Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 13328-13333· 0 citations· 50 references
Abstract
Financial markets are one of the few machine-learning settings where feedback is fast, mistakes are expensive, and deployment is constrained by execution and regulation. Yet most existing tutorials still treat finance either as an NLP benchmark or as a classical forecasting problem. This tutorial takes a different view. We show how recent progress in generative AI becomes genuinely useful in markets when it helps model participant behavior, ground reasoning in live documents and order-flow data, and support research and execution workflows that can survive contact with production. The material therefore moves in three steps: behavioral and market foundations; grounded LLM systems, including RAG, critique models, and agentic workflows; and responsible deployment, including corrected backtesting, execution, risk controls, and governance. Two live demonstrations and three optional Colab notebooks connect the lecture material to implementation.
This work investigates LLM-based forecasting agents, meaning systems in which a language model contributes to a scored prediction about a future or currently unobserved target, and organizes architectures into three groups.
Xiao-Gang Xu, Jiaqi Tang, Jianmin Chen et al.· 0 citations
DE-Venus is presented, a unified framework for data-efficient RLVR that treats supervision as evolving state across data preparation and policy optimization and reduces annotation and training costs without sacrificing scalable RL execution.
Shen-Zhi Yang, Guang-Cheng Zhu, Kai Tang et al.· 0 citations
It is concluded that while LLMs hold genuine promise within AI trading systems, robust deployment requires careful task decomposition, rigorous backtesting protocols, and domain-aware fine-tuning strategies.
PACE (Plan-Ahead Controlled Execution), a hierarchical framework that decomposes parent-order execution into long-horizon planning and short-horizon execution, requiring neither explicit market assumptions nor task-specific training is proposed, suggesting that LLMs can complement human traders in execution decisions.
A framework for causal prediction that enables managers to evaluate and deploy novel marketing content generated by AI, and establishes a threshold based on how closely new content resembles past campaigns, separating cases where causal prediction is reliable from cases where direct experimentation is warranted.
Paul B. Ellickson, Wreetabrata Kar, James C. Reeder et al.· Journal of Marketing Researc...· 1 citation
The analysis first benchmarks naive and behavioral models against interpretable machine learning and deep learning models, then evaluates the modified EWA specifications of prior work against these benchmarks and uses the LASSO diagnostics to motivate a further nested frequency tracking extension.
N. Dao· 0 citations
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