Sparse Non-Convex Optimization For Higher Moment Portfolio Management

MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent i…

Identifying Small Mean Reverting Portfolios

Given multivariate time series, we study the problem of forming portfolios with maximum mean reversion while constraining the number of assets in these portfolios. We show that it can be formulated as a sparse canonical correlation analysis and study various algorithms to solve the corresponding sparse generalized eigenvalue problems. After discussing penalized parameter estimation procedures, we study the sparsity versus predictability tradeoff and the impact of predictability in various markets.

Adaptive Predictive Portfolio Management Agent

The paper presents an advanced version of an adaptive market-making agent capable of performing experiential learning, exploiting a "try and fail" approach relying on a swarm of subordinate agents executed in a virtual environment to determine optimal strategies. The problem is treated as a "Narrow AGI" problem with the scope of goals and environments bound to financial markets, specifically crypto-markets. Such an agent is called an "adaptive multi-strategy agent" as it executes multiple strategies virtually and selects only a few for real execution. The presented version of the agent is exte…

Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket

Portfolio management aims at maximizing the return on investment while minimizing risk by continuously reallocating the assets forming the portfolio. These assets are not independent but correlated during a short time period. A graph convolutional reinforcement learning framework called DeepPocket is proposed whose objective is to exploit the time-varying interrelations between financial instruments. These interrelations are represented by a graph whose nodes correspond to the financial instruments while the edges correspond to a pair-wise correlation function in between assets. DeepPocket con…

利用深度学习预测分析优化数字资产的投资组合管理与风险评估

Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis

本文提出将DQN算法以新颖直接的方式应用于数字资产投资组合管理，通过选择最大Q值的资产与动作进行决策，并针对不同资产间Q值漂移问题提出共享Q值分布的约束改进，实验表明该方法显著超越基准。

Risk-Management Methods for the Libor Market Model Using Semidefinite Programming

When interest rate dynamics are described by the Libor Market Model as in BGM97, we show how some essential risk-management results can be obtained from the dual of the calibration program. In particular, if the objetive is to maximize another swaption's price, we show that the optimal dual variables describe a hedging portfolio in the sense of \cite{Avel96}. In the general case, the local sensitivity of the covariance matrix to all market movement scenarios can be directly computed from the optimal dual solution. We also show how semidefinite programming can be used to manage the Gamma exposu…

面向可定制股票池投资组合管理的带可掩码股票表示的强化学习

Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools

针对可定制股票池投资组合管理需频繁重训的问题，本文提出EarnMore框架，通过可掩码股票表示、自监督掩蔽重构和重新加权机制实现全局股票池一次性训练，在8个美国股票子池上以超40%利润提升显著优于14个基线。

OpenPM：面向LLM投资组合管理智能体的可审计时点评估框架

OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agents

OpenPM是面向LLM投资组合管理智能体的可审计时点评估框架，构建了分层分配器参考智能体，发现分析师质量比构造器选择更重要，换手率是主要成本驱动因素。

面向情绪的均值-方差投资组合优化用于加密货币

Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies

本文提出一种结合技术指标和情绪分析的加密货币投资策略，通过均值-方差优化提升风险调整收益，但需进一步改进稳定性。

基于图时空学习的危机韧性投资组合管理

Crisis-Resilient Portfolio Management via Graph-based Spatio-Temporal Learning

针对危机期间资产关联结构转变导致传统图方法失效的问题，提出CRISP框架，利用图卷积、BiLSTM和注意力机制自适应学习稀疏关联，在2005-2024年多类危机数据上实现夏普比率3.76，较基线提升707%。