PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance

• URL: http://arxiv.org/abs/2606.06823v1
• Date: Fri, 05 Jun 2026 01:56:28 GMT
• Title: PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance
• Abstract summary: textbfPandaAI is a closed-loop neuro-symbolic LLM agent with market regime modeling and constrained alpha generation.<n>Our experiments show textbfPandaAI achieves a $18.2%$ higher Rank IC and $25.7%$ lower maximum drawdown than state-of-the-art time-series models.
• Score: 11.790070440436294
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While deep learning has excelled in various domains, its application to sequential decision-making in finance remains challenging due to the low Signal-to-Noise Ratio (SNR) and non-stationarity of financial data. Leveraging the reasoning capabilities of Large Language Models (LLMs), we propose \textbf{PandaAI}, a closed-loop neuro-symbolic LLM agent with market regime modeling and constrained alpha generation, which bridges general LLM reasoning with financial rigor and suppresses the financial toxicity of LLM-generated outputs. To bridge the gap between general linguistic capability and financial rigor, we fine-tune a domain-specific LLM. Furthermore, we integrate this LLM into a modular architecture and form a closed-loop system. Unlike traditional models that optimize isolated prediction metrics, \textbf{PandaAI} is designed as a neuro-symbolic agent that navigates the complex, real-world financial environment with explicit risk awareness. Extensive experiments on CSI 300 stock data show that \textbf{PandaAI} achieves a $18.2\%$ higher Rank IC and $25.7\%$ lower maximum drawdown than state-of-the-art time-series models. Our constrained LLM generation and dual-channel adaptation method provide a general paradigm for LLM deployment in high-stakes sequential decision-making scenarios.

Related papers

• Evolutionary Rule Extraction from Corporate Default Prediction Models [0.0]
  Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies.<n>Recent advances in machine learning (ML) have improved predictive performance in credit risk modeling.<n>This study investigates SME's default predictors and applies explainable artificial intelligence (XAI) techniques to them.
  arXiv  Detail & Related papers  (2026-05-28T07:07:01Z)
• $φ$-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models [58.217707070069885]
  This paper presents a novel Fairness Direct Preference Optimization (FaiDPO or $$-DPO) framework for continual learning in LMMs.<n>We first propose a new continual learning paradigm based on Direct Preference Optimization (DPO) to mitigate catastrophic forgetting by aligning learning with pairwise preference signals.<n> Extensive experiments and ablation studies show the proposed $$-DPO achieves State-of-the-Art performance across multiple benchmarks.
  arXiv  Detail & Related papers  (2026-02-26T04:14:33Z)
• Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection [64.75447949495307]
  Large language models (LLMs) have been widely applied across various domains of finance.<n> behavioral biases can lead to instability and uncertainty in decision-making.<n>mfmdscen is a benchmark for evaluating behavioral biases in mfmd across diverse economic scenarios.
  arXiv  Detail & Related papers  (2026-01-08T22:00:32Z)
• LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models [7.216206616406649]
  Large language models (LLMs) like BloombergGPT and FinMA have set new benchmarks across various financial NLP tasks.<n>We propose Layer-wise Adaptive Ensemble Tuning (LAET), a novel strategy that selectively fine-tunes the most effective layers of pre-trained LLMs.<n>Our approach shows strong results in financial NLP tasks, outperforming existing benchmarks and state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-11-14T13:57:46Z)
• The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications [4.211847212372977]
  The LLM Pro Finance Suite is a collection of five instruction-tuned large language models (LLMs) specifically designed for financial applications.<n>Our approach focuses on enhancing generalist instruction-tuned models, leveraging their existing strengths in instruction following, reasoning, and toxicity control.<n>We evaluate the Suite on a comprehensive financial benchmark suite, demonstrating consistent improvement over state-of-the-art baselines in finance-oriented tasks and financial translation.
  arXiv  Detail & Related papers  (2025-11-07T11:08:31Z)
• 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)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Demystifying Domain-adaptive Post-training for Financial LLMs [87.28855088465197]
  FINDAP is a systematic and fine-grained investigation into domain-adaptive post-training of large language models.<n>Our approach consists of four key components: FinCap, FinRec, FinTrain and FinEval.<n>The resulting model, Llama-Fin, achieves, state-of-the-art performance across a wide range of financial tasks.
  arXiv  Detail & Related papers  (2025-01-09T04:26:15Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models [11.154814189699735]
  Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks. We introduce a retrieval-augmented LLMs framework for financial sentiment analysis. Our approach achieves 15% to 48% performance gain in accuracy and F1 score.
  arXiv  Detail & Related papers  (2023-10-06T05:40:23Z)
• Large Language Models in Finance: A Survey [12.243277149505364]
  Large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance.
  arXiv  Detail & Related papers  (2023-09-28T06:04:04Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [48.87381259980254]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training.<n>Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)

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.