Designing Agentic AI-Based Screening for Portfolio Investment

• URL: http://arxiv.org/abs/2603.23300v1
• Date: Tue, 24 Mar 2026 15:03:40 GMT
• Title: Designing Agentic AI-Based Screening for Portfolio Investment
• Abstract summary: We introduce a new agentic artificial intelligence (AI) platform for portfolio management.<n>Our architecture consists of three layers. First, two large language model (LLM) agents are assigned specialized tasks.<n>Second, these agents deliberate to generate and agree upon buy and sell signals from a large portfolio, substantially narrowing the pool of candidate assets.
• Score: 1.7499351967216341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new agentic artificial intelligence (AI) platform for portfolio management. Our architecture consists of three layers. First, two large language model (LLM) agents are assigned specialized tasks: one agent screens for firms with desirable fundamentals, while a sentiment analysis agent screens for firms with desirable news. Second, these agents deliberate to generate and agree upon buy and sell signals from a large portfolio, substantially narrowing the pool of candidate assets. Finally, we apply a high-dimensional precision matrix estimation procedure to determine optimal portfolio weights. A defining theoretical feature of our framework is that the number of assets in the portfolio is itself a random variable, realized through the screening process. We introduce the concept of sensible screening and establish that, under mild screening errors, the squared Sharpe ratio of the screened portfolio consistently estimates its target. Empirically, our method achieves superior Sharpe ratios relative to an unscreened baseline portfolio and to conventional screening approaches, evaluated on S&P 500 data over the period 2020--2024.

Related papers

• A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management [0.8122270502556375]
  We present a three-phase deep reinforcement learning system for personalized portfolio management.<n>Phase 1 pretrains a ticker-identity-free cross asset encoder via self-supervised learning on a multi-asset corpus.<n>Phase 2 fine-tunes a MoE (Mixture of Experts) portfolio actor with PPO under an objective-conditioned reward.<n>Phase 3 adds a lightweight personalization layer further adapted at inference time to each individual.
  arXiv  Detail & Related papers  (2026-06-30T00:19:59Z)
• Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation [59.362016745945375]
  We propose the first Tri-party LLM-agent Recommendation framework (TriRec)<n>TriRec explicitly coordinates user utility, item exposure, and platform-level fairness.<n>We show consistent gains in accuracy, fairness, and item-level utility.
  arXiv  Detail & Related papers  (2026-03-11T11:40:13Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• CryptoBench: A Dynamic Benchmark for Expert-Level Evaluation of LLM Agents in Cryptocurrency [60.83660377169452]
  This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large Language Model (LLM) agents.<n>Unlike general-purpose agent benchmarks for search and prediction, professional crypto analysis presents specific challenges.
  arXiv  Detail & Related papers  (2025-11-29T09:52:34Z)
• Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market [8.11097322686573]
  We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios.<n>We evaluate the system on the constituents by the CSI 300 Index of China's A-share market and find that it consistently outperforms standard benchmarks.
  arXiv  Detail & Related papers  (2025-10-24T04:38:37Z)
• Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading [57.28635022507172]
  TiMi is a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment.<n>We propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection.
  arXiv  Detail & Related papers  (2025-10-06T13:08:55Z)
• From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions [4.288926547930663]
  We present an end-to-end framework that learns portfolio weights using deep learning.<n>We evaluate the framework on nine U.S. stocks spanning six sectors, chosen to balance sector diversity and news coverage.<n>Although the stock universe is limited, the results underscore the value of integrating price, relational, and sentiment signals for portfolio management.
  arXiv  Detail & Related papers  (2025-09-29T00:42:24Z)
• FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering [57.18367828883773]
  FinAgentBench is a benchmark for evaluating agentic retrieval with multi-step reasoning in finance.<n>The benchmark consists of 26K expert-annotated examples on S&P-500 listed firms.<n>We evaluate a suite of state-of-the-art models and demonstrate how targeted fine-tuning can significantly improve agentic retrieval performance.
  arXiv  Detail & Related papers  (2025-08-07T22:15:22Z)
• Building crypto portfolios with agentic AI [46.348283638884425]
  The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility.<n>This paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
  arXiv  Detail & Related papers  (2025-07-11T18:03:51Z)
• MASS: Muli-agent simulation scaling for portfolio construction [17.363056358369143]
  We introduce the Multi-Agent Scaling Simulation (MASS), a novel framework that leverages multi-agent simulation for direct, end-to-end portfolio construction.<n>At its core, MASS employs a backward optimization process to dynamically learn the optimal distribution of heterogeneous agents.<n>We demonstrate that as the number of agents increases exponentially (up to 512), the aggregated decisions yield progressively higher excess returns.
  arXiv  Detail & Related papers  (2025-05-15T13:27:18Z)
• Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation [0.0]
  We propose a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT. By reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points. The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches.
  arXiv  Detail & Related papers  (2024-04-13T09:10:05Z)
• Deep Learning for Portfolio Optimization [5.833272638548154]
  Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period.
  arXiv  Detail & Related papers  (2020-05-27T21:28:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.