Paper: arXiv 2503.21422

Abstract

Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 1.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper is a survey that reviews AI applications in quant finance, focusing on high-level concepts and evolutionary trends rather than novel mathematical derivations or empirical backtests. It lacks the dense formulas, code, or implementation details characteristic of high-math or high-rigor research.

Research Flowchart

  flowchart TD
  A["Research Goal: AI in Quant Investment"] --> B["Data Sources<br>Traditional + Unstructured Data"]
  B --> C["Methodology Stages<br>1. Traditional Statistical (Human Features)<br>2. Deep Learning (Scalable Models)<br>3. LLMs & Agents (Autonomous Workflow)"]
  C --> D["Computational Processes<br>Alpha Strategy Pipeline<br>Feature Generation → Signal Prediction → Execution"]
  D --> E["Key Findings/Outcomes<br>- AI improves predictive modeling<br>- LLMs extend beyond prediction<br>- Autonomous agents enable self-iterative workflows<br>- Potential paradigm shift in asset management"]

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  360. A Survey on Financial Applications of Metaheuristics (2017) — link
  361. The Next Decade in AI : Four Steps Towards Robust Artificial Intelligence (2020) — link
  362. A Survey on Gaps between Mean - Variance Approach and Exponential Growth Rate Approach for Portfolio Optimization (2022) — link
  363. Multiagent-based deep reinforcement learning for risk-shifting portfolio management (2022) — link
  364. GPM : A graph convolutional network based reinforcement learning framework for portfolio management (2022) — link
  365. Dynamic portfolio rebalancing through reinforcement learning (2022) — link
  366. Cost- Sensitive Portfolio Selection via Deep Reinforcement Learning (2022) — link
  367. High- Dimensional Stock Portfolio Trading with Deep Reinforcement Learning (2022) — link
  368. MetaTrader : An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization (2022) — link
  369. Objective Driven Portfolio Construction Using Reinforcement Learning (2022) — link
  370. Hypergraph- Based Reinforcement Learning for Stock Portfolio Selection (2022) — link
  371. A Hierarchical Reinforcement Learning Framework for Stock Selection and Portfolio (2022) — link
  372. Federated Reinforcement Learning for Portfolio Management (2022) — link
  373. A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management — → 2212.14477
  374. Portfolio management system in equity market neutral using reinforcement learning (2021) — link
  375. Deep reinforcement learning for portfolio management of markets with a dynamic number of assets (2021) — link
  376. Portfolio optimization with return prediction using deep learning and machine learning (2021) — link
  377. Deep graph convolutional reinforcement learning for financial portfolio management - DeepPocket (2021) — link
  378. XPM : An Explainable Deep Reinforcement Learning Framework for Portfolio Management (2021) — link
  379. Evolutionary meta reinforcement learning for portfolio optimization (2021) — link
  380. Explainable deep reinforcement learning for portfolio management: an empirical approach (2021) — link
  381. An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning (2021) — link
  382. Continuous- Action Reinforcement Learning for Portfolio Allocation of a Life Insurance Company (2021) — link
  383. Continuous control with Stacked Deep Dynamic Recurrent Reinforcement Learning for portfolio optimization (2020) — link
  384. An intelligent financial portfolio trading strategy using deep Q -learning (2020) — link
  385. Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder - DeepBreath (2020) — link
  386. Portfolio formation with preselection using deep learning from long-term financial data (2020) — link
  387. Portfolio management via two-stage deep learning with a joint cost (2020) — link
  388. Portfolio trading system of digital currencies: A deep reinforcement learning with multidimensional attention gating mechanism (2020) — link
  389. A deep Q -learning portfolio management framework for the cryptocurrency market (2020) — link
  390. Deep- Learning Solution to Portfolio Selection with Serially Dependent Returns (2020) — link
  391. A Two -level Reinforcement Learning Algorithm for Ambiguous Mean -variance Portfolio Selection Problem (2020) — link
  392. MAPS : Multi - Agent reinforcement learning-based Portfolio management System (2020) — link
  393. Adaptive rolling window selection for minimum variance portfolio estimation based on reinforcement learning (2020) — link
  394. Deep Reinforcement Learning ( DRL ) for Portfolio Allocation (2020) — link
  395. A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning (2019) — link
