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[1] Zero Shot Forecasting Mortality Rates
零射预测死亡率
来源:ARXIV_20250521
[2] CATS
猫
来源:ARXIV_20250521
[3] Quantum Reservoir Computing for Realized Volatility Forecasting
实现波动预测的量子储层计算
来源:ARXIV_20250521
[4] AI Shrinkage: A Data-Driven Approach for Risk-Optimized Portfolios
AI收缩：一种风险优化投资组合的数据驱动方法
来源:SSRN_20250521
[5] Liquidity provision with reset strategies
具有重置策略的流动性准备金
来源:ARXIV_20250522
[6] Deep Learning for Continuous time Stochastic Control with Jumps
具有跳跃的连续时间随机控制的深度学习
来源:ARXIV_20250522
[7] Interpretable Machine Learning for Macro Alpha
Macro Alpha的可解释机器学习
来源:ARXIV_20250523
[8] Machine learning approach to stock price crash risk
股票价格崩盘风险的机器学习方法
来源:ARXIV_20250523
[9] Spatio-Temporal Fusion for Large-Scale Construction Investment Valuation: Integrating Gated Attention and Temporal Gating
大规模建设投资评估的时空融合：门控注意力和时间门控的结合
来源:SSRN_20250523
[10] A Unified Framework for Multi-Agent Reinforcement Learning
多智能体强化学习的统一框架
来源:SSRN_20250524
[1] Zero Shot Forecasting Mortality Rates
标题:零射预测死亡率
作者:Gabor Petnehazi, Laith Al Shaggah, Jozsef Gall, Bernadett Aradi
来源:ARXIV_20250521
Abstract : This study explores the potential of zero shot time series forecasting, an innovative approach leveraging pre trained foundation models, to forecast mortality rates without task specific fine tuning. We evaluate two state of the art foundation models, TimesFM and CHRONOS, alongside traditional and machine learning based methods across three forecasting horizons (5, 10, and 20 years) using data from 50 countries......(摘要翻译及全文见知识星球)
Keywords :
[2] CATS
标题:猫
作者:Yingjie Kuang, Tianchen Zhang, Zhen-Wei Huang, Zhongjie Zeng, Zhe-Yuan Li, Ling Huang, Yuefang Gao
来源:ARXIV_20250521
Abstract : Accurately predicting customers purchase intentions is critical to the success of a business strategy. Current researches mainly focus on analyzing the specific types of products that customers are likely to purchase in the future, little attention has been paid to the critical factor of whether customers will engage in repurchase behavior. Predicting whether a customer will make the next purchase......(摘要翻译及全文见知识星球)
Keywords :
[3] Quantum Reservoir Computing for Realized Volatility Forecasting
标题:实现波动预测的量子储层计算
作者:Qingyu Li, Chiranjib Mukhopadhyay, Abolfazl Bayat, Ali Habibnia
来源:ARXIV_20250521
Abstract : Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high dimensional time series. As with many data driven disciplines, quantitative finance and econometrics can hugely benefit......(摘要翻译及全文见知识星球)
Keywords :
[4] AI Shrinkage: A Data-Driven Approach for Risk-Optimized Portfolios
标题:AI收缩：一种风险优化投资组合的数据驱动方法
作者:Gianluca De Nard,Damjan Kostovic
来源:SSRN_20250521
Abstract : The paper introduces a new type of shrinkage estimation that is not based on asymptotic optimality but uses artificial intelligence (AI) techniques to shrink the sample eigenvalues. The proposed AI Shrinkage estimator applies to both linear and nonlinear shrinkage, demonstrating improved performance compared to the classic shrinkage estimators. Our results demonstrate that reinforcement learning solutions identify a downward bias in classic......(摘要翻译及全文见知识星球)
Keywords : Covariance matrix estimation, linear and nonlinear shrinkage, portfolio management reinforcement learning, risk optimization JEL Classification: C13, C58, G11
[5] Liquidity provision with reset strategies
标题:具有重置策略的流动性准备金
作者:Andrey Urusov, Rostislav Berezovskiy, Anatoly Krestenko, Andrei Kornilov
来源:ARXIV_20250522
Abstract : Since the launch of Uniswap and other AMM protocols, the DeFi industry has evolved from simple constant product functions with uniform liquidity distribution across the entire price axis to more advanced mechanisms that allow Liquidity Providers (LPs) to concentrate capital within selected price ranges. This evolution has introduced new research challenges focused on optimizing capital allocation in Decentralized Exchanges (DEXs) under......(摘要翻译及全文见知识星球)
Keywords :
[6] Deep Learning for Continuous time Stochastic Control with Jumps
标题:具有跳跃的连续时间随机控制的深度学习
作者:Patrick Cheridito, Jean-Loup Dupret, Donatien Hainaut
来源:ARXIV_20250522
Abstract : In this paper, we introduce a model based deep learning approach to solve finite horizon continuous time stochastic control problems with jumps. We iteratively train two neural networks one to represent the optimal policy and the other to approximate the value function. Leveraging a continuous time version of the dynamic programming principle, we derive two different training objectives based on......(摘要翻译及全文见知识星球)
Keywords :
[7] Interpretable Machine Learning for Macro Alpha
标题:Macro Alpha的可解释机器学习
作者:Yuke Zhang
来源:ARXIV_20250523
Abstract : This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project s worldwide news feed using FinBERT a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance specific language to construct daily sentiment indices incorporating......(摘要翻译及全文见知识星球)
Keywords :
[8] Machine learning approach to stock price crash risk
标题:股票价格崩盘风险的机器学习方法
作者:Abdullah Karasan, Ozge Sezgin Alp, Gerhard-Wilhelm Weber
来源:ARXIV_20250523
Abstract : In this study, we propose a novel machine learning based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of......(摘要翻译及全文见知识星球)
Keywords :
[9] Spatio-Temporal Fusion for Large-Scale Construction Investment Valuation: Integrating Gated Attention and Temporal Gating
标题:大规模建设投资评估的时空融合：门控注意力和时间门控的结合
作者:Fatemeh Mostofi,Vedat Togan,Onur Behzat Tokdemir
来源:SSRN_20250523
Abstract : This study developed a novel parallel elementwise fusion strategy tailored to latent engineering data, an underexplored domain compared to the extensively studied visual and traffic-based applications. Specifically, construction investment valuation is a high-stakes decision-making process that determines the financial viability and risk exposure of multi-million-dollar projects, requiring an understanding of relational information dependencies and the temporality of project risks. Multi-criteria decision-making......(摘要翻译及全文见知识星球)
Keywords : Graph neural networks (GNN), Recurrent Neural Networks (RNN), Construction Investment Valuation, Spatio-Temporal Fusion, Data-Driven Decision Support, Machine Learning (ML)
[10] A Unified Framework for Multi-Agent Reinforcement Learning
标题:多智能体强化学习的统一框架
作者:Miquel Noguer I Alonso,Fernando Arias
来源:SSRN_20250524
Abstract : This paper establishes a rigorous unified mathematical framework for multi-agent reinforcement learning (MARL). We develop measure-theoretic foundations for MARL through the lens of stochastic games and prove precise equivalence relationships between fundamental approaches: mean field approximations, centralized training with decentralized execution (CTDE), and group-based policy optimization methods. Unlike previous works that rely on informal arguments, we provide complete proofs with explicit......(摘要翻译及全文见知识星球)
Keywords : Multi-agent reinforcement learning, Markov games, Mean field approximation, Centralized training decentralized execution, Policy optimization, Convergence analysis, Function approximation
