How to Create Consistent Edge...with AI

Explaining the magic behind Intelligent Alpha's edge

The Deload explores my curiosities and experiments across AI, finance, and philosophy. If you haven’t subscribed, join nearly 2,000 readers:

Disclaimer. The Deload is a collection of my personal thoughts and ideas. My views here do not constitute investment advice. Content on the site is for educational purposes. The site does not represent the views of Deepwater Asset Management. I may reference companies in which Deepwater has an investment. See Deepwater’s full disclosures here.

Additionally, any Intelligent Alpha strategies referred to in writings on The Deload represent strategies tracked as indexes or private test portfolios that are not investable in either case. References to these strategies is for educational purposes as I explore how AI acts as an investor.

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A Timeless Question About Beating Markets

Good essays start with good questions. A good question allows the writer to explore something important and tell readers something surprising. Ideally the essay invites new questions worth further exploration (h/t Paul Graham’s latest greatest essay).

After explaining the potential for an AI-powered BlackRock two weeks ago on The Deload, a reader posed one of those questions worth further exploration: Is it possible to develop an AI-powered system that consistently outsmarts the market?

Consistently outsmarting the market is the point of Intelligent Alpha, and nearly nine months of data says AI can do it. More than 70% of my 40+ strategies are ahead of benchmarks, and the average strategy is ahead by almost 400 bps.

Time is the ultimate arbiter of consistency, so the only honest answer to whether AI will consistently outsmart markets is, “We’ll see.”

Yet, the question about AI’s potential for consistent outperformance sticks with me because it encompasses the biggest question I get about using AI to invest: Explainability.

Two endowments asked if I can explain how the AI works so they can feel confident in staying with an AI-powered strategy when it underperforms, just as they would scrutinize a human manager in the same way. Others have asked whether AI-powered strategies could get so big that it would eliminate the alpha. A few people have wondered how I can be confident the AI won’t blow up in a period of market stress.

All these questions can be answered by explaining how Intelligent Alpha works.

So I’ll reframe the question from whether AI will consistently outsmart markets to why AI has been outperforming markets. That’s explainability, and it also answers why I think AI will keep winning.

Breaking Down the Benchmark

When investors say they want to “beat the market,” they mean beat a benchmark of a particular segment of the market. A benchmark represents a structured opportunity cost for trying to generate excess return. If we can’t beat a benchmark regularly, we should just accept the market return.

Useful benchmarks play the game of investing in a knowable and consistent way. That’s what makes a benchmark a benchmark — consistent explainability, back to the subject of this post. If we understand the consistent process by which benchmarks are constructed, we can understand the levers for generating excess return.

How is a market benchmark constructed?

Let’s use the most popular benchmark, the S&P 500. The S&P 500 is created by three elements: rules, schedules, and opinions.

Rules define the universe of stocks relevant to the benchmark — how many stocks the benchmark will own, how the portfolio is weighted, and what factors or qualities the stocks in the benchmark should represent. The S&P 500 is a collection of 500 large cap US equities weighted by market cap.

Schedule defines how often the benchmark is reviewed. The S&P 500 is reviewed and rebalanced quarterly. Typically, the S&P committee replaces ~20 stocks in the index, 4% of the total, per year.

Opinion is the ultimate arbiter of what goes into benchmarks that use a committee to govern composition. The S&P 500 depends on a team of humans to select stocks for the portfolio given the rules and schedule. So too do the Dow Jones, S&P 400, and S&P 600. Other major benchmarks, like the Russell 2000 or Nasdaq 100, use only rules and schedules without a component of human opinion.

Per the S&P: “The committee focuses on an eligible company’s reputation, its history of sustained growth, its interest to investors, and its sector representation of the broader market.”

If the S&P 500 is our benchmark, now we know the game our competition plays. The seeming advantage to active managers is that they don’t have to play the same game. Active managers can play by a different rule set in terms of portfolio holdings or cap focus. They can review the portfolio on any schedule that they wish. They can opine on matters far beyond a company’s interest to investors and sector representation.

Is such infinite freedom a blessing or a curse for the active manager?

SPIVA data I’ve shared before shows that the majority of managers underperform cap benchmarks over any meaningful time frame. Freedom appears a curse to managers.

SPIVA report

In his preface to the Intelligent Investor, Warren Buffett wrote:

“To invest successfully over a lifetime does not require a stratospheric IQ, unusual business insights, or inside information. What’s needed is a sound intellectual framework for making decisions and the ability to keep emotions from corroding that framework.”

