249 citations · 586 across the 43 of their papers we have counts for
49 papers
Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics
Anubha Goel, Illia Oleksiienko, Mateusz Wilinski +2
Information and infectious diseases spread through social networks, but the spreading probabilities driving them are hard to estimate without per-node activation times. Application…
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys +2
Forecasting short-horizon mid-price movements from limit order book (LOB) data is central to algorithmic trading, yet most deep LOB forecasters are point predictors: they output a…
Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
Eljas Linna, Kestutis Baltakys, Derrick Manoharan +2
Deep Limit Order Book forecasting models capture nonlinear market dynamics, but their ability to quantify the effects of counterfactual order book messages has not been systematica…
Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks
Anubha Goel, Juho Kanniainen
Graph neural networks have achieved strong performance on graph-structured data, but their effectiveness depends heavily on the quality of the observed graph. In real applications,…
Class of topological portfolios: Are they better than classical portfolios?
Anubha Goel, Amita Sharma, Juho Kanniainen
Topological Data Analysis (TDA), an emerging field in investment sciences, harnesses mathematical methods to extract data features based on shape, offering a promising alternative…
LOBERT: Generative AI Foundation Model for Limit Order Book Messages
Eljas Linna, Kestutis Baltakys, Alexandros Iosifidis +1
Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequen…
Prospects of Imitating Trading Agents in the Stock Market
Mateusz Wilinski, Juho Kanniainen
In this work we show how generative tools, which were successfully applied to limit order book data, can be utilized for the task of imitating trading agents. To this end, we propo…
Agent-based model of information diffusion in the limit order book trading
Mateusz Wilinski, Juho Kanniainen
There are multiple explanations for stylized facts in high-frequency trading, including adaptive and informed agents, many of which have been studied through agent-based models. Th…
Variational Graph Convolutional Neural Networks
Illia Oleksiienko, Juho Kanniainen, Alexandros Iosifidis
Estimation of model uncertainty can help improve the explainability of Graph Convolutional Networks and the accuracy of the models at the same time. Uncertainty can also be used in…
Classifying and Clustering Trading Agents
Mateusz Wilinski, Anubha Goel, Alexandros Iosifidis +1
The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but…
Foundation Time-Series AI Model for Realized Volatility Forecasting
Anubha Goel, Puneet Pasricha, Martin Magris +1
Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. These models are trained on numerous diverse datasets and claim to be…
Learning hidden cascades via classification
Derrick Gilchrist Edward Manoharan, Anubha Goel, Alexandros Iosifidis +2
The spreading dynamics in social networks are often studied under the assumption that individuals' statuses, whether informed or infected, are fully observable. However, in many re…
Forecasting mortality associated emergency department crowding
Jalmari Nevanlinna, Anna Eidstø, Jari Ylä-Mattila +5
Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preve…
Topological Clustering of Agents in Hidden Information Contagions: Application to Financial Markets
Anubha Goel, Henri Hansen, Juho Kanniainen
Building on topological data analysis and expert knowledge, this study introduces a Mapper-based approach to cluster agents based on their tendency to be influenced by information…
Time-Series Foundation AI Model for Value-at-Risk Forecasting
Anubha Goel, Puneet Pasricha, Juho Kanniainen
This study is the first to analyze the performance of a time-series foundation AI model for Value-at-Risk (VaR), which essentially forecasts the left-tail quantiles of returns. Fou…
Detachment Problem -- Application in Prevention of Information Leakage in Stock Markets
Henri Hansen, Juho Kanniainen
In this paper, we introduce the Detachment Problem. It can be seen as a generalized Vaccination Problem. The aim is to optimally cut the individuals' ties to circles that connect t…
Sparse Index Tracking via Topological Learning
Anubha Goel, Puneet Pasricha, Juho Kanniainen
In this research, we introduce a novel methodology for the index tracking problem with sparse portfolios by leveraging topological data analysis (TDA). Utilizing persistence homolo…
Cryptocurrency Portfolio Optimization by Neural Networks
Quoc Minh Nguyen, Dat Thanh Tran, Juho Kanniainen +2
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on…
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification
Zaffar Zaffar, Fahad Sohrab, Juho Kanniainen +1
In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial…
Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input
Jalmari Tuominen, Eetu Pulkkinen, Jaakko Peltonen +4
Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has t…
Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting
Adamantios Ntakaris, Moncef Gabbouj, Juho Kanniainen
High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vecto…
Early Warning Software for Emergency Department Crowding
Jalmari Tuominen, Teemu Koivistoinen, Juho Kanniainen +3
Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future servic…
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots
Mostafa Shabani, Martin Magris, George Tzagkarakis +2
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchroniz…
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification
Mostafa Shabani, Dat Thanh Tran, Juho Kanniainen +1
Deep Learning models have become dominant in tackling financial time-series analysis problems, overturning conventional machine learning and statistical methods. Most often, a mode…