Generative AI Can’t Generate Alpha… But Machine Learning Can | Pictet’s David Wright

Why It Matters

The discussion underscores that generative AI will not replace proven machine‑learning models for alpha generation, but can boost operational efficiency, reshaping cost dynamics for quantitative firms.

Key Takeaways

• Generative AI unsuitable for quantitative return forecasts due to hallucinations.
• Pictet relies on decision‑tree gradient boosting for interpretable alpha generation.
• Generative AI is used for internal support tasks, not core investment models.
• Efficient model training has reduced cloud spend despite increasing model complexity.
• Pictet’s AI‑enhanced ETFs aim for 1‑2% outperformance over benchmarks.

Summary

In a sponsored fireside chat, David Wright, co‑head of Pictet Asset Management’s quantitative franchise, explained why generative AI, despite its hype, is ill‑suited for the firm’s core alpha‑generation process. The discussion centered on the distinction between broad‑stroke generative models that create text, images or code and the disciplined machine‑learning techniques required to forecast equity returns.

Wright emphasized that generative AI’s propensity to hallucinate makes it unreliable for return forecasts. Instead, Pictet employs thousands of decision‑tree models using gradient boosting on structured, tabular data. This approach delivers interpretable signals from hundreds of features—price trends, accounting metrics, sell‑side forecasts and other fundamentals—to predict 20‑day relative returns and target a modest 1‑2% outperformance over benchmarks.

He noted that generative AI does have a role in supporting functions: drafting client decks, summarizing meetings and assisting developers with code snippets. The firm’s two AI‑enhanced ETFs, PQNT and PQUS, embody this hybrid strategy, leveraging robust ML for investment decisions while using generative tools for operational efficiency.

The takeaway for investors is clear: traditional, tree‑based machine learning remains the workhorse for quantitative alpha, while generative AI offers productivity gains without reshaping the core investment engine. Moreover, Pictet’s improved training pipelines have lowered cloud expenditures, suggesting that efficiency, not raw compute power, will drive future cost structures in quant finance.

Original Description

Learn More about PQNT (Pictet AI Enhanced International Equity ETF) here:

https://etf.am.pictet.com/pqnt/?utm_campaign=usetf&cid=2883088750&utm_source=jfmv&utm_content=pqntlandingpage&utm_medium=social_02&utm_term=youtube

Learn more about PQUS (Pictet AI Enhanced US Equity ETF) here:

https://etf.am.pictet.com/pqus/?utm_campaign=usetf&cid=2882903230&utm_source=jfmv&utm_content=pquslandingpage&utm_medium=social_02&utm_term=youtube

Jack Farley sits down with David Wright, co-head of Quantitative Investments at Pictet Asset Management, to discuss the machine learning techniques his team uses in their $30 billion quant franchise, and the degree to which AI has impacted serious quantitative investing. Wright explains why he prefers to utilize many decision trees and use gradient boosting rather than Generative AI to generate return forecasts, citing the need to avoid "hallucinations" and ensure models remain interpretable. The conversation explores their sophisticated investment process, which analyzes over 400 features, including accounting data, market trends, and analyst sentiment, to predict relative stock performance over 20-day horizons. These strategies, which now are included in new ETFs $PQNT, Pictet AI Enhanced International Equity ETF, and $PQUS, Pictet AI Enhanced US Equity ETF, are designed as "passive replacements," aiming to maintain a Beta of 1.0 while aiming to deliver an additional 1–2% annual outperformance over the relevant benchmarks, S&P 500 and MSCI EAFE indices. Finally, Wright addresses the common "black box" misconception of quantitative finance, advocating instead for a "crystal box" approach that provides full transparency into the economic rationale behind every trade. Recorded April 21, 2026.

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Important Information

Before investing, carefully consider the fund’s investment objectives, risks, charges, and expenses. This and other information can be found in the fund’s prospectus or, if available, the summary prospectus, which may be obtained by calling 855 994-4778 or visiting www.pictet.com/etf. Read it carefully before investing.

Investing in Exchange Traded Funds ETFs involves risk, including possible loss of principal. The fund's principal investment risks include Artificial Intelligence Models and Data Risk, Non-Diversification Risk, Convertible Securities Risk, Rights and Warrants Risk, Real Estate Investment Trusts REITs Risk and Sustainability & ESG Data Risk. For additional information about these and other fund risks, please refer to the "Principal Investment Risks" section of the prospectus.

ETFs are subject to additional risks that do not apply to conventional mutual funds, including the risks that the market price of an ETF's shares may trade at a premium or discount to its net asset value, an active secondary trading market may not develop or be maintained, or trading may be halted by the exchange in which they trade, which may impact an ETF's ability to sell its shares. Shares of any ETF are bought and sold at market price, not NAV, and are not individually redeemed from the ETF. Brokerage commissions will reduce returns.

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Definitions of terms used in the interview:

1. S&P 500 Index

The Standard & Poor’s 500 Index (S&P 500) is a market-capitalization-weighted index of 500 leading publicly traded companies in the U.S. It is widely regarded as the best single gauge of large-cap U.S. equities.

2. MSCI EAFE Index

The MSCI EAFE Index is a stock market index that tracks the performance of large- and mid-cap securities across developed markets around the world, excluding the U.S. and Canada. The acronym stands for Europe, Australasia, and the Far East.

3. Alpha

Alpha represents the "excess return" of an investment relative to the return of a benchmark index. It is a measure of performance on a risk-adjusted basis. "Positive Alpha: indicates the investment outperformed its benchmark after accounting for risk and "Negative Alpha" indicates the investment underperformed relative to the benchmark.

For descriptions such as Beta and Basis points, see podcast description.

Timestamps

00:00 Intro

01:20 Quantitative Tools In Quantitative Investing

6:45 GenAI Hallucinations Make It Unreliable For Serious Quant Investing

15:20 Forecasting 20-Day Returns Using Decision Trees & Gradient Boosting

25:37 Analyst Upgrade/Downgrade Signal Example

30:21 PQNT & PQUS

36:50 Greatest Misconception About Quant Investing

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