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[1] Adaptive Dueling Double Deep Q networks in Uniswap V3 Replication and Extension with Mamba
基于Mamba的Uniswap V3复制和扩展中的自适应对偶双深度Q网络
来源:ARXIV_20251201
[2] DeXposure
DeXposure
来源:ARXIV_20251201
[3] Standard Occupation Classifier A Natural Language Processing Approach
标准职业分类器——一种自然语言处理方法
来源:ARXIV_20251201
[4] Reinforcement Learning for Volatility Surface Fitting: Addressing Complexity and Arbitrage Constraints
波动率曲面拟合的强化学习：解决复杂性和套利约束
来源:SSRN_20251201
[5] LSTM-Augmented DQN for Quantitative Trading in Partially Observable Markets
LSTM增强DQN用于部分可观测市场的定量交易
来源:SSRN_20251201
[6] Monopoly Pricing of Weather Index Insurance
天气指数保险的垄断定价
来源:ARXIV_20251202
[7] Financial Text Classification Based On rLoRA Finetuning On Qwen3 8B model
基于Qwen3-8B模型的rLoRA微调金融文本分类
来源:ARXIV_20251202
[8] Empirical Analysis of the Interlinkages between Energy Prices, Market Volatility, and Policy Uncertainty (2020-2025)
能源价格、市场波动和政策不确定性之间相互联系的实证分析（2020-2025）
来源:SSRN_20251202
[9] Risk-Aware Deep Reinforcement Learning for Crypto and Equity Trading Under Transaction Costs
交易成本下加密货币和股票交易的风险意识深度强化学习
来源:SSRN_20251202
[10] Integration of LSTM Networks in Random Forest Algorithms for Stock Market Trading Predictions
LSTM网络在股市交易预测随机森林算法中的集成
来源:ARXIV_20251203
[11] Statistical Arbitrage in Polish Equities Market Using Deep Learning Techniques
使用深度学习技术在波兰股市进行统计套利
来源:ARXIV_20251203
[12] Optimal Comprehensible Targeting
最佳可理解目标
来源:ARXIV_20251203
[13] Does Firm Level AI Adoption Improve Early Warning of Corporate Financial Distress Evidence from Chinese Non Financial Firms
企业级人工智能的采用是否改善了企业财务困境的预警——来自中国非金融企业的证据
来源:ARXIV_20251203
[14] Modelling the Doughnut of social and planetary boundaries with frugal machine learning
用节俭的机器学习模拟社会和地球边界的甜甜圈
来源:ARXIV_20251203
[15] Statistical Modeling of Volatility and Regime Switching in Financial Markets: Volatility Clustering and Hidden Regime Dynamics in SPX and BTC
金融市场波动和制度转换的统计建模：SPX和BTC的波动聚类和隐藏制度动态
来源:SSRN_20251203
[16] A Co evolutionary Approach for Heston Calibration
Heston校准的协同进化方法
来源:ARXIV_20251204
[17] Stress-Testing Machine Learning Models for Stock Forecasting with Minimalist Feature Sets
基于最小特征集的股票预测压力测试机器学习模型
来源:SSRN_20251204
[18] Continuous time reinforcement learning for optimal switching over multiple regimes
连续时间强化学习，实现多种模式的最佳切换
来源:ARXIV_20251205
[19] Deep Statistical Jump Models: Towards General Mixture-of-Experts Framework for Time Series Learning
深度统计跳跃模型：时间序列学习的一般混合专家框架
来源:SSRN_20251205
[1] Adaptive Dueling Double Deep Q networks in Uniswap V3 Replication and Extension with Mamba
标题:基于Mamba的Uniswap V3复制和扩展中的自适应对偶双深度Q网络
作者:Zhaofeng Zhang
来源:ARXIV_20251201
Abstract : The report goes through the main steps of replicating and improving the article Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning. The replication part includes how to obtain data from the Uniswap Subgraph, details of the implementation, and comments on the results. After the replication, I propose a new structure based on the original model, which combines......(摘要翻译及全文见知识星球)
Keywords :
[2] DeXposure
标题:DeXposure
作者:Wenbin Wu, Kejiang Qian, Alexis Lui, Christopher Jack, Yue Wu, Peter McBurney, Fengxiang He, Bryan Zhang
来源:ARXIV_20251201
Abstract : We curate the DeXposure dataset, the first large scale dataset for inter protocol credit exposure in decentralized financial networks, covering global markets of 43.7 million entries across 4.3 thousand protocols, 602 blockchains, and 24.3 thousand tokens, from 2020 to 2025. A new measure, value linked credit exposure between protocols, is defined as the inferred financial dependency relationships derived from changes in......(摘要翻译及全文见知识星球)
Keywords :
[3] Standard Occupation Classifier A Natural Language Processing Approach
标题:标准职业分类器——一种自然语言处理方法
作者:Sidharth Rony, Jack Patman
来源:ARXIV_20251201
