跳至正文
返回文库全部文档

AI在股票与加密市场中的投资：证据与局限

文章 arXiv papers · 作者: Linsen Zhu et al.
阅读原文

总结

本综述评估AI在股票、ETF、中心化加密市场、永续期货和链上活动中的应用。它将实现投资回报的过程描述为一系列环节：时点数据必须支持可靠信号，形成可行的头寸和可执行订单，并在计入成本后取得正的风险调整表现。综述涵盖机器学习、时间序列模型和语言模型、强化学习以及自动化智能体。报告显示，相关进展在预测、文本分析、投资组合设计和研究工作流程方面最为显著。

综述发现，关于持续净盈利能力的公开证据较少。数据泄漏、反复筛选策略、幸存者偏差、不当比较、交易成本、交易场所限制和容量有限，都可能削弱历史结果。综述还指出预测因子衰减、前视偏差错误得到纠正、前瞻性结果不一，以及经审计的实盘资金证据较少。加密市场需要结合市场特性进行分析，因为现货、永续合约和去中心化市场的现金流与执行条件各不相同。综述未发现任何已获证实能在不同市场状态和容量水平下持续产生净阿尔法的AI方法。更好的评估需要使用时点数据、采用与交易决策相关的目标、结合投资组合与执行测试、实施受控适应、开展前瞻性评估并进行适当治理；这些做法能增强证据，但不能确保回报。

核心观点

  • AI在预测和工作流程支持方面的进展，大于已证明的持续盈利能力方面的进展。
  • 可用的投资信号必须经过从时点信息到可执行且计入成本的回报的每个环节检验。
  • 数据污染、选择效应、基准薄弱、执行摩擦和容量限制都可能削弱历史结果。
  • 解读股票和加密市场的证据时，应考虑两者不同的市场结构和现金流。
  • 前瞻性测试以及对投资组合、执行和治理的联合评估，能强化相关论断，但不能保证盈利。

标签

机器学习回测交易执行风险管理加密资产股票
全文
# 2609.04917


# Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing









Artificial intelligence (AI) now supports investment workflows from data and prediction through research, portfolios, execution, and tool use. Technical capability, however, is not evidence of investment profitability. This critical state-of-the-art review examines public research available through 31 August 2026 on listed equities, exchange-traded funds, centralized crypto spot, perpetual futures, and on-chain markets. We organize evidence with an alpha-translation chain: point-in-time information must yield a stable signal, feasible positions, executable orders, and risk-adjusted returns after costs. Across machine learning, time-series foundation models, financial language models, reinforcement learning, and agents, the examined record shows real but mainly upstream progress in prediction, text processing, portfolio design, and workflow integration. Evidence is thinner for durable net performance. Temporal contamination, repeated selection, survivorship, weak benchmarks, implementation costs, venue mechanics, and capacity can break translation to net alpha. Strong historical results coexist with predictor decay, corrected look-ahead failures, mixed prospective evidence, and few audited live-capital records. Crypto adds informative state but requires separate treatment of spot, perpetual, and decentralized cash flows and execution. Within the public evidence examined here, no general AI architecture is shown to deliver persistent, cross-regime, capacity-aware net alpha. More credible claims require point-in-time data and models, decision-aligned objectives, joint portfolio--execution evaluation, controlled adaptation, prospective tests, and authority-matched governance. These conditions can improve evidence and implementation; they do not guarantee profit.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。