  396. Continuous-time reinforcement learning approach for portfolio management with time penalization (2019) — link
  397. Deep Learning Algorithm to solve Portfolio Management with Proportional Transaction Cost (2019) — link
  398. Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization (2019) — link
  399. Continuous- Time Mean - Variance Portfolio Optimization via Reinforcement Learning (2019) — link
  400. Large scale continuous-time mean-variance portfolio allocation via reinforcement learning (2019) — link
  401. Reinforcement Learning for Portfolio Management (2018) — link
  402. Dynamic Portfolio Management with Reinforcement Learning (2019) — link
  403. A Reinforcement Learning Approach for Solving the Mean Variance Customer Portfolio in Partially Observable Models (2018) — link
  404. An Asynchronous Advantage Actor - Critic Reinforcement Learning Method for Stock Selection and Portfolio Management (2018) — link
  405. An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown (2017) — link
  406. A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean-variance customer portfolio: The case of bank marketing campaigns (2015) — link
  407. Reinforcement Learning Method for Portfolio Optimal Frontier Search (2013) — link
  408. Reinforcement Learning for Trading Systems and Portfolios (1998) — link
  409. AlphaPortfolio : Direct Construction Through Deep Reinforcement Learning and Interpretable AI (2021) — link
  410. Reinforcement Learning for Quantitative Trading (2023) — link
  411. MSPM : A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management (2022) — link
  412. Deep Portfolio Optimization via Distributional Prediction of Residual Factors (2021) — link
  413. Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory (2023) — link
  414. Adversarial Deep Reinforcement Learning in Portfolio Management (2018) — link
  415. Reinforcement Learning for Trading (1998) — link
  416. Learning to trade via direct reinforcement (2001) — link
  417. Cost- Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment (2022) — link
  418. An Agent - Based Market Simulator for Back - Testing Deep Reinforcement Learning Based Trade Execution Strategies (2021) — link
  419. Investigating the Robustness and Generalizability of Deep Reinforcement Learning Based Optimal Trade Execution Systems (2021) — link
  420. TradeR : Practical Deep Hierarchical Reinforcement Learning for Trade Execution (2021) — link
  421. A Deep Reinforcement Learning Framework for Optimal Trade Execution (2020) — link
  422. A reinforcement learning extension to the Almgren - Chriss framework for optimal trade execution (2014) — link
  423. Optimal execution of portfolio transactions (2000)
  424. Optimal Execution : A Review (2022) — link
  425. Optimal control of execution costs (1998) — link
  426. Universal Trading for Order Execution with Oracle Policy Distillation (2021) — link
  427. An End -to- End Optimal Trade Execution Framework based on Proximal Policy Optimization (2020) — link
  428. Online Learning in Limit Order Book Trade Execution (2018) — link
  429. A reinforcement learning approach to optimal execution (2022) — link
  430. Double Deep Q - Learning for Optimal Execution (2021) — link
  431. Deep learning for limit order books (2019) — link
  432. Optimal order placement in limit order markets (2017) — link
  433. Deep Execution - Value and Policy Based Reinforcement Learning for Trading and Beating Market Benchmarks (2019) — link
  434. Global Wealth 2022: Standing Still Is Not an Option (2022) — link
  435. Gpt-4 technical report (2023) — link
  436. Bert: Pre-training of deep bidirectional transformers for language understanding (2018) — link
  437. Transformer-based attention network for stock movement prediction (2022)
  438. A review on graph neural network methods in financial applications (2021) — link
  439. Stock closing price prediction based on sentiment analysis and LSTM (2020)
  440. Textual Analysis of Stock Market Prediction Using Breaking Financial News: The AZFin Text System (2009) — link
  441. Using News Articles to Predict Stock Price Movements (2001)
  442. Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media (2013)
  443. Correlating Financial Time Series with Micro-blogging Activity (2012)
  444. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining (2010)
  445. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings (1999)
  446. VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text (2015)
  447. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) — link
  448. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019) — link
  449. FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications — → 2403.12285
