---
title: "Are AI Fund Managers Superior? JPMorgan Chase Backtesting: Outperforms Classic Portfolio with Higher Annualized Returns and Lower Volatility"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/292340591.md"
description: "JPMorgan Chase recently completed a backtesting experiment on AI investment agents. The optimal system achieved an annualized return 0.7 percentage points higher than the 60/40 portfolio over a twenty-year simulation, with lower volatility. However, JPMorgan Chase emphasized that the results stem from historical simulations and have not been verified in live trading. Meanwhile, academia remains vigilant about the systemic risks potentially triggered by convergent institutional AI strategies"
datetime: "2026-07-10T15:56:34.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/292340591.md)
  - [en](https://longbridge.com/en/news/292340591.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/292340591.md)
---

# Are AI Fund Managers Superior? JPMorgan Chase Backtesting: Outperforms Classic Portfolio with Higher Annualized Returns and Lower Volatility

AI is advancing into the core domain of investment decision-making on Wall Street. A team of strategists at JPMorgan Chase led by Thomas Salopek recently completed a backtesting experiment on AI investment agents, **applying AI systems to market regime identification for the first time.** The team constructed multiple AI agents capable of dynamically adjusting equity and bond allocations based on market conditions, aiming to explore the feasibility of autonomous investment decisions.

The backtesting results showed that **the best-performing system achieved an annualized return 0.7 percentage points higher than the traditional 60/40 equity-bond portfolio in a simulated environment over the past twenty years, with lower volatility,** while also outperforming JPMorgan Chase’s existing rule-based market regime models.

Although Wall Street is accelerating the deployment of AI in analysis, programming, and investment tools, this experiment marks an extension of AI applications into the core decision-making processes of capital allocation. However, JPMorgan Chase explicitly warned that **these results should not be interpreted as evidence that AI possesses the ability to consistently outperform the market, as related explorations are still in their early stages.**

## Promising Historical Simulations, Unverified in Live Trading

The AI investment agents developed by JPMorgan Chase researchers **feature the core function of dynamically adjusting equity and bond allocation ratios in response to changes in market conditions.** In historical simulations covering the past twenty years, the optimal system achieved an annualized excess return of 0.7 percentage points, realized lower volatility, and surpassed the bank’s existing rule-based market regime models.

The strategist team pointed out in the report that the AI agent was designed to make decisions under conditions of uncertainty, achieving superior performance compared to reasonable benchmarks. This also marks the first time JPMorgan Chase has publicly disclosed its research findings in the field of AI-driven capital allocation, signifying a key step forward in the bank’s exploration of intelligent investment decision systems.

Despite the positive backtesting data, JPMorgan Chase remained cautious in its interpretation of the conclusions. The bank explicitly emphasized that **the aforementioned results are entirely derived from historical simulation environments and have not been validated through real-market trading; therefore, one should not infer that AI has an inherent ability to consistently outperform the market.**

In the report, the strategist team also warned market participants against uncritically accepting overconfident AI judgments derived from in-sample backtesting results. They argued that agent-based AI systems must be built upon rigorous and prudent asset allocation processes, rather than simply assuming that the agents themselves constitute a source of expertise.

## Rising Risks of AI Consensus: Automated Trading Enters the "Deep Waters" of Decision-Making

As enthusiasm for AI investment tools continues to heat up on Wall Street, academic vigilance regarding their potential systemic risks is rising in tandem. According to Bloomberg, an increasing number of studies are focusing on a core proposition: how market dynamics will change when a large number of institutions deploy similar AI models for investment decisions.

Researchers pointed out that **while AI technology can significantly enhance information acquisition efficiency and decision accuracy, it may also foster hidden dangers such as convergent position structures and increased market manipulability.** Especially in stress scenarios, if a large number of institutions simultaneously reach similar conclusions, it could further amplify market volatility. The JPMorgan Chase strategy team also acknowledged the existence of these risks in a recent report.

This recent test by JPMorgan Chase reflects the evolutionary trajectory of AI applications on Wall Street. Over the past two years, major banks have widely embedded large language models into auxiliary scenarios such as research report generation, code writing, and internal investment tools. The current tests indicate that the industry is evaluating whether AI systems possess the capability to advance from assisting employee decisions to undertaking more decisive core functions, such as cross-market capital allocation.

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