July 9, 2024
Gain a Sharper AI Edge to Find Winners
Dear Subscriber,
Editor’s Note: With AI dominating headlines, the natural question of how we can use this groundbreaking technology looms large.
To help answer that question as it applies to your financial journey, Dr. Martin Weiss just held his urgent briefing, AI Profit Bonanza earlier today.
In it, he explains how Weiss Rating’s own AI model, IRVING, can beat the S&P 500 Index by 51x.
I suggest you check out that briefing when you get a chance. And in the meantime, you can read up on how IRVING performs compared to models like ChatGPT from our resident tech expert, Jurica Dujmovic, below.
To your wealth,
Beth Canova
Crypto Managing Editor
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| By Jurica Dujmovic |
I recently took you on a tour of the strengths and weaknesses of a custom GPT on ChatGPT called "Finance & Trading: Stock, Crypto, Forex Investing."
My goal was to sort out whether a large language model AI could be used as a reliable tool in my investing research.
I gave it the prompt: “What are the top 10 S&P 500, Dow and Nasdaq stocks to sell right now?”
The results were mixed.
On the surface, things looked good. The AI generated a list of recognizable stocks that seemed to fit the bill.
But there was no rhyme nor reason why the AI gave me those names.
I did some of my own analysis on those names. It appears ChatGPT sorted through its data source and spat out similar-sounding answers based on old data. Further, it claimed incorrect sources for several answers.
That said, this is exactly what we can expect from large language models. There’s nothing “intelligent” about them.
Like the kid who’d copy your homework in grade school, large language models don’t understand the information they’re sifting through. So, they can’t offer robust analysis. They just copy and paste in a way that mimics human communication.
That’s why knowing what AI model you’re working with is key.
However, there’s a better way to use AI to scour the vast, nearly 13,000-stock universe.
I gave this same prompt to Weiss Ratings' own AI model, IRVING.
Unlike ChatGPT, this model is built using machine learning techniques specifically designed for financial analysis.
That creates a fundamental difference in technology and capability. And, as you’ll soon see, in the results.
Machine learning models used in finance are:
- Data-Driven: They work with structured, real-time financial data.
- Specialized: Designed specifically for financial analysis and prediction.
- Consistent: They produce reproducible results based on the same input data.
- Focused: They perform specific tasks like risk assessment or trend prediction.
So, when our IRVING AI — named after our founder Dr. Martin Weiss’ father, Irving Weiss — was given the same prompt, its response was vastly different.
That’s because it has been fed proprietary real-time data, rather than static, unvetted third-party webpage content. It also has access to mathematical models designed by Weiss Ratings experts and analysts.
IRVING’s output was a simple list of stocks and tickers:
1. Universal Health Services (UHS)
2. Amphenol Corp. (APH)
3. Skyworks Solutions (SWKS)
4. Hewlett-Packard Enterprise Co. (HPE)
5. PulteGroup (PHM)
6. Applied Materials (AMAT)
7. Dominion Energy (D)
8. Digital Realty Trust. (DLR)
9. Chipotle Mexican Grill (CMG)
10. Carrier Global Corp. (CARR)
And, because this was the Weiss Ratings model, I was able to follow up with the expert minds behind it.
That team told me they used two models to come up with the above response.
First, they removed the stocks from their main AI model that didn’t qualify for inclusion in IRVING’s universe. There are very strict criteria that include fundamentals and the Weiss stock ratings themselves. And IRVING is constantly working with fresh data to ensure that the right stocks are kept, and the wrong ones get booted in real time.
Then, they married that output with that of another AI model to look at expectations of how those stocks were set to perform (whether outperform or underperform) the broad-market S&P 500 Index over the coming 30 days.
The combination of the two gave us the list you see above.
This commitment to providing and processing data in real time is pivotal in making AI programs like IRVING better financial tools than custom GPTs.
Now, the stock list I gave you above isn’t the newest one. However, you can gain access to the freshest signals from IRVING as soon as next week.
If you want to learn more about IRVING and the future of AI in finance, I suggest you watch Dr. Weiss’ latest briefing, AI Profit Bonanza.
IRVING is solely focused on stocks as of this writing. But AI isn’t going away. In fact, I wouldn’t be surprised to see it show up in other trading vehicles and publications, including crypto.
In the meantime, my opinion is that the future of AI in investing likely lies in a hybrid approach.
We may see systems that combine the analytical power of specialized machine learning models with the explanatory capabilities of large language models, all under human supervision.
This could provide investors with both data-driven insights and understandable rationales for investment decisions.
For now, individual investors should approach AI-generated financial advice with caution. Even the most advanced AI is a tool to augment human decision-making, not replace it entirely.
If you do use tools like ChatGPT, my suggestion would be to use it for initial research or to generate ideas. Then, the next step should be to follow up with your own research.
Remember, you are the one in control of your financial destiny.
As AI continues to evolve, staying informed about its capabilities and limitations will be crucial for anyone looking to leverage these technologies in their investment strategies.
The key here is to harness AI's strengths while being mindful of its weaknesses, ultimately using it to enhance, rather than dictate, our financial decisions.
Best,
Jurica Dujmovic
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