  450. Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — link
  451. FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining (2020) — link
  452. RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019) — link
  453. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2023) — link
  454. Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements? (2023) — link
  455. Modeling News Interactions and Influence for Financial Market Prediction (2024) — link
  456. Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023) — link
  457. A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News (2022) — link
  458. Language Models are Few-Shot Learners (2020)
  459. GPT-4 Technical Report (2023) — link
  460. Financial Statement Analysis with Large Language Models (2024) — link
  461. The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over Multimodal Stock Movement Prediction Challenges (2023) — link
  462. GPT’s Idea of Stock Factors (2024) — link
  463. Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment (2023) — link
  464. Large Language Models Are Zero-Shot Time Series Forecasters (2023) — link
  465. S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting (2024) — link
  466. TIME-LLM: Time Series Forecasting by Reprogramming Large Language Models (2024) — link
  467. RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data (2024) — link
  468. Temporal Data Meets LLM–Explainable Financial Time Series Forecasting (2023) — link
  469. Adaptive l 1-regularization for short-selling control in portfolio selection (2019)
  470. Optimal portfolio selection using regularization (2011)
  471. Managing portfolio diversity within the mean variance theory (2019)
  472. Portfolio optimization with noisy covariance matrices (2019)
  473. Estimated correlation matrices and portfolio optimization (2004)
  474. Online portfolio selection: A survey (2014)
  475. Optimal Liquidation under Stochastic Liquidity (2016)
  476. Optimal portfolio liquidation with execution cost and risk (2010)
  477. Optimal trade execution: A mean quadratic variation approach (2012)
  478. Optimal execution with nonlinear impact functions and trading-enhanced risk (2003)
  479. Optimal Trading with Stochastic Liquidity and Volatility (2012)
  480. Optimal Trading with Signals and Stochastic Price Impact (2021)
  481. Reinforcement learning for optimized trade execution (2006)
  482. A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution (2014)
  483. Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon (2020)
  484. End-to-end risk budgeting portfolio optimization with neural networks (2024)
  485. End-to-end, decision-based, cardinality-constrained portfolio optimization (2025)
  486. Optimal execution: A review (2022)
  487. FinMem: A performance-enhanced LLM trading agent with layered memory and character design (2024)
  488. A multimodal foundation agent for financial trading: Tool-augmented, diversified, and generalist (2024)
  489. FinRobot: an open-source AI agent platform for financial applications using large language models — → 2405.14767
  490. Fincon: A synthesized llm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making (2025)
  491. INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent — → 2412.18174
  492. FinVision: A Multi-Agent Framework for Stock Market Prediction (2024)
  493. TradingAgents: Multi-Agents LLM Financial Trading Framework — → 2412.20138
  494. Bloomberggpt: A large language model for finance (2023) — link
  495. Fingpt: Open-source financial large language models — → 2306.06031
  496. Pixiu: A large language model, instruction data and evaluation benchmark for finance (2023) — link
  497. CFGPT: Chinese financial assistant with large language model (2023) — link
  498. DISC-FinLLM: A Chinese financial large language model based on multiple experts fine-tuning (2023) — link
  499. Xuanyuan 2.0: A large chinese financial chat model with hundreds of billions parameters (2023)
  500. Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2024) — link
  501. MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time Correlations (2024) — link
  502. Multi-Relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification — → 2401.05430
  503. Diffstock: Probabilistic relational stock market predictions using diffusion models (2024)
  504. A review of Deep Generative Models for Synthetic Financial Data Generation (2024)
  505. Tabular and latent space synthetic data generation: a literature review (2023)
  506. A review of synthetic data generation methods for privacy preserving data publishing (2017)
  507. Findiff: Diffusion models for financial tabular data generation (2023)
  508. Agent-based simulation of a financial market (2001)