Abstract : Standard Occupational Classifiers (SOC) are systems used to categorize and classify different types of jobs and occupations based on their similarities in terms of job duties, skills, and qualifications. Integrating these facets with Big Data from job advertisement offers the prospect to investigate labour demand that is specific to various occupations. This project investigates the use of recent developments in natural......(摘要翻译及全文见知识星球)
Keywords :
[4] Reinforcement Learning for Volatility Surface Fitting: Addressing Complexity and Arbitrage Constraints
标题:波动率曲面拟合的强化学习：解决复杂性和套利约束
作者:Pierre Cornilleau,Omar Karkar
来源:SSRN_20251201
Abstract : The calibration of volatility surfaces is a fundamental task in quantitative finance, traditionally relying on deterministic algorithms that may struggle with adaptability to dynamic market conditions and lose valuable past information. This paper extends prior work on applying Reinforcement Learning (RL) to volatility fitting by addressing more complex and, potentially, realistic market models. We demonstrate the critical role of a Prioritized......(摘要翻译及全文见知识星球)
Keywords : Reinforcement Learning, Volatility fitting, Arbitrage, Machine Learning, Equity Derivatives
[5] LSTM-Augmented DQN for Quantitative Trading in Partially Observable Markets
标题:LSTM增强DQN用于部分可观测市场的定量交易
作者:hanyue yu,Jiyang Dong,Yaqian Jiang
来源:SSRN_20251201
Abstract : Deep Reinforcement Learning (DRL) has demonstrated tremendous potential in quantitative trading research in recent years. However, its performance still faces significant challenges in financial market environments characterized by high noise, low signal-to-noise ratios, and partial observability. Traditional approaches often rely on single supervised learning or reinforcement learning architectures. Still, these methods frequently struggle to adequately capture temporal dependencies and may exhibit......(摘要翻译及全文见知识星球)
Keywords : Long Short-term Memory, Deep reinforcement learning, Partially Observable Markov Decision Process, Quantitative trading
[6] Monopoly Pricing of Weather Index Insurance
标题:天气指数保险的垄断定价
作者:Tim J. Boonen, Wenyuan Li, Zixiao Quan
来源:ARXIV_20251202
Abstract : This study models the monopoly pricing of weather index insurance as a Bowley type sequential game involving a profit maximizing insurer (leader) and a farmer (follower). The farmer chooses an insurance payoff to minimize a convex distortion risk measure, while the insurer anticipates this best response and selects a premium principle and its parameters to maximize profit net of administrative costs.......(摘要翻译及全文见知识星球)
Keywords :
[7] Financial Text Classification Based On rLoRA Finetuning On Qwen3 8B model
标题:基于Qwen3-8B模型的rLoRA微调金融文本分类
作者:Zhiming Lian
来源:ARXIV_20251202
Abstract : Financial text classification has increasingly become an important aspect in quantitative trading systems and related tasks, such as financial sentiment analysis and the classification of financial news. In this paper, we assess the performance of the large language model Qwen3 8B on both tasks. Qwen3 8B is a state of the art model that exhibits strong instruction following and multilingual capabilities,......(摘要翻译及全文见知识星球)
Keywords :
[8] Empirical Analysis of the Interlinkages between Energy Prices, Market Volatility, and Policy Uncertainty (2020-2025)
标题:能源价格、市场波动和政策不确定性之间相互联系的实证分析（2020-2025）
作者:Ayman Mohammed
来源:SSRN_20251202
Abstract : This report investigates the empirical linkages between energy prices, market volatility, and economic policy uncertainty between October 2020 and October 2025. Using daily data sourced from the Federal Reserve Economic Data (FRED) database, the study analyses the relationships among WTI Crude Oil, Natural Gas, the Volatility Index (VIX), the S&P 500, and the Economic Policy Uncertainty Index (EPU). The analysis integrates......(摘要翻译及全文见知识星球)
Keywords : Energy Prices, Crude Oil (WTI), Natural Gas, Market Volatility, VIX (Volatility Index), Economic Policy Uncertainty (EPU), S&P 500, Machine Learning, XGBoost, SHAP Values, Behavioral Finance, Econometric Analysis, Monte Carlo Simulation, Correlation Analysis, Policy Risk, Financial Sentiment
[9] Risk-Aware Deep Reinforcement Learning for Crypto and Equity Trading Under Transaction Costs
标题:交易成本下加密货币和股票交易的风险意识深度强化学习
作者:Ekantheswar Bandarupalli
来源:SSRN_20251202
Abstract : We present a reinforcement learning (RL) trading agent that optimizes risk-adjusted returns in volatile markets by explicitly penalizing drawdowns and turnover. Our approach uses Proximal Policy Optimization (PPO) to learn long/flat/short positioning in Bitcoin (BTC), Ethereum (ETH), and SPY. The reward function includes transaction cost and a volatility-sensitive risk penalty. We evaluate performance on 2020-2024 daily data and report out-of-sample 2024......(摘要翻译及全文见知识星球)
Keywords : Reinforcement Learning, Quantitative Finance, Financial AI, Trading, Risk Management
[10] Integration of LSTM Networks in Random Forest Algorithms for Stock Market Trading Predictions
标题:LSTM网络在股市交易预测随机森林算法中的集成
作者:Juan C. King, Jose M. Amigo
来源:ARXIV_20251203
Abstract : The aim of this paper is the analysis and selection of stock trading systems that combine different models with data of different nature, such as financial and microeconomic information. Specifically, based on previous work by the authors and applying advanced techniques of Machine Learning and Deep Learning, our objective is to formulate trading algorithms for the stock market with empirically tested......(摘要翻译及全文见知识星球)
Keywords :
[11] Statistical Arbitrage in Polish Equities Market Using Deep Learning Techniques
标题:使用深度学习技术在波兰股市进行统计套利
作者:Marek Adamczyk, Michał Dąbrowski
来源:ARXIV_20251203
Abstract : We study a systematic approach to a popular Statistical Arbitrage technique Pairs Trading. Instead of relying on two highly correlated assets, we replace the second asset with a replication of the first using risk factor representations. These factors are obtained through Principal Components Analysis (PCA), exchange traded funds (ETFs), and, as our main contribution, Long Short Term Memory networks (LSTMs).......(摘要翻译及全文见知识星球)
Keywords :
[12] Optimal Comprehensible Targeting
标题:最佳可理解目标
作者:Walter W. Zhang
来源:ARXIV_20251203
Abstract : Developments in machine learning and big data allow firms to fully personalize and target their marketing mix. However, data and privacy regulations, such as those in the European Union (GDPR), incorporate a right to explanation, which is fulfilled when targeting policies are comprehensible to customers. This paper provides a framework for firms to navigate right to explanation legislation. First,......(摘要翻译及全文见知识星球)
Keywords :
[13] Does Firm Level AI Adoption Improve Early Warning of Corporate Financial Distress Evidence from Chinese Non Financial Firms
标题:企业级人工智能的采用是否改善了企业财务困境的预警——来自中国非金融企业的证据
作者:Frederik Rech (1), Fanchen Meng (2), Hussam Musa (3), Martin Šebeňa (4), Siele Jean Tuo (5) ((1) School of Economics, Beijing Institute of Technology, Beijing, China (2) Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, China (3) Faculty of Economics, Matej Bel University, Banská Bystrica, Slovakia (4) Faculty of Arts and Social Sciences, Hong Kong Baptist University, Hong Kong, China (5) Business School, Liaoning University, Shenyang, China)
来源:ARXIV_20251203
Abstract : This study investigates whether firm level artificial intelligence (AI) adoption improves the out of sample prediction of corporate financial distress models beyond traditional financial ratios. Using a sample of Chinese listed firms (2008 2023), we address sparse AI data with a novel pruned training window method, testing multiple machine learning models. We find that AI adoption consistently increases predictive accuracy, with......(摘要翻译及全文见知识星球)
Keywords :
[14] Modelling the Doughnut of social and planetary boundaries with frugal machine learning
标题:用节俭的机器学习模拟社会和地球边界的甜甜圈
作者:Stefano Vrizzi, Daniel W. O'Neill
来源:ARXIV_20251203
Abstract : The Doughnut of social and planetary boundaries has emerged as a popular framework for assessing environmental and social sustainability. Here, we provide a proof of concept analysis that shows how machine learning (ML) methods can be applied to a simple macroeconomic model of the Doughnut. First, we show how ML methods can be used to find policy parameters that......(摘要翻译及全文见知识星球)
Keywords :
[15] Statistical Modeling of Volatility and Regime Switching in Financial Markets: Volatility Clustering and Hidden Regime Dynamics in SPX and BTC
标题:金融市场波动和制度转换的统计建模：SPX和BTC的波动聚类和隐藏制度动态
作者:Ekantheswar Bandarupalli
来源:SSRN_20251203
Abstract : We study volatility dynamics and regime behavior in equity and crypto markets by combining conditional volatility models from financial econometrics with hiddenstate regime models from time-series inference. Using daily S&P 500 proxy (SPY) and Bitcoin (BTC-USD) returns from 2015 to 2025, we estimate GARCH-family processes (GARCH(1,1), EGARCH, and GJR-GARCH) to model volatility clustering, and a Gaussian Hidden Markov Model (HMM) to......(摘要翻译及全文见知识星球)
Keywords : Volatility, GARCH, Regime Switching, Hidden Markov Models, Financial Econometrics, Bitcoin, S&P 500, Machine Learning Finance
[16] A Co evolutionary Approach for Heston Calibration
标题:Heston校准的协同进化方法
作者:Julian Gutierrez
来源:ARXIV_20251204
Abstract : We evaluate a co evolutionary calibration framework for the Heston model in which a genetic algorithm (GA) over parameters is coupled to an evolving neural inverse map from option surfaces to parameters. While GA history sampling can reduce training loss quickly and yields strong in sample fits to the target surface, learning curve diagnostics show a widening train validation gap......(摘要翻译及全文见知识星球)
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[17] Stress-Testing Machine Learning Models for Stock Forecasting with Minimalist Feature Sets
标题:基于最小特征集的股票预测压力测试机器学习模型
作者:Sevda Kuşkaya,Faik Bilgili
来源:SSRN_20251204
Abstract : Accurate prediction of stock market indices is an important part of financial decision making. This study performs a stress test of machine learning models by forecasting the Dow Jones Australia Index (DJ Australia) from 2015 to 2025 using a minimalist set of purely temporal features. Eight regression models; Linear Regression, Support Vector Regression (SVR), XGBoost, Random Forest, k-Nearest Neighbors (KNN), Multi-layer......(摘要翻译及全文见知识星球)
Keywords : Machine Learning, Time series forecasting, Model Comparison, Stress Testing, Stock Market Prediction
[18] Continuous time reinforcement learning for optimal switching over multiple regimes
标题:连续时间强化学习，实现多种模式的最佳切换
作者:Yijie Huang, Mengge Li, Xiang Yu, Zhou Zhou
来源:ARXIV_20251205
Abstract : This paper studies the continuous time reinforcement learning (RL) for optimal switching problems across multiple regimes. We consider a type of exploratory formulation under entropy regularization where the agent randomizes both the timing of switches and the selection of regimes through the generator matrix of an associated continuous time finite state Markov chain. We establish the well posedness of the associated......(摘要翻译及全文见知识星球)
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[19] Deep Statistical Jump Models: Towards General Mixture-of-Experts Framework for Time Series Learning
标题:深度统计跳跃模型：时间序列学习的一般混合专家框架
作者:Chenyu Yu,John M. Mulvey,Petter N. Kolm
来源:SSRN_20251205
Abstract : We propose a general and simple deep learning extension of Statistical Jump Models in the spirit of a Mixture-of-Experts framework. This framework is designed to ensemble neural networks via inferred latent state distributions. We detail the variants and applications of Deep Statistical Jump Models such as regime-switching factor models, end-to-end decision models, among others. We also describe how to use Block......(摘要翻译及全文见知识星球)
Keywords : Regime Model, Machine Learning, Deep Learning, Statistics, Semisupervised Learning, Time Series
