人工智能在股票与加密货币市场中的应用：进展、盈利能力证据及自动化投资的局限性
Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing
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中文总结 AI 辅助
本文综述AI在股票与加密货币市场的应用进展，发现AI在预测等上游环节有进展但缺乏持久净绩效，无通用AI架构能持续提供净阿尔法，需满足特定条件提升证据与实施但不保证盈利。
中文摘要 AI 辅助
人工智能（AI）目前支持从数据、预测到研究、投资组合、执行及工具使用的投资工作流程，但技术能力并不等同于投资盈利能力。本文是一篇重要的最新进展综述，研究了截至2026年8月31日的公开研究，涵盖上市股票、交易所交易基金、中心化加密货币现货、永续期货及链上市场。我们采用阿尔法转化链来组织证据：时点信息必须产生稳定信号、可行头寸、可执行订单，且扣除成本后经风险调整的回报达标。在机器学习、时间序列基础模型、金融语言模型、强化学习及智能体等领域，所考察的记录显示在预测、文本处理、投资组合设计及工作流程整合方面取得了切实但主要是上游的进展，而持久净绩效的证据则较为薄弱。时间污染、重复选择、生存偏差、薄弱基准、实施成本、交易场所机制及容量问题可能破坏向净阿尔法的转化。强劲的历史结果与预测因子衰减、修正的前瞻偏差、混杂的前瞻性证据及少数经审计的实盘资金记录并存。加密货币带来了有价值的状态信息，但需要分别处理现货、永续期货及去中心化现金流与执行。在本文考察的公开证据中，未发现任何通用AI架构能持续提供跨 regimes、考虑容量的净阿尔法。更可信的主张需要时点数据与模型、与决策对齐的目标、投资组合-执行联合评估、受控适应、前瞻性测试及与权限匹配的治理。这些条件可改善证据与实施，但不能保证盈利。
英文摘要
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.
