3 citations · 4 across the 5 of their papers we have counts for
8 papers
RiskReason: A Benchmark for Verifiable Financial Risk Reasoning
Nazanin Yousefi, Mohammadreza Ghafouri, Seyedreza Tavakoli +7
Robust financial-risk inference requires more than mathematical correctness. Models must accurately apply conditional rules, maintain evolving financial states, and make decisions…
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction
Arshia Abolghasemi, Seyed Reza Tavakoli, Parsa Naderi +7
Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equation (SDE). For market time ser…
Mixture-of-Experts for Cryptocurrency Order Execution: Training Stability, Tail Risk, and Failure Modes
Alexander Ardaiz, Varun Budati, Ali Habibnia
Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect favourable training realisations…
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
Yutong Chao, Resat Gökhan, Jalal Etesami +1
We study a nonlinear factor model in which observed responses depend on low-rank latent factors through an unknown monotone link function. This setting is challenging and largely u…
Quantum Reservoir Computing for Realized Volatility Forecasting
Qingyu Li, Chiranjib Mukhopadhyay, Abolfazl Bayat +1
Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir…
Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework
Ali Habibnia, Mahdi Soltanzadeh
This study presents a Reinforcement Learning (RL)-based portfolio management model tailored for high-risk environments, addressing the limitations of traditional RL models and expl…
Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework
Jalal Etesami, Ali Habibnia, Negar Kiyavash
We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the…
Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)
Ali Habibnia, Esfandiar Maasoumi
This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality…