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[1] Deep Hedging to Manage Tail Risk
深度对冲以管理尾部风险
来源:ARXIV_20250701
[2] Can We Reliably Predict the Fed s Next Move A Multi Modal Approach to U.S. Monetary Policy Forecasting
我们能否可靠地预测美联储的下一步行动？美国货币政策预测的多模式方法
来源:ARXIV_20250701
[3] Overparametrized models with posterior drift
具有后向漂移的过参数化模型
来源:ARXIV_20250701
[4] FairMarket RL
公平市场RL
来源:ARXIV_20250701
[5] Optimization Method of Multi factor Investment Model Driven by Deep Learning for Risk Control
基于深度学习的风险控制多因素投资模型优化方法
来源:ARXIV_20250702
[6] Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules
利用机器学习计算约束最优碳税规则
来源:ARXIV_20250703
[7] Forecasting Nigerian Equity Stock Returns Using Long Short Term Memory Technique
利用长短期记忆技术预测尼日利亚股票收益
来源:ARXIV_20250704
[8] News Sentiment Embeddings for Stock Price Forecasting
股价预测的新闻情绪嵌入
来源:ARXIV_20250704
[9] DeepSupp
深汤
来源:ARXIV_20250704
[10] Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning
强化学习加速投资组合优化和期权定价
来源:ARXIV_20250704
[11] Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation
基于LSTM的小波变换卷积和通道注意力集成在基于股票价格预测的投资组合分配中
来源:ARXIV_20250704
[12] Forecasting Labor Markets with LSTNet
用LSTNet预测劳动力市场
来源:ARXIV_20250704
[13] Detecting Fraud in Financial Networks
检测金融网络中的欺诈行为
来源:ARXIV_20250704
[14] FinAI BERT
FinAI BERT
来源:ARXIV_20250704
[15] Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
基于机器学习的印度金融市场投资组合压力测试框架
来源:ARXIV_20250704
[16] Introducing a New Brexit Related Uncertainty Index
引入新的英国脱欧相关不确定性指数
来源:ARXIV_20250704
[17] Cross-Country Equity Prediction Using a Geographically Informed Machine Learning Model
基于地理信息的机器学习模型的跨国股票预测
来源:SSRN_20250704
[1] Deep Hedging to Manage Tail Risk
标题:深度对冲以管理尾部风险
作者:Yuming Ma
来源:ARXIV_20250701
Abstract : Extending Buehler et al. s 2019 Deep Hedging paradigm, we innovatively employ deep neural networks to parameterize convex risk minimization (CVaR ES) for the portfolio tail risk hedging problem. Through comprehensive numerical experiments on crisis era bootstrap market simulators customizable with transaction costs, risk budgets, liquidity constraints, and market impact our end to end framework......(摘要翻译及全文见知识星球)
Keywords :
[2] Can We Reliably Predict the Fed s Next Move A Multi Modal Approach to U.S. Monetary Policy Forecasting
标题:我们能否可靠地预测美联储的下一步行动？美国货币政策预测的多模式方法
作者:Fiona Xiao Jingyi, Lili Liu
来源:ARXIV_20250701
Abstract : Forecasting central bank policy decisions remains a persistent challenge for investors, financial institutions, and policymakers due to the wide reaching impact of monetary actions. In particular, anticipating shifts in the U.S. federal funds rate is vital for risk management and trading strategies. Traditional methods relying only on structured macroeconomic indicators often fall short in capturing the forward looking cues embedded in......(摘要翻译及全文见知识星球)
Keywords :
[3] Overparametrized models with posterior drift
标题:具有后向漂移的过参数化模型
作者:Guillaume Coqueret, Martial Laguerre
来源:ARXIV_20250701
Abstract : This paper investigates the impact of posterior drift on out of sample forecasting accuracy in overparametrized machine learning models. We document the loss in performance when the loadings of the data generating process change between the training and testing samples. This matters crucially in settings in which regime changes are likely to occur, for instance, in financial markets. Applied to equity......(摘要翻译及全文见知识星球)
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[4] FairMarket RL
标题:公平市场RL
作者:Shrenik Jadhav, Birva Sevak, Srijita Das, Akhtar Hussain, Wencong Su, Van-Hai Bui
来源:ARXIV_20250701
Abstract : Peer to peer (P2P) trading is increasingly recognized as a key mechanism for decentralized market regulation, yet existing approaches often lack robust frameworks to ensure fairness. This paper presents FairMarket RL, a novel hybrid framework that combines Large Language Models (LLMs) with Reinforcement Learning (RL) to enable fairness aware trading agents. In a simulated P2P microgrid with multiple sellers and buyers,......(摘要翻译及全文见知识星球)
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[5] Optimization Method of Multi factor Investment Model Driven by Deep Learning for Risk Control
标题:基于深度学习的风险控制多因素投资模型优化方法
作者:Ruisi Li, Xinhui Gu
来源:ARXIV_20250702
Abstract : Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model s adaptability and robustness to market changes. Empirical analysis shows that the LSTM model......(摘要翻译及全文见知识星球)
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[6] Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules
标题:利用机器学习计算约束最优碳税规则
作者:Felix Kübler, Simon Scheidegger, Oliver Surbek
来源:ARXIV_20250703
Abstract : We develop a computational framework for deriving Pareto improving and constrained optimal carbon tax rules in a stochastic overlapping generations (OLG) model with climate change. By integrating Deep Equilibrium Networks for fast policy evaluation and Gaussian process surrogate modeling with Bayesian active learning, the framework systematically locates optimal carbon tax schedules for heterogeneous agents exposed to climate risk. We apply our......(摘要翻译及全文见知识星球)
Keywords :
[7] Forecasting Nigerian Equity Stock Returns Using Long Short Term Memory Technique
标题:利用长短期记忆技术预测尼日利亚股票收益
作者:Adebola K. Ojo, Ifechukwude Jude Okafor
来源:ARXIV_20250704
Abstract : Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset......(摘要翻译及全文见知识星球)
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[8] News Sentiment Embeddings for Stock Price Forecasting
标题:股价预测的新闻情绪嵌入
作者:Ayaan Qayyum
来源:ARXIV_20250704
Abstract : This paper will discuss how headline data can be used to predict stock prices. The stock price in question is the SPDR S&P 500 ETF Trust, also known as SPY that tracks the performance of the largest 500 publicly traded corporations in the United States. A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict......(摘要翻译及全文见知识星球)
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[9] DeepSupp
标题:深汤
作者:Boris Kriuk, Logic Ng, Zarif Al Hossain
来源:ARXIV_20250704
Abstract : Support and resistance (SR) levels are central to technical analysis, guiding traders in entry, exit, and risk management. Despite widespread use, traditional SR identification methods often fail to adapt to the complexities of modern, volatile markets. Recent research has introduced machine learning techniques to address the following challenges, yet most focus on price prediction rather than structural level identification. This paper......(摘要翻译及全文见知识星球)
Keywords :
[10] Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning
标题:强化学习加速投资组合优化和期权定价
作者:Hadi Keramati, Samaneh Jazayeri
来源:ARXIV_20250704
Abstract : We present a reinforcement learning (RL) driven framework for optimizing block preconditioner sizes in iterative solvers used in portfolio optimization and option pricing. The covariance matrix in portfolio optimization or the discretization of differential operators in option pricing models lead to large linear systems of the form mathbf A textbf x textbf b . Direct......(摘要翻译及全文见知识星球)
Keywords :
[11] Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation
标题:基于LSTM的小波变换卷积和通道注意力集成在基于股票价格预测的投资组合分配中
作者:Junjie Guo
来源:ARXIV_20250704
Abstract : Portfolio allocation via stock price prediction is inherently difficult due to the notoriously low signal to noise ratio of stock time series. This paper proposes a method by integrating wavelet transform convolution and channel attention with LSTM to implement stock price prediction based portfolio allocation. Stock time series data first are processed by wavelet transform convolution to reduce the noise. Processed......(摘要翻译及全文见知识星球)
Keywords :
[12] Forecasting Labor Markets with LSTNet
标题:用LSTNet预测劳动力市场
作者:Adam Nelson-Archer, Aleia Sen, Meena Al Hasani, Sofia Davila, Jessica Le, Omar Abbouchi
来源:ARXIV_20250704
Abstract : We present a deep learning approach for forecasting short term employment changes and assessing long term industry health using labor market data from the U.S. Bureau of Labor Statistics. Our system leverages a Long and Short Term Time series Network (LSTNet) to process multivariate time series data, including employment levels, wages, turnover rates, and job openings. The model outputs both......(摘要翻译及全文见知识星球)
Keywords :
[13] Detecting Fraud in Financial Networks
标题:检测金融网络中的欺诈行为
作者:Linh Nguyen, Marcel Boersma, Erman Acar
来源:ARXIV_20250704
Abstract : Fraudulent activity in the financial industry costs billions annually. Detecting fraud, therefore, is an essential yet technically challenging task that requires carefully analyzing large volumes of data. While machine learning (ML) approaches seem like a viable solution, applying them successfully is not so easy due to two main challenges (1) the sparsely labeled data, which makes the training of such......(摘要翻译及全文见知识星球)
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[14] FinAI BERT
标题:FinAI BERT
作者:Muhammad Bilal Zafar
来源:ARXIV_20250704
Abstract : The proliferation of artificial intelligence (AI) in financial services has prompted growing demand for tools that can systematically detect AI related disclosures in corporate filings. While prior approaches often rely on keyword expansion or document level classification, they fall short in granularity, interpretability, and robustness. This study introduces FinAI BERT, a domain adapted transformer based language model designed to classify AI......(摘要翻译及全文见知识星球)
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[15] Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
标题:基于机器学习的印度金融市场投资组合压力测试框架
作者:Vidya Sagar G, Shifat Ali, Siddhartha P. Chakrabarty
来源:ARXIV_20250704
Abstract : This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address the limitations observed in conventional stress testing through dimensionality reduction and latent factor modeling via Principal Component Analysis and Autoencoders. Building on this, we extend the methodology using Variational Autoencoders,......(摘要翻译及全文见知识星球)
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[16] Introducing a New Brexit Related Uncertainty Index
标题:引入新的英国脱欧相关不确定性指数
作者:Ismet Gocer, Julia Darby, Serdar Ongan
来源:ARXIV_20250704
Abstract : Important game changer economic events and transformations cause uncertainties that may affect investment decisions, capital flows, international trade, and macroeconomic variables. One such major transformation is Brexit, which refers to the multiyear process through which the UK withdrew from the EU. This study develops and uses a new Brexit Related Uncertainty Index (BRUI). In creating this index, we apply Text Mining,......(摘要翻译及全文见知识星球)
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[17] Cross-Country Equity Prediction Using a Geographically Informed Machine Learning Model
标题:基于地理信息的机器学习模型的跨国股票预测
作者:David Seruga,Radovan Vojtko
来源:SSRN_20250704
Abstract : This study demonstrates that the value of a geographically informed machine learning model for global equity returns lies not in its raw predictions, but in the deviations from simple price-based benchmarks like moving averages. Large differences between the model's forecasts and these benchmarks reveal potential mispricings, offering improved signals for tactical country allocation. This approach outperforms traditional indicators and an equally......(摘要翻译及全文见知识星球)
Keywords : Machine Learning, Quantitative Finance, Neural Networks, Deep Learning, Statistical Modelling, Investing, Equity Markets, Return Forecasting, Data-Driven Investing, Financial Modelling