I believe most investment managers fail to outperform benchmarks over the long run because they don’t have the emotional temperament necessary to control corrosive urges. Infinite freedom invites more decision making, and more decision making allows for more emotional influence. Active managers might be better to apply rules and schedules to limit decision making, but that would be antithetical to the belief that human opinion is the ultimate path to alpha.

AI suffers from no emotion, nor does it chafe at rules and schedules to constrain its decision making, and that’s where AI’s advantage begins. The Intelligent Alpha process starts with rules and schedules to enhance the odds of AI’s smarts at stock picking to shine through.

AI + Structure for the Win

The first step in creating an Intelligent Alpha strategy is to decide what game we want to play. Maybe we want to try to beat the S&P 500, or maybe we want some aggressive take on the QQQ. By defining what game we want to play, we define our benchmark, and we therefore know what rules, schedules, and opinions we’re up against.

Given our benchmark, we can establish a set of rules for Intelligent Alpha. There are now over 45 Intelligent Alpha strategies built around the same general set of rules:

• Target exposure: market cap, sector, factor, theme, etc.

• Target number of holdings: Select (generally 100+ stocks), Conviction (30 stocks), Custom (other defined number)

• Weighting: Select (decided by AI committee), Conviction (equal weighted)

The Intelligent Equal Select strategy is one of my favorite examples of rules enhancing a strategy.

Equal Select is a large cap focused “equal” weight strategy that uses my AI investment committee (GPT, Gemini, Claude) to weight the stocks in the portfolio in a flatter way than the S&P 500 but in a more dynamic way than the S&P 500 Equal Weight index. Each AI committee member selects up to 100 stocks to include in the Equal Select strategy. Every vote carries a base weight equal to one divided by the total number of stocks nominated by the AI's including duplicates. If a stock gets selected by all three committee members, it gets 3x the base weight.

The rules of the Equal Select strategy plus the AI investment committee’s creative stock picking have been impressive. The Equal Select is ahead of the RSP (S&P 500 Equal Weight) by 750 bps since inception. Even more impressive, the Equal Select is ahead of the S&P 500 itself by 230 bps while the RSP trails the S&P by 520 bps.

Once we have rules, then I define the Intelligent Alpha strategy’s schedule. I use simple bi-annual, quarterly, or monthly portfolio reviews. The intent of Intelligent Alpha isn’t to unleash AI to make an infinite number of on-going decisions. We want AI to use its unemotional intelligence to pick a set of companies within the given rules that represent good medium-to-long-term investments.

That brings us to opinion, or in this case, intelligence.

Intelligent Alpha strategies are driven by the opinion of my AI investment committee. The committee acts with a united philosophy inspired by many of history’s greatest investors which is responsible for alpha inception to date.

One of the strategies I’m proudest of, although it doesn’t show the highest overall alpha, is the Intelligent Select. It’s a broad based core portfolio intended to compete directly with the S&P 500. It holds 181 stocks. It’s currently underweight the Mag 6 stocks (MSFT, AAPL, META, NVDA, AMZN, and GOOGL) by 1340 bps. And it’s beaten the S&P by 300 bps since its July 2023 inception. I think that’s incredible performance given how much of the S&P’s total returns have been driven by the Mag 6 over the past year plus.

Results suggest AI is a good stock picker. If AI were picking bad stocks, rules and schedules wouldn’t save it. Rules and schedules let AI’s stock picking power drive results in scalable and repeatable ways that are explainable…for the most part.

Magic

Arthur C. Clark’s third law states: Any sufficiently advanced technology is indistinguishable from magic.

Investing with AI feels like magic to me, and some of it is. While I can explain the rules and the schedule and the philosophy I program into the AI, I can’t explain to you exactly how or why the AI picks the stocks it picks. Not even the creators of language models can explain exactly how the systems work.

AI-powered investing is a bit of magic, and like all forms of magic, you may need to suspend a bit of your desire to explain the trick to enjoy the show.

Ultimately the performance of Intelligent Alpha’s portfolios will be the most convincing evidence, and we’re back to the original question of AI consistently outsmarting markets. Ironically, the search for an investment strategy that can consistently beat markets is timeless because we’re always seeking the answer, yet the question can only be answered with the continual passing of time. Perhaps that means we never get a satisfying answer.

All I know for certain is that AI’s advantage of ever-improving and unemotional intelligence paired with rules and schedules give it the best odds to figure it out our timeless question of consistent outperformance.

The future of investing is intelligent.

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Disclaimer: My views here do not constitute investment advice. They are for educational purposes only. My firm, Deepwater Asset Management, may hold positions in securities I write about. See our full disclaimer.

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