:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cheng, Liexin, Cheng, Xue, Liu, Shuaiqiang
Format:	Preprint
Published:	2025
Subjects:	Computational Finance
Online Access:	https://arxiv.org/abs/2505.09551
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Understanding Short-Term Implied Volatility Dynamics: A Model-Independent Approach Beyond Stochastic Volatility
by: Cheng, Liexin, et al.
Published: (2024)

Joint Pricing in SPX and VIX Derivative Markets with Composite Change of Time Models
by: Cheng, Liexin, et al.
Published: (2024)

Finance-Informed Neural Network: Learning the Geometry of Option Pricing
by: Aboussalah, Amine M., et al.
Published: (2024)

Controllable Generation of Implied Volatility Surfaces with Variational Autoencoders
by: Wang, Jing, et al.
Published: (2025)

Intelligent Optimization of Mine Environmental Damage Assessment and Repair Strategies Based on Deep Learning
by: Cheng, Qishuo
Published: (2024)

Machine Learning Methods for Pricing Financial Derivatives
by: Fan, Lei, et al.
Published: (2024)

The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
by: Pippas, Nikolaos, et al.
Published: (2024)

QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
by: Wang, Haoxue, et al.
Published: (2025)

Forecasting Intraday Volume in Equity Markets with Machine Learning
by: Cucuringu, Mihai, et al.
Published: (2025)

FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
by: Li, Yang, et al.
Published: (2025)

Pricing options and computing implied volatilities using neural networks
by: Liu, Shuaiqiang, et al.
Published: (2019)

Predicting Accurate Hot Spots in a More Than Ten-Thousand-Core GPU with a Million-Time Speedup over FEM Enabled by a Physics-based Learning Algorithm
by: Jian, Lin, et al.
Published: (2024)

Non-Atomic Arbitrage in Decentralized Finance
by: Heimbach, Lioba, et al.
Published: (2024)

Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis
by: Cheng, Qishuo, et al.
Published: (2024)

Research on Optimizing Real-Time Data Processing in High-Frequency Trading Algorithms using Machine Learning
by: Fan, Yuxin, et al.
Published: (2024)

Mathematics of Differential Machine Learning in Derivative Pricing and Hedging
by: Gomes, Pedro Duarte
Published: (2024)

HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning
by: Audrino, Francesco, et al.
Published: (2024)

Beyond Knowledge to Agency: Evaluating Expertise, Autonomy, and Integrity in Finance with CNFinBench
by: Ding, Jinru, et al.
Published: (2025)

The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications
by: Caillaut, Gaëtan, et al.
Published: (2025)

Physics-Aware POD-Based Learning for Ab initio QEM-Galerkin Simulations of Periodic Nanostructures
by: Veresko, Martin, et al.
Published: (2025)

Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
by: Kim, Minwu, et al.
Published: (2024)

Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach
by: Liu, Jinjun, et al.
Published: (2026)

From Deep Learning to LLMs: A survey of AI in Quantitative Investment
by: Cao, Bokai, et al.
Published: (2025)

NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance
by: Su, Huan-Yi, et al.
Published: (2024)

Beyond the Black Box: Interpretability of LLMs in Finance
by: Tatsat, Hariom, et al.
Published: (2025)

Protein Multimer Structure Prediction via Prompt Learning
by: Gao, Ziqi, et al.
Published: (2024)

Technical Analysis Meets Machine Learning: Bitcoin Evidence
by: Anguiano, José Ángel Islas, et al.
Published: (2025)

Agentic Artificial Intelligence in Finance: A Comprehensive Survey
by: Aldridge, Irene, et al.
Published: (2026)

Reinforcement Learning Framework for Quantitative Trading
by: Yasin, Alhassan S., et al.
Published: (2024)

Finite Element and Machine Learning Modeling of Autogenous Self-Healing Concrete
by: Liu, William
Published: (2025)

Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction
by: Du, Yimin
Published: (2025)

Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading
by: Long, Wo, et al.
Published: (2025)

Machine Learning Classification and Portfolio Allocation: with Implications from Machine Uncertainty
by: Bai, Yang, et al.
Published: (2021)

QuantBench: Benchmarking AI Methods for Quantitative Investment
by: Wang, Saizhuo, et al.
Published: (2025)

Q-A3C2: Quantum Reinforcement Learning with Time-Series Dynamic Clustering for Adaptive ETF Stock Selection
by: Liu, Yen-Ku, et al.
Published: (2025)

An Efficient Machine Learning Framework for Option Pricing via Fourier Transform
by: Zhang, Liying, et al.
Published: (2025)

Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing?
by: Milyushkov, Georgy
Published: (2025)

Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps
by: Sakuma, Takayuki
Published: (2026)

Liquidity Premium, Liquidity-Adjusted Return and Volatility, and Extreme Liquidity
by: Deng, Qi, et al.
Published: (2023)

Gradient-Informed Machine Learning in Electromagnetics
by: Zorzetto, Matteo, et al.
Published: (2026)