Stock Market Forecasting Methods

Últimos artículos

• PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

  Duong Hien Chi Kien, Thanh Trung Huynh · 2 de octubre de 2026

  Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passive long exposure on upward-drifting assets, while aggressively risk-penalized rewards can become too defensive during volatile periods. This paper p…

• Can Language Models Learn to Forecast Stock Prices

  Jiacheng Guo, Suozhi Huang, Shuzhen Li, Yunlong Gao, Zerui Cheng, Jason Ge, Shushu Liang, Zihao Li, Hao Lu, Ming Yin, Shilong Liu, Jiashuo Liu, Xu Kuang, Mengdi Wang · 1 de octubre de 2026

  Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning, software engineering, and computer use. However, whether the same approach can improve forecasting in financial markets is much less clear. Compared…

• NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance

  Hanwool Lee, Sara Yu, Yewon Hwang, Jonghyun Choi, Heejae Ahn, Sungbum Jung, Youngjae Yu · 1 de octubre de 2026

  Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18.8k source-linked triplets: paraphrases and Korean-English translations preserve meaning, while targeted financial rewri…

• DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting

  Wentao Zhao, Hongqiang Wu, Shanghang Liu, Zhaochen Zan, Yu Zhang, Biqing Huang · 1 de octubre de 2026

  Financial time-series forecasting must capture price dynamics across heterogeneous assets while incorporating news available at prediction time. We introduce DualCast, a dual-path framework that extends a frozen language model with a discrete financial vocabulary. Each log-return patch is represente…

• Reasoning Externalization for Faithful Large Language Model Narratives of Stock Return Predictions

  Sujung Kim, Seung Hwan Cho, Sangjin Park, Young-Min Kim · 1 de octubre de 2026

  In finance, interpreting machine learning predictions is essential, yet the numerical outputs of explainable AI can be difficult for non-experts to understand. While large language models (LLMs) can translate these outputs into natural language, they may produce errors when inferring numerical chang…

• AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning

  Yingbo Zhao, Zeyu Yang, Zhoufan Zhu · 29 de septiembre de 2026

  Formulaic alpha discovery is a core challenge in quantitative trading, as identifying alphas that work well together remains difficult. Recent reinforcement learning (RL) methods formulate this task as a Markov decision process (MDP), but two important issues remain unresolved. First, as the alpha p…

• Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis

  Kelvin J. L. Koa, Filip Orestav, Shengqiong Wu, Michael J. Wooldridge, Ke-Wei Huang · 29 de septiembre de 2026

  While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several limitations. Whereas the natural sciences provide objectively correct relationships, financial valuation constitutes a distinct class of symbolic disc…

• Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retraining

  Bicheng Wang, Xinyi Zhang · 29 de septiembre de 2026

  Consistently profitable trading is difficult because equity markets are noisy, non-stationary, and only partially predictable from historical data. We benchmark five deep reinforcement learning (DRL) actor-critic methods: A2C, PPO, DDPG, TD3, and SAC, that learn trading actions end-to-end from marke…

• Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets

  Xingtong Yu, Jiarun Zhou, Guanlin Ding, Wenkang Wei, Jiarui Liu, Chang Zhou, Fangzhou Ge, Chenyi Xu, Xikun Zhang, Renqiang Luo, Jie Zhang, Hong Cheng, Xinming Zhang, Hui Zhang, Yuan Fang · 29 de septiembre de 2026

  AI-based trading methods have rapidly evolved from machine learning and reinforcement learning to large language models (LLMs) and trading agents, yet their performance is still predominantly assessed through historical backtesting. Such evaluations provide limited evidence of whether a method can g…

• LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs

  Haochen Luo, Yifan Li, Binh Minh An, Xiaolong Luo, Zhengzhao Lai, Yuan Zhang, Chen Liu · 29 de septiembre de 2026

  Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamental…

• PALM: Point-in-Time Adaptation for Financial Language Models

  Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn · 28 de septiembre de 2026

  Language models used in financial backtests suffer from look-ahead bias, as a model trained on text published after the study period has already observed the outcomes it is asked to predict. To handle this issue, point-in-time (PIT) language models are pretrained on chronologically filtered corpora …

• UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting

  Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys, Hao Dong, Juho Kanniainen · 28 de septiembre de 2026

  Forecasting short-horizon mid-price movements from limit order book (LOB) data is central to algorithmic trading, yet most deep LOB forecasters are point predictors: they output a direction or a displacement, but never indicate which of their forecasts can be trusted. We introduce UQ-LOB, a lightwei…

• The Price of Thought: Does Test-Time Reasoning Pay in LLM Trading?

