting times between two consecutive infection and recovery events in spreading processes are often assumed to be exponentially distributed, which results in Markovian (i.e., memo…
Ensemble approach for generalized network dismantling
Xiao-Long Ren, Nino Antulov-Fantulin
Finding a set of nodes in a network, whose removal fragments the network below some target size at minimal cost is called network dismantling problem and it belongs to the NP-hard…
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo, Tao Lin, Nino Antulov-Fantulin
For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights i…
Low-dimensional statistical manifold embedding of directed graphs
Thorben Funke, Tian Guo, Alen Lancic +1
We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-lin…
Sensing Social Media Signals for Cryptocurrency News
Johannes Beck, Roberta Huang, David Lindner +4
The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency…
Inferring short-term volatility indicators from Bitcoin blockchain
Nino Antulov-Fantulin, Dijana Tolic, Matija Piskorec +2
In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily tran…