Who Let the Bots Out?: 2026 AI Portfolio Performance Showdown

Can AI effectively manage risk and deliver returns in real-world markets? To answer this question, we have deployed seven of the most advanced AI platforms.

As artificial intelligence transitions from a conversational tool to a strategic partner, its ability to navigate complex financial markets has become a key area of interest for investors and technologists alike.

Can AI effectively manage risk and deliver returns in real-world markets? To answer this question, we have deployed seven of the most advanced AI platforms such as ChatGPT, Gemini, Copilot, DeepSeek, Perplexity, Meta AI and Grok to act as portfolio managers.

Key takeaways

  • May 2026 delivered a split result across AI portfolios, with no single model consistently outperforming across all risk profiles.
  • DeepSeek led the conservative portfolio with a gain of 9.75%, ChatGPT topped the aggressive category at 7.91%, and Gemini was the best performer in neutral at 4.03%, while DeepSeek edged ahead in moderate at 1.84%.
  • Grok recorded the steepest loss of the month, falling 15.42% in the moderate portfolio, and Meta AI was the most consistently weak performer, declining across all four risk profiles.
  • Benchmark indices outpaced most AI portfolios in May, with the S&P 500 gaining 9.03% in the neutral category and 5.27% in moderate, and the Vanguard MSCI Index International Shares ETF rising 4.70% in aggressive.

What is the 2026 AI Portfolio Performance Showdown?

The 2026 AI Portfolio Performance Showdown is a year-long experiment running from January 2026 to December 2026. It is designed to test how different artificial intelligence models make real-world investment decisions over a full market cycle.

The project aims to assess how AI interprets market conditions, balances risk and return and adapts its strategy as economic and market data evolves throughout the year. By tracking decisions over 12 months rather than relying on short-term predictions or hypothetical scenarios, the project provides a more realistic view of AI-driven investment behaviour.

All portfolios are constructed and managed under consistent rules, allowing for direct comparison between models and risk profiles within an Australian market context. The results displayed are updated monthly, with rankings based on total return adjusted for the specific risk parameters of each portfolio profile.

How are the portfolios built and tested over time?

At the start of the project in January 2026, each AI model is asked to construct four separate 10-stock portfolios and assign a percentage weighting to each stock. Each portfolio reflects a different risk appetite commonly seen among Australian investors and is designed to operate within the realities of the local share market.

The four risk profiles are:

  • Conservative, focused on capital preservation and reliable income

  • Moderate, balancing long-term growth with steadier returns

  • Aggressive, targeting higher growth through more volatile sectors and smaller companies

  • Neutral, designed to broadly track overall market performance

Once the initial portfolios are established, the project moves into a monthly review cycle that runs through to December 2026. At each review point, the AI models must decide whether to keep existing stocks unchanged, adjust position sizes to increase or reduce exposure, or replace stocks if they no longer align with the portfolio's objectives. All decisions must remain consistent with the portfolio's original risk profile.

This combined process tests both the quality of each AI model's initial stock selection and its ability to adapt strategy over the course of the year.

How are the results measured and reported?

Portfolio performance is tracked from January 2026 through December 2026 using total return, which includes both price movements and income such as dividends. Results are updated monthly to reflect portfolio changes and market performance over the same period.

Portfolios are ranked within their respective risk categories rather than against all portfolios combined.

At the conclusion of the project in December 2026, the full year of results will provide a complete view of how each AI model performed across different market conditions.

Important: This test covers performance over a relatively short timeframe. Results for individual funds, portfolios or indices during this period may not reflect how they perform over the long term. Past performance is not a reliable indicator of future returns.

Monthly Leaderboard: April

Which AI model is navigating the market most effectively? This leaderboard ranks AI models based on the performance of their portfolios over the month. Results are calculated using the percentage return generated by each portfolio, providing a clear comparison of how each model's investment strategy has performed.

Conservative

May 2026 delivered a broadly positive result for conservative AI portfolios, with most models posting gains as the period drew to a close.

DeepSeek was the standout performer, rising 9.75% to lead all conservative portfolios. ChatGPT also performed strongly, returning 5.81%, followed by Copilot at 4.36% and Perplexity at 3.83% and Gemini at 3.80%. On the downside, Grok recorded the steepest decline at -4.31%, followed by Meta AI at -3.71%.

The benchmark portfolios delivered mixed results, with the iShares Core U.S. Aggregate Bond ETF returning 0.48%, while the S&P 500 Low Volatility Index fell -1.93%.

Moderate

May 2026 was a challenging month for moderate AI portfolios, with most models finishing in negative territory and significantly underperforming their benchmarks.

Grok was the weakest performer by a wide margin, falling 15.42% to sit well behind the rest of the field. Meta AI also struggled, declining 5.48%, followed by ChatGPT at -4.29%. Perplexity edged down 0.58% and Copilot slipped 0.46%, while Gemini was able to eke out a small gain at 0.14%. DeepSeek was the clear standout, returning 1.84% to lead all moderate portfolios for the period.

The benchmark portfolios told a starkly different story, with the S&P 500 rising 5.27%, a result that most AI models failed to come close to. The S&P/ASX 200 was more modest, gaining 0.40%.

Aggressive

May 2026 produced a mixed result for aggressive AI portfolios, with performance spread widely across the field.

ChatGPT was the standout performer, rising 7.91% to lead all aggressive portfolios for the period. Copilot also delivered a solid result, gaining 4.37%. On the downside, Meta AI recorded the steepest decline at -7.91%, followed by Grok at -6.17% and DeepSeek at -4.14%. Gemini slipped 2.53% and Perplexity fell 1.77%.

The benchmarks both posted healthy gains, with the Vanguard MSCI Index International Shares ETF rising 4.70% and the Russell 2000 returning 4.41%, results that only ChatGPT and Copilot managed to match or exceed among the AI portfolios.

Neutral

May 2026 was a difficult period for neutral AI portfolios, with the majority of models failing to keep pace with a strongly performing S&P 500.

Gemini led the field, returning 4.03%, followed by Copilot at 3.08% and Grok at 2.77%. ChatGPT and Meta AI were broadly flat, slipping just 0.28% and 0.32% respectively. DeepSeek and Perplexity were the weakest performers, falling 6.21% and 5.52%.

The S&P 500 rose 5.27%, outpacing every AI portfolio in the neutral category. The S&P/ASX 200 gained 0.40% for the period.

 

What stocks did each AI model choose?

The AI models were asked to build a 10-stock portfolio in January 2026. The portfolios are reviewed monthly, giving each model the opportunity to add or remove stocks and adjust portfolio weightings based on its assessment of current market conditions.

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Written by

Insights Analyst

William Capada is an insights analyst at Finder. With years of experience as an analyst, he has honed his skills in analysing complex datasets and extracting actionable insights. Proficient in various analytical tools, he has a proven track record of delivering meaningful insights that drive strategic decision-making. William conducts research related to economic data and is also responsible for updating the insights statistics pages. He also assists in ensuring that the scoring makes sense for the Finder Retail Awards. See full bio

William's expertise
William has written 5 Finder guides across topics including:
  • Data Analysis
  • Data Visualization
  • Retail Awards

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