4 citations · 4 across the 6 of their papers we have counts for
6 papers
Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
Lin Li, Li Rong Wang, Hsuan Fu +1
Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction. However, financi…
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
Jamie Duell, Monika Seisenberger, Hsuan Fu +1
Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in…
Onchain Sports Betting using UBET Automated Market Maker
Daniel Jiwoong Im, Alexander Kondratskiy, Vincent Harvey +1
The paper underscores how decentralization in sports betting addresses the drawbacks of traditional centralized platforms, ensuring transparency, security, and lower fees. Non-cust…
UAMM: Price-oracle based Automated Market Maker
Daniel Jiwoong Im, Alexander Kondratskiy, Vincent Harvey +1
Automated market makers (AMMs) are pricing mechanisms utilized by decentralized exchanges (DEX). Traditional AMM approaches are constrained by pricing solely based on their own liq…
Stock Price Predictability and the Business Cycle via Machine Learning
Li Rong Wang, Hsuan Fu, Xiuyi Fan
We study the impacts of business cycles on machine learning (ML) predictions. Using the S&P 500 index, we find that ML models perform worse during most recessions, and the inclusio…
An Investigation of the Impact of COVID-19 Non-Pharmaceutical Interventions and Economic Support Policies on Foreign Exchange Markets with Explainable AI Techniques
Siyuan Liu, Mehmet Orcun Yalcin, Hsuan Fu +1
Since the onset of the the COVID-19 pandemic, many countries across the world have implemented various non-pharmaceutical interventions (NPIs) to contain the spread of virus, as we…