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[1] Deep Reinforcement Learning for Investor Specific Portfolio Optimization
用于投资者特定投资组合优化的深度强化学习
来源:ARXIV_20250508
[2] Risk sensitive Reinforcement Learning Based on Convex Scoring Functions
基于凸评分函数的风险敏感强化学习
来源:ARXIV_20250508
[3] A new architecture of high order deep neural networks that learn martingales
一种学习鞅的高阶深度神经网络的新架构
来源:ARXIV_20250508
[4] Can Deep Reinforcement Learning Reliably Improve Dynamic Portfolio Allocation?
深度强化学习能否可靠地改善动态投资组合分配？
来源:SSRN_20250508
[5] Error Analysis of Deep PDE Solvers for Option Pricing
期权定价的深度偏微分方程求解器的误差分析
来源:ARXIV_20250509
[1] Deep Reinforcement Learning for Investor Specific Portfolio Optimization
标题:用于投资者特定投资组合优化的深度强化学习
作者:Arishi Orra, Aryan Bhambu, Himanshu Choudhary, Manoj Thakur, Selvaraju Natarajan
来源:ARXIV_20250508
Abstract : Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the......(摘要翻译及全文见知识星球)
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[2] Risk sensitive Reinforcement Learning Based on Convex Scoring Functions
标题:基于凸评分函数的风险敏感强化学习
作者:Shanyu Han, Yang Liu, Xiang Yu
来源:ARXIV_20250508
Abstract : We propose a reinforcement learning (RL) framework under a broad class of risk objectives, characterized by convex scoring functions. This class covers many common risk measures, such as variance, Expected Shortfall, entropic Value at Risk, and mean risk utility. To resolve the time inconsistency issue, we consider an augmented state space and an auxiliary variable and recast the problem as a......(摘要翻译及全文见知识星球)
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[3] A new architecture of high order deep neural networks that learn martingales
标题:一种学习鞅的高阶深度神经网络的新架构
作者:Syoiti Ninomiya, Yuming Ma
来源:ARXIV_20250508
Abstract : A new deep learning neural network architecture based on high order weak approximation algorithms for stochastic differential equations (SDEs) is proposed. The architecture enables the efficient learning of martingales by deep learning models. The behaviour of deep neural networks based on this architecture, when applied to the problem of pricing financial derivatives, is also examined. The core of this new architecture......(摘要翻译及全文见知识星球)
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[4] Can Deep Reinforcement Learning Reliably Improve Dynamic Portfolio Allocation?
标题:深度强化学习能否可靠地改善动态投资组合分配？
作者:Rethyam Gupta,Atharwa Pandey,Adarsh Pandey
来源:SSRN_20250508
Abstract : This paper evaluates the effectiveness of Deep Reinforcement Learning (DRL) for dynamic portfolio allocation and benchmarks its performance against the classical Mean-Variance Optimization (MVO) framework. While DRL has gained significant attention for its ability to learn adaptive trading strategies from highdimensional market environments, its evaluation is often limited to comparisons with other DRL variants rather than established portfolio theory methods. In......(摘要翻译及全文见知识星球)
Keywords : Computational Finance, Deep Reinforcement Learning, Mean-Variance Optimization, Portfolio Allocation, Transaction Costs, Financial Backtesting
[5] Error Analysis of Deep PDE Solvers for Option Pricing
标题:期权定价的深度偏微分方程求解器的误差分析
作者:Jasper Rou
来源:ARXIV_20250509
Abstract : Option pricing often requires solving partial differential equations (PDEs). Although deep learning based PDE solvers have recently emerged as quick solutions to this problem, their empirical and quantitative accuracy remain not well understood, hindering their real world applicability. In this research, our aim is to offer actionable insights into the utility of deep PDE solvers for practical option pricing implementation. Through......(摘要翻译及全文见知识星球)
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