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Wall Street’s AI Weakness Is Your Biggest Strength
Wall Street’s AI Weakness Is Your Biggest Strength
BY MICHAEL SALVATORE, EDITOR, TRADESMITH DAILY
In This Digest:
- Why financial research firms got smacked down hard yesterday
- Predictive Alpha’s newest top 10 ideas
- Your last chance to trade the Signals Master Portfolio
The AI disruption theme is back…
And this time, it’s coming for Wall Street’s research and ratings firms.
If you’ve been following along in these pages for a while, you’ll remember what happened to software stocks earlier this year.
Software-as-a-service (“SaaS”) companies like Salesforce (CRM), Adobe (ADBE), and ServiceNow (NOW) saw their share prices fall sharply as investors began to worry that AI tools could replace much of what their products do. Why pay thousands of dollars per seat for enterprise software when an AI agent can do the same job for pennies?
Now that same fear has spread to the next category — the Wall Street firms that sell financial data, research, and analysis.
Shares of financial data provider FactSet (FDS) fell as much as 8.1% yesterday. Investment research firm Morningstar (MORN) and S&P Global (SPGI) — the company behind the S&P 500 index — each took a 3% hit. Credit rating firm Moody’s (MCO) dropped 2% before recovering.
All on a day when the broader market finished higher.
The trigger, once again, was Anthropic…
This time, the private AI firm behind the popular Claude model unveiled a set of new AI tools built specifically for the finance industry.
The pitch is simple: The expensive research and data work these firms charge a premium for can now be done by software at a fraction of the cost.
Take Moody’s and the other credit rating agencies.
Their job is to read through a company’s financial filings, judge how likely it is to pay back its debts, and assign a letter grade — AAA for the safest, all the way down to D for the riskiest.
It’s slow, expensive work done by armies of human analysts. And they don’t always get it right. In the lead-up to the 2008 financial crisis, the major rating agencies infamously gave their top AAA grade to toxic mortgage bonds that later collapsed and helped trigger the meltdown.
An AI agent can do much of the same work in seconds. It can read every filing a company has ever made, run thousands of “what if” scenarios, and update its assessment continuously as new information comes in.
Credit rating agencies still have a moat — the U.S. government legally requires the use of approved firms like Moody’s, S&P, and Fitch for many bond issues. But that doesn’t make them immune. If AI tools start producing better, faster, and cheaper analysis, the case for paying premium prices to the incumbents gets harder to make.
So investors aren’t wrong to be worried about the future of these companies.
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Here at TradeSmith, we’re familiar with the power of AI…
We launched our first AI trading tool, Predictive Alpha, in 2023. And we’ve been building on that foundation ever since to help folks like you level the playing field with Wall Street.
It’s no secret that the world’s most successful hedge funds – Renaissance Technologies, Citadel, and Two Sigma – rely heavily on algorithms and machine learning for their edge. And they haul hundreds of billions of dollars out of the market every year as a result.
Predictive Alpha, our AI-powered forecasting tool, has been projecting where stocks are likely to trade three weeks out since 2023. Our new Signals tool uses AI to scan the market every day to flag high-odds trading setups the big firms used to keep to themselves. And the AI Super Portfolio uses machine learning to manage risk in real time.
Each one applies cutting-edge AI to decades of market data – to give you a measurable edge on every trade you make.
Today, I want to show you what that looks like in practice with two of our most popular trading tools…
Predictive Alpha is ignoring the Mag 7 and focusing on these…
Every morning before the opening bell, our AI-powered stock forecasting model – Predictive Alpha – runs projections on thousands of individual stocks.
Claude and ChatGPT predict the next word in a sentence based on billions of examples. Predictive Alpha predicts the next price move based on more than 100 billion stock market data points.
For each stock, it produces a projected target price, an expected percentage move, a target date, and historical accuracy ratings – how often this model has been right on past forecasts.
Right now, I want to show you the 10 strongest forecasts across the individual stocks we track.
A few things stand out.
There’s not a single Mag 7 stock in this list. No Nvidia. No Apple. No Meta. The model isn’t chasing the most-covered, most-discussed companies in financial media. It’s scanning everything and surfacing where the math says the best risk/reward sits.