  509. Artificial economic life: a simple model of a stockmarket (1994)
  510. Multivariate GARCH models: a survey (2006)
  511. Towards realistic market simulations: a generative adversarial networks approach (2021)
  512. Generating Realistic Stock Market Order Streams (2020)
  513. Stochastic backpropagation and approximate inference in deep generative models (2014)
  514. Auto-encoding variational bayes (2013) — link
  515. Time series simulation by conditional generative adversarial net (2019) — link
  516. Data augmentation for improving heating load prediction of heating substation based on TimeGAN (2022)
  517. Generative adversarial nets (2014)
  518. Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns (2024)
  519. Stock market prediction on high-frequency data using generative adversarial nets (2018)
  520. Attention is all you need (2017)
  521. Learning to simulate realistic limit order book markets from data as a World Agent (2022)
  522. Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness (2023)
  523. Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context (2024)
  524. Tail-gan: Learning to simulate tail risk scenarios (2022) — link
  525. Fin-gan: Forecasting and classifying financial time series via generative adversarial networks (2024)
  526. Quant GANs: deep generation of financial time series (2020)
  527. A simulation analysis of the microstructure of double auction markets (2002)
  528. The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows (2009)
  529. Generating realistic stock market order streams (2020)
  530. Deeplob: Deep convolutional neural networks for limit order books (2019)
  531. MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model — → 2409.07486
  532. Controllable Financial Market Generation with Diffusion Guided Meta Agent (2024) — link
  533. Scaling and criticality in a stochastic multi-agent model of a financial market (1999)
  534. ABIDES: Towards High-Fidelity Multi-Agent Market Simulation (2020)
  535. Spoofing the Limit Order Book: An Agent-Based Model (2017)
  536. 101 formulaic alphas (2016)
  537. Stock price prediction via discovering multi-frequency trading patterns (2017)
  538. Finding Alphas: A quantitative approach to building trading strategies (2019)
  539. Generating synergistic formulaic alpha collections via reinforcement learning — → 2306.12964
  540. Alphaevolve: A learning framework to discover novel alphas in quantitative investment (2021)
  541. Alpha Mining and Enhancing via Warm Start Genetic Programming for Quantitative Investment — → 2412.00896
  542. Autoalpha: an efficient hierarchical evolutionary algorithm for mining alpha factors in quantitative investment (2020) — link
  543. Hist: A graph-based framework for stock trend forecasting via mining concept-oriented shared information (2021) — link
  544. Factorvae: A probabilistic dynamic factor model based on variational autoencoder for predicting cross-sectional stock returns (2022)
  545. Applications of genetic programming to finance and economics: past, present, future (2020)
  546. When ai meets finance (stockagent): Large language model-based stock trading in simulated real-world environments — → 2407.18957
  547. Alpha-GPT 2.0: Human-in-the-Loop AI for quantitative investment — → 2402.09746
  548. Is deep learning for image recognition applicable to stock market prediction? (2019)
  549. CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)
  550. Stock prediction using convolutional neural network (2018)
  551. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms (2019)
  552. Deep learning using risk-reward function for stock market prediction (2018)
  553. Trade when opportunity comes: price movement forecasting via locality-aware attention and iterative refinement labeling (2021) — link
  554. Explainable stock price movement prediction using contrastive learning (2024)
  555. A Universal and Interpretable Method for Enhancing Stock Price Prediction (2024) — link
  556. Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies — → 2307.05522
  557. Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction. (2021)
  558. Modeling Interactions Between Stocks Using LLM-Enhanced Graphs for Volume Prediction (2025)
  559. Modal-adaptive knowledge-enhanced graph-based financial prediction from monetary policy conference calls with LLM (2024) — link
  560. Findkg: Dynamic knowledge graph with large language models for global finance (2023)
  561. REFinD: Relation extraction financial dataset (2023)
  562. Company similarity using large language models — → 2308.08031
  563. Gpt-finre: in-context learning for financial relation extraction using large language models (2023) — link

15 of 571 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.