  Jiayi Chen, Guiling Wang · 28 de septiembre de 2026

  While inference-time reasoning in large language models (LLMs) promises better decision making, its higher computational cost may not yield better economic outcomes. Yet reasoning controls are rarely evaluated as economic interventions, where changes in model outputs must translate into better portf…

• AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining

  Qingzhuo Wang, Zikun Wei, Zhihua Wei, Wen Shen · 25 de septiembre de 2026

  Large language model (LLM)-based multi-agent systems can automate alpha factor mining, but their reliance on external APIs limits control over cost, availability, and confidentiality. Long research loops also tend to revisit a few successful economic mechanisms that lead to research path collapse. T…

• Agent Memory with Episodic Retrieval for Financial Decision-Making

  Nuoyue Xu, Jiang Liu, Wenxuan Huang, Xiang Zhang, Juntai Cao, Jiaqi Wei · 25 de septiembre de 2026

  Large language models (LLMs) have demonstrated strong capabilities in financial analysis and reasoning, inspiring recent advances in agent-based trading frameworks. While these systems show promise, prior approaches either emphasize long-horizon forecasting or operate as stateless analyzers, limitin…

• Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes

  Juli Huang, Jake Cheng, Rupert Lu · 24 de septiembre de 2026

  We treat options pricing as a representation problem: can machine learning detect systematic deviations from Black-Scholes using 2.6M real option contracts? We compare three regimes: learned abstract embeddings (Kernel PCA), preserved domain structure (tree-based ensembles), and neural network valid…

• HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting

  Nabeel Ahmad Saidd · 24 de septiembre de 2026

  Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations. We introduce HARN, a Hierarchical Associative Resonance Network for event-driven multi-timeframe for…

• Financial sentiment analysis using FinBERT with application in predicting stock movement

  Tingsong Jiang, Qingyun Zeng · 23 de septiembre de 2026

  In this study, we integrate sentiment analysis within a financial framework by leveraging FinBERT, a fine-tuned BERT model specialized for financial text, to construct an advanced deep learning model based on Long Short-Term Memory (LSTM) networks. Our objective is to forecast financial market trend…

• Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions

  Kemal Kirtac · 22 de septiembre de 2026

  Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrated deployment framework for evaluating whether those signals remain useful in financial decision systems. Computer science research has developed stron…

• ResNLS: An Improved Model for Stock Price Forecasting

  Yuanzhe Jia, Ali Anaissi, Basem Suleiman · 18 de septiembre de 2026

  Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper, we introduce a hybrid model that improves the prediction of stock prices by e…

• Evaluating Financial Sentiment in the Age of AI

  Arslan Bisharat, Oudom Hean · 18 de septiembre de 2026

  Financial sentiment measures are widely used in empirical finance, but it remains unclear whether general-purpose large language models (LLMs) improve on existing finance-specific methods. This paper evaluates twelve sentiment models, including dictionary-based methods, finance-specific transformers…

• SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes

  Mengxiao Wang, Nitesh Saxena · 18 de septiembre de 2026

  Autonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely domain-agnostic and overlook the distinctive, high-consequence attack surface such settings create. We examine this gap through financial trading agents, …

• PaGNet: A Panel-Aware GBDT--Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting

  Wonho Song, Hyungjoon Kim · 18 de septiembre de 2026

  Forecasting corporate tax avoidance proxies from firm--year panel data is challenging because predictive signals are distributed across short firm histories and related targets, while screening-oriented use requires transparent model behavior. We propose PaGNet (Panel-Aware GBDT--Neural Network), a …

• Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems

  Qi Rong Sua, Junhao Dong, Nguyen Duc Thai, Yuqing Wen, Cheston Tan, Yew-Soon Ong · 18 de septiembre de 2026

  Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to adversarial inputs is largely unknown. We study the vulnerability of LLM trading stacks to black-box, input-only attacks that enter solely via admissible social-…

• FINSKILLOPS: A Self-Evolving Multi-Agent System for SEC Filing QA

  Yanzhang Ma, Zhenghan Tai, Hanwei Wu, Sizhe Guan, Jianliang Lei, Hailin He, Chaolong Jiang, Jijun Chi, Tung Sum Thomas Kwok, Bohuai Xiao, Jingrui Tian, Xinlu Wu, Xingao Zhan, Peng Lu, Muzhi Li, Yihong Wu, Liheng Ma, Sicheng Lyu, Tianshuo Yan, Junhao Zhu, Yaqian Xu, Lei Ding, Yufei Cui, Ziquan Liu, Boyu Han, Hengli Liu, Ling Zhou, Xinyu Wang · 18 de septiembre de 2026

  Financial QA systems are typically improved before deployment through better retrieval, prompting, or agent coordination, leaving their reliability behavior fixed thereafter. In practice, new SEC-filing questions repeatedly expose heterogeneous errors in period, entity, evidence use, and calculation…