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[1] Bankruptcy analysis using images and convolutional neural networks (CNN)
使用图像和卷积神经网络进行破产分析（CNN）
来源:ARXIV_20250225
[2] Stock Price Prediction Using a Hybrid LSTM GNN Model
基于混合LSTM-GNN模型的股票价格预测
来源:ARXIV_20250225
[3] Multi Agent Stock Prediction Systems
多智能体股票预测系统
来源:ARXIV_20250225
[4] Bounded Foresight Equilibrium in Large Dynamic Economies with Heterogeneous Agents and Aggregate Shocks
具有异质主体和总体冲击的大型动态经济体中的有限远见均衡
来源:ARXIV_20250225
[5] Predicting Liquidity Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
使用因果GAN和深度强化学习结合LLM评估预测流动性感知债券收益率
来源:ARXIV_20250225
[6] Event Based Limit Order Book Simulation under a Neural Hawkes Process
神经霍克斯过程下基于事件的限价订单模拟
来源:ARXIV_20250225
[7] Application of Norms in Transformer Models: Theory and Analysis
变压器模型中范数的应用：理论与分析
来源:SSRN_20250225
[8] Recurrent Neural Networks for Dynamic VWAP Execution
用于动态VWAP执行的递归神经网络
来源:ARXIV_20250226
[9] Pursuing Top Growth with Novel Loss Function
用新的损失函数追求最高增长
来源:ARXIV_20250226
[10] Ensemble RL through Classifier Models
通过分类器模型集成RL
来源:ARXIV_20250226
[11] Robust and Efficient Deep Hedging via Linearized Objective Neural Network
基于线性化目标神经网络的稳健高效深度套期保值
来源:ARXIV_20250226
[12] Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management
自适应Nesterov加速分布深度对冲，实现高效波动风险管理
来源:ARXIV_20250226
[13] A Two-Layer Ensemble Architecture for Enhanced Directional Price Prediction in Financial Markets
一种用于增强金融市场定向价格预测的两层集成架构
来源:SSRN_20250227
[14] A Method for Evaluating the Interpretability of Machine Learning Models in Predicting Bond Default Risk Based on LIME and SHAP
基于LIME和SHAP的债券违约风险预测中机器学习模型可解释性评估方法
来源:ARXIV_20250228
[1] Bankruptcy analysis using images and convolutional neural networks (CNN)
标题:使用图像和卷积神经网络进行破产分析（CNN）
作者:Luiz Tavares, Jose Mazzon, Francisco Paletta, Fabio Barros
来源:ARXIV_20250225
Abstract : The marketing departments of financial institutions strive to craft products and services that cater to the diverse needs of businesses of all sizes. However, it is evident upon analysis that larger corporations often receive a more substantial portion of available funds. This disparity arises from the relative ease of assessing the risk of default and bankruptcy in these more prominent companies.......(摘要翻译及全文见知识星球)
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[2] Stock Price Prediction Using a Hybrid LSTM GNN Model
标题:基于混合LSTM-GNN模型的股票价格预测
作者:Meet Satishbhai Sonani, Atta Badii, Armin Moin
来源:ARXIV_20250225
Abstract : This paper presents a novel hybrid model that integrates long short term memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the accuracy of stock market predictions. The LSTM component adeptly captures temporal patterns in stock price data, effectively modeling the time series dynamics of financial markets. Concurrently, the GNN component leverages Pearson correlation and association analysis to model......(摘要翻译及全文见知识星球)
Keywords :
[3] Multi Agent Stock Prediction Systems
标题:多智能体股票预测系统
作者:Daksh Dave, Gauransh Sawhney, Vikhyat Chauhan
来源:ARXIV_20250225
Abstract : This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various recurrent neural network (RNN) architectures, including Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and attention based models. These models are assessed for their ability to capture complex......(摘要翻译及全文见知识星球)
Keywords :
[4] Bounded Foresight Equilibrium in Large Dynamic Economies with Heterogeneous Agents and Aggregate Shocks
标题:具有异质主体和总体冲击的大型动态经济体中的有限远见均衡
作者:Bilal Islah, Bar Light
来源:ARXIV_20250225
Abstract : Large dynamic economies with heterogeneous agents and aggregate shocks are central to many important applications, yet their equilibrium analysis remains computationally challenging. This is because the standard solution approach, rational expectations equilibria require agents to predict the evolution of the full cross sectional distribution of state variables, leading to an extreme curse of dimensionality. In this paper, we introduce a novel......(摘要翻译及全文见知识星球)
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[5] Predicting Liquidity Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
标题:使用因果GAN和深度强化学习结合LLM评估预测流动性感知债券收益率
作者:Jaskaran Singh Walia, Aarush Sinha, Srinitish Srinivasan, Srihari Unnikrishnan
来源:ARXIV_20250225
Abstract : 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 incorporating 12 key......(摘要翻译及全文见知识星球)
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[6] Event Based Limit Order Book Simulation under a Neural Hawkes Process
标题:神经霍克斯过程下基于事件的限价订单模拟
作者:Luca Lalor, Anatoliy Swishchuk
来源:ARXIV_20250225
Abstract : In this paper, we propose an event driven Limit Order Book (LOB) model that captures twelve of the most observed LOB events in exchange based financial markets. To model these events, we propose using the state of the art Neural Hawkes process, a more robust alternative to traditional Hawkes process models. More specifically, this model captures the dynamic relationships between different......(摘要翻译及全文见知识星球)
Keywords :
[7] Application of Norms in Transformer Models: Theory and Analysis
标题:变压器模型中范数的应用：理论与分析
作者:Yifan Guo
来源:SSRN_20250225
Abstract : The Transformer architecture has transformed the landscape of deep learning, particularly in natural language processing (NLP) tasks. Central to its success are the incorporation of attention mechanisms and the strategic use of normalization techniques. This paper delves into the theoretical underpinnings of norm applications within the Transformer model, focusing on Layer Normalization, L2-norm regularization in self-attention, and weight decay regularization. We......(摘要翻译及全文见知识星球)
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[8] Recurrent Neural Networks for Dynamic VWAP Execution
标题:用于动态VWAP执行的递归神经网络
作者:Remi Genet
来源:ARXIV_20250226
Abstract : The execution of Volume Weighted Average Price (VWAP) orders remains a critical challenge in modern financial markets, particularly as trading volumes and market complexity continue to increase. In my previous work arXiv 2502.13722, I introduced a novel deep learning approach that demonstrated significant improvements over traditional VWAP execution methods by directly optimizing the execution problem rather than relying on volume curve......(摘要翻译及全文见知识星球)
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[9] Pursuing Top Growth with Novel Loss Function
标题:用新的损失函数追求最高增长
作者:Ruoyu Guo, Haochen Qiu
来源:ARXIV_20250226
Abstract : Making consistently profitable financial decisions in a continuously evolving and volatile stock market has always been a difficult task. Professionals from different disciplines have developed foundational theories to anticipate price movement and evaluate securities such as the famed Capital Asset Pricing Model (CAPM). In recent years, the role of artificial intelligence (AI) in asset pricing has been growing. Although the black......(摘要翻译及全文见知识星球)
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[10] Ensemble RL through Classifier Models
标题:通过分类器模型集成RL
作者:Zheli Xiong
来源:ARXIV_20250226
Abstract : This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance. By combining RL algorithms such as A2C, PPO, and SAC with traditional classifiers like Support Vector Machines (SVM), Decision Trees, and Logistic Regression, we investigate how different classifier groups can be integrated to improve risk return......(摘要翻译及全文见知识星球)
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[11] Robust and Efficient Deep Hedging via Linearized Objective Neural Network
标题:基于线性化目标神经网络的稳健高效深度套期保值
作者:Lei Zhao, Lin Cai
来源:ARXIV_20250226
Abstract : Deep hedging represents a cutting edge approach to risk management for financial derivatives by leveraging the power of deep learning. However, existing methods often face challenges related to computational inefficiency, sensitivity to noisy data, and optimization complexity, limiting their practical applicability in dynamic and volatile markets. To address these limitations, we propose Deep Hedging with Linearized objective Neural Network (DHLNN), a......(摘要翻译及全文见知识星球)
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[12] Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management
标题:自适应Nesterov加速分布深度对冲，实现高效波动风险管理
作者:Lei Zhao, Lin Cai, Wu-Sheng Lu
来源:ARXIV_20250226
Abstract : In the field of financial derivatives trading, managing volatility risk is crucial for protecting investment portfolios from market changes. Traditional Vega hedging strategies, which often rely on basic and rule based models, are hard to adapt well to rapidly changing market conditions. We introduce a new framework for dynamic Vega hedging, the Adaptive Nesterov Accelerated Distributional Deep Hedging (ANADDH), which combines......(摘要翻译及全文见知识星球)
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[13] A Two-Layer Ensemble Architecture for Enhanced Directional Price Prediction in Financial Markets
标题:一种用于增强金融市场定向价格预测的两层集成架构
作者:Sahand Hassanizorgabad,Semih Yumuşak
来源:SSRN_20250227
Abstract : Financial markets exhibit nonlinear dynamics and complexity, where diverse assets are traded between buyers and sellers, each seeking to maximise their return on investment (ROI). Predicting short-term price movements is particularly challenging due to the interaction of multiple unpredictable factors, including stock-specific news, company profiles, public sentiment, and macroeconomic conditions. Existing approaches often struggle with evaluation bias and lack the flexibility......(摘要翻译及全文见知识星球)
Keywords : Algorithmic Trading, Stock Prediction, Financial Forecasting, Machine Learning, ensemble learning, Meta Learning
[14] A Method for Evaluating the Interpretability of Machine Learning Models in Predicting Bond Default Risk Based on LIME and SHAP
标题:基于LIME和SHAP的债券违约风险预测中机器学习模型可解释性评估方法
作者:Yan Zhang, Lin Chen, Yixiang Tian
来源:ARXIV_20250228
Abstract : Interpretability analysis methods for artificial intelligence models, such as LIME and SHAP, are widely used, though they primarily serve as post model for analyzing model outputs. While it is commonly believed that the transparency and interpretability of AI models diminish as their complexity increases, currently there is no standardized method for assessing the inherent interpretability of the models themselves. This paper......(摘要翻译及全文见知识星球)
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