And right now, that’s places like online dating services company Match Group (MTCH) where Predictive Alpha is forecasting a 4.6% move to $39.91 by May 21 with an 89.8% target accuracy rate.
It’s also seeing action in Harley-Davidson (HOG), with a projection for the stock to reach $23.81 by June 1 – a +2.6% move, with an 88.3% target directional accuracy rate.
Other ranking names include more under-the-radar technology stocks like American Superconductor (AMSC), a power electronics company whose work spans the defense, renewable energy, and industrial sectors. Predictive Alpha is forecasting a +9.7% move to $55.18 by June 3, with an 88.3% historical target accuracy rate.
The model doesn’t care what the Anthropic press release says. It doesn’t read earnings transcripts or follow the news cycle. It analyzes price action to determine the next most likely move and reinforces the model, whether that move does or does not happen.
Keep these ideas on your radar over the coming weeks.
Signals just added its first pure machine learning trade rule…
Yesterday, we just added our first machine-learning rule to our newly launched Signals trading tool.
It doesn’t look at balance sheets… read earnings reports… or follow news headlines. Instead, it detects tiny anomalies in stocks’ historical data. Then it finds statistical connections between them that a human analyst would never find.
Think of it like a “thumbprint.” Every great trade has one. A unique alignment of factors – technical indicators, price patterns, market conditions – that has lined up before.
When those factors align again, our system flags a high-probability setup. Some with historical accuracy rates of 90% or more.
Until recently, every entry and exit rule was built from a combination of observable technical patterns: price structure, momentum shifts, volume anomalies. Some of these rules were built by human analysts. Others were discovered by AI and then tested by our team.
Last week, we added something new. The first trade rule in Signals that’s entirely based on a machine-learning algorithm.
On Tuesday, that signal fired in Lululemon Athletica (LULU).
This LULU machine-learning signal has been accurate 82.2% of the time on LULU specifically over 10 years of historical data. The typical gain from winning trades is +2.3%. The average hold time is about eight days.
Even more important, it has a Quality Score of 90.2.
It runs from 1 to 100. And it’s the result of two separate machine-learning models working together in real time.
The first model looks backward. It grades a signal’s full historical track record — win rate, average gain, how many times the signal has fired in the past.
The second model looks at the present. It evaluates current market conditions and asks whether those conditions match the ones under which this signal has historically performed best.
A signal with a 100% historical win rate can score lower than one with an 80% win rate — if today’s conditions don’t support it. And a signal with a modest track record can score very high if the conditions are ideal.
Lululemon has been under pressure this year. The stock is off sharply from its 2023 highs, hit by slowing growth concerns and a market that’s rotated away from consumer discretionary companies.
But our machine-learning model isn’t reading the narratives. It’s reading the price patterns – and right now, it’s seeing a setup.
Keep an eye on LULU over the next week or so for signs of a short-term recovery. If you take the trade, the typical exit comes around eight days, so take your profits within that window.
The Signals Master Portfolio launches Thursday – here’s your last chance to join.
Tomorrow, May 7, at 10:30 a.m. ET, we’re launching the Signals Master Portfolio – our most turnkey way to profit from these setups.
It’s a model portfolio built directly on top of our Signals technology that’s designed to make using the system as turnkey as possible.
The portfolio will hold three stocks at any given time. Each one is selected by the Signals algorithm, based on win rate, average gain, number of past occurrences, and Quality Score.
The aim is to have three open trades in the portfolio at all times. These trades are selected to work in sync to produce the best compounded returns over time.
We backtested the strategy from January 2020 through January 2026 – a stretch that included the COVID crash, the 2022 bear market, and the full inflation and rate cycle that followed. The result: a compounded annual return of 54% vs. 15% for the S&P 500. Nearly four times better.
At the same time, the strategy’s worst drawdown over that period was 18.1% – well below the S&P 500’s worst drawdown of 25% in 2022. Higher returns with less pain during difficult markets.
We closed our Signals charter offer last week. But we’re reopening the door for just one more day to give folks a chance to join in for the Signals Master Portfolio.
If you’ve been following this coverage and like what you’ve seen, this is your window. The Master Portfolio launches tomorrow, and after that, the opportunity is gone.
Watch the full story from our CEO, Keith Kaplan, and join before the window closes.
To building wealth beyond measure,
Michael Salvatore
Editor, TradeSmith Daily
