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[1] Stealing Accuracy
盗窃准确性
来源:ARXIV_20250818
[2] Optimal Portfolio Construction A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL BHRP) Approach
最优投资组合构建——一种嵌入强化学习的贝叶斯分层风险奇偶性（RL-BHRP）方法
来源:ARXIV_20250819
[3] EXOTIC
异国的
来源:ARXIV_20250819
[4] A Category Theory Framework for Macroeconomic Modeling
宏观经济建模的范畴理论框架
来源:ARXIV_20250820
[5] AlphaX
在电场中会变色的特种试纸
来源:ARXIV_20250820
[6] AlphaEval
AlphaEval 的
来源:ARXIV_20250820
[7] Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions
基于图学习和综合多头头寸的期权市场统计套利
来源:ARXIV_20250821
[8] Variable selection for minimum variance portfolios
最小方差投资组合的变量选择
来源:ARXIV_20250822
[9] Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
基于现代投资组合理论和深度学习模型的投资组合优化
来源:ARXIV_20250822
[10] Generative Neural Operators of Log Complexity Can Simultaneously Solve Infinitely Many Convex Programs
对数复杂度的生成神经算子可以同时求解无穷多个凸程序
来源:ARXIV_20250822
[1] Stealing Accuracy
标题:盗窃准确性
作者:Arkadiusz Lipiecki, Kaja Bilinska, Nicolaos Kourentzes, Rafal Weron
来源:ARXIV_20250818
Abstract : We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day ahead electricity prices and show that reconciling forecasts for hourly products, 2 to 12 hour blocks, and baseload contracts significantly (up to 13 ) improves accuracy at all levels. These results remain consistent throughout a challenging 4 year test period (2021 2024) in the German power market and......(摘要翻译及全文见知识星球)
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[2] Optimal Portfolio Construction A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL BHRP) Approach
标题:最优投资组合构建——一种嵌入强化学习的贝叶斯分层风险奇偶性（RL-BHRP）方法
作者:Shaofeng Kang, Zeying Tian
来源:ARXIV_20250819
Abstract : We propose a two level, learning based portfolio method (RL BHRP) that spreads risk across sectors and stocks, and adjusts exposures as market conditions change. Using U.S. Equities from 2012 to mid 2025, we design the model using 2012 to 2019 data, and evaluate it out of sample from 2020 to 2025 against a sector index built from exchange traded funds......(摘要翻译及全文见知识星球)
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[3] EXOTIC
标题:异国的
作者:Chinmay Maheshwari, Chinmay Pimpalkhare, Debasish Chatterjee
来源:ARXIV_20250819
Abstract : Min max optimization arises in many domains such as game theory, adversarial machine learning, etc., with gradient based methods as a typical computational tool. Beyond convex concave min max optimization, the solutions found by gradient based methods may be arbitrarily far from global optima. In this work, we present an algorithmic apparatus for computing globally optimal solutions in convex non concave......(摘要翻译及全文见知识星球)
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[4] A Category Theory Framework for Macroeconomic Modeling
标题:宏观经济建模的范畴理论框架
作者:Luciano Pollicino
来源:ARXIV_20250820
Abstract : Traditional macroeconomic models, based on static algebraic systems, fail to capture the dynamics of a bimonetary economy like Argentina s. This paper proposes a framework based on category theory to develop a more flexible and structured model that represents the evolving relationships between key variables such as inflation expectations, interest rates, and currency demand. Using concepts like objects, morphisms, learning forgetful......(摘要翻译及全文见知识星球)
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[5] AlphaX
标题:在电场中会变色的特种试纸
作者:Paulo André Lima de Castro
来源:ARXIV_20250820
Abstract : Autonomous trading strategies have been a subject of research within the field of artificial intelligence (AI) for aconsiderable period. Various AI techniques have been explored to develop autonomous agents capable of trading financial assets. These approaches encompass traditional methods such as neural networks, fuzzy logic, and reinforcement learning, as well as more recent advancements, including deep neural networks and deep reinforcement......(摘要翻译及全文见知识星球)
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[6] AlphaEval
标题:AlphaEval 的
作者:Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, Ming Zhang
来源:ARXIV_20250820
Abstract : Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches such as genetic programming, reinforcement learning, and large language models have significantly expanded the capacity for alpha discovery, systematic evaluation remains a key challenge. Existing evaluation metrics predominantly include backtesting and correlation based measures. Backtesting is computationally intensive, inherently sequential, and sensitive......(摘要翻译及全文见知识星球)
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[7] Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions
标题:基于图学习和综合多头头寸的期权市场统计套利
作者:Yoonsik Hong, Diego Klabjan
来源:ARXIV_20250821
Abstract : Statistical arbitrages (StatArbs) driven by machine learning has garnered considerable attention in both academia and industry. Nevertheless, deep learning (DL) approaches to directly exploit StatArbs in options markets remain largely unexplored. Moreover, prior graph learning (GL) a methodological basis of this paper studies overlooked that features are tabular in many cases and that tree based......(摘要翻译及全文见知识星球)
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[8] Variable selection for minimum variance portfolios
标题:最小方差投资组合的变量选择
作者:Guilherme V. Moura, André P. Santos, Hudson S. Torrent
来源:ARXIV_20250822
Abstract : Machine learning (ML) methods have been successfully employed in identifying variables that can predict the equity premium of individual stocks. In this paper, we investigate if ML can also be helpful in selecting variables relevant for optimal portfolio choice. To address this question, we parameterize minimum variance portfolio weights as a function of a large pool of firm level characteristics as......(摘要翻译及全文见知识星球)
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[9] Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
标题:基于现代投资组合理论和深度学习模型的投资组合优化
作者:Maciej Wysocki, Paweł Sakowski
来源:ARXIV_20250822
Abstract : This paper investigates an important problem of an appropriate variance covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models DeepVAR......(摘要翻译及全文见知识星球)
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[10] Generative Neural Operators of Log Complexity Can Simultaneously Solve Infinitely Many Convex Programs
标题:对数复杂度的生成神经算子可以同时求解无穷多个凸程序
作者:Anastasis Kratsios, Ariel Neufeld, Philipp Schmocker
来源:ARXIV_20250822
Abstract : Neural operators (NOs) are a class of deep learning models designed to simultaneously solve infinitely many related problems by casting them into an infinite dimensional space, whereon these NOs operate. A significant gap remains between theory and practice worst case parameter bounds from universal approximation theorems suggest that NOs may require an unrealistically large number of parameters to solve most......(摘要翻译及全文见知识星球)
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