arXiv · arXiv q-fin · 2026
Detecting anomalous trajectories in decentralized crypto networks is fundamentally challenged by extreme label scarcity and the adaptive evasion strategies of illicit actors. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they struggle to internalize multi hop, logic driven motifs such as fund dispersal and layering that characterize sophisticated money laundering, limiting their fo…
Gyuyeon Na, Minjung Park, Soyoun Kim, Jungbin Shin, Sangmi Chai
arXiv · arXiv q-fin · 2026
This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct a teacher student learning pipeline in which a Conditional Value at Risk (CVaR) optimizer generates supervisory labels, and neural models (Bayesian and deterministic) are trained using both real and synthetically augmented data. The synthetic data is generated using …
Adhiraj Chattopadhyay
arXiv · arXiv q-fin · 2022
Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures. While these strategies have been applied successfully to data-rich settings involving mature assets with long histories, deploying them on instruments with limited samples generally produce over-fitted models with degraded performance. In this paper, we introduce…
Daniel Poh, Stephen Roberts, Stefan Zohren
arXiv · arXiv q-fin · 2025
Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In this paper, we propose a novel framework that leverages Causal Generative Adversarial Networks (CausalGANs) and Soft Actor-Critic (SAC) reinforcement learning (RL) to generate high-fidelity synthetic bond yield data for four major bond categories (AAA, BAA, US10Y, Junk). By in…
Jaskaran Singh Walia, Aarush Sinha, Naman Saraswat, Srinitish Srinivasan, Srihari Unnikrishnan
arXiv · arXiv q-fin · 2022
Human decision-making in real-life deviates significantly from the optimal decisions made by fully rational agents, primarily due to computational limitations or psychological biases. While existing studies in behavioral finance have discovered various aspects of human sub-rationality, there lacks a comprehensive framework to transfer these findings into an adaptive human model applicable across diverse financial mar…
Penghang Liu, Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch
arXiv · arXiv q-fin · 2025
Data scarcity and confidentiality in finance often impede model development and robust testing. This paper presents a unified multi-criteria evaluation framework for synthetic financial data and applies it to three representative generative paradigms: the statistical ARIMA-GARCH baseline, Variational Autoencoders (VAEs), and Time-series Generative Adversarial Networks (TimeGAN). Using historical S and P 500 daily dat…
Christophe D. Hounwanou, Yae Ulrich Gaba, Pierre Ntakirutimana
arXiv · arXiv q-fin · 2025
Financial scenario simulation is essential for risk management and portfolio optimization, yet it remains challenging especially in high-dimensional and small data settings common in finance. We propose a diffusion factor model that integrates latent factor structure into generative diffusion processes, bridging econometrics with modern generative AI to address the challenges of the curse of dimensionality and data s…
Minshuo Chen, Renyuan Xu, Yumin Xu, Ruixun Zhang
arXiv · arXiv q-fin · 2025
This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the current scarcity of flexible methods in this area, we employ a suite of machine learning (ML) models enriched with numerous HF predictors to enhance the predictability of intraday trading volumes. Our findings reveal that intraday stock trading…
Mihai Cucuringu, Kang Li, Chao Zhang
arXiv · arXiv q-fin · 2024
The inherent volatility and dynamic fluctuations within the financial stock market underscore the necessity for investors to employ a comprehensive and reliable approach that integrates risk management strategies, market trends, and the movement trends of individual securities. By evaluating specific data, investors can make more informed decisions. However, the current body of literature lacks substantial evidence s…
Alhassan S. Yasin, Prabdeep S. Gill
arXiv · arXiv q-fin · 2024
Universal power laws have been scrutinised in physics and beyond, and a long-standing debate exists in econophysics regarding the strict universality of the nonlinear price impact, commonly referred to as the square-root law (SRL). The SRL posits that the average price impact $I$ follows a power law with respect to transaction volume $Q$, such that $I(Q) \propto Q^δ$ with $δ\approx 1/2$. Some researchers argue that t…
Yuki Sato, Kiyoshi Kanazawa
arXiv · arXiv q-fin · 2020
We use a deep neural network to generate controllers for optimal trading on high frequency data. For the first time, a neural network learns the mapping between the preferences of the trader, i.e. risk aversion parameters, and the optimal controls. An important challenge in learning this mapping is that in intraday trading, trader's actions influence price dynamics in closed loop via the market impact. The exploratio…
Laura Leal, Mathieu Laurière, Charles-Albert Lehalle
arXiv · arXiv q-fin · 2000
We have studied here the self-organising features of the dynamics of a model market, where the agents `trade' for a single commodity with their money. The model market consists of fixed numbers of economic agents, money supply and commodity. We demonstrate that the model, apart from showing a self-organising behaviour, indicates a crucial role for the money supply in the market and also its self-organising behaviour …
Anirban Chakraborti, Srutarshi Pradhan, Bikas K. Chakrabarti
arXiv · arXiv · 2024
We propose a minimal model of the secured interbank network able to shed light on recent money markets puzzles. We find that excess liquidity emerges due to the interactions between the reserves and liquidity ratio constraints; the appearance of evergreen repurchase agreements and collateral re-use emerges as a simple answer to banks' counterparty risk and liquidity ratio regulation. In line with prevailing theories,…
Victor Le Coz, Michael Benzaquen, Damien Challet