Monday Momentum - IndexGPT

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About a year ago, JP Morgan filed a trademark for the term "IndexGPT" which caused a frenzy of speculation. Here was one of the world's largest banks getting involved with the AI craze, building some secretive project leveraging the new technology.

A lot more information about this project is now known, and it appears that JP Morgan is building investment products using OpenAI's GPT-4 language model that mirror indexes with alternative stocks. Their goal is to find lesser-known companies that fit into a basket of investments performing like said index. This allows for the creation of a wider range of investment products and services that they can then sell to their clients.

Most fund managers and institutions earn fees for the use of such products, but there is only so much variety and differentiation available if your goal is to benchmark against the index. A popular twist on these types of investment products has been the rise of "thematic" investing in 2020 and beyond: essentially, you can create a basket of stocks that mirror and follow a certain trend like gaming, green energy, or AI.

If we look at this calendar year, for example, any company remotely benefiting from AI has seen their stock price rise dramatically. Arguably benefiting the most, Nvidia is up more than 86% year-to-date and at one point was trading at a whopping $974 per share. If you had created a "themed" basket of stocks around AI exposure, that portfolio would benefit from this momentum and share in all of the upside.

Thematic investing also has its downsides. The risk of losses increases when momentum shifts. If you are overexposed to a certain industry or sector, one bad news report can lead to leveraged losses in your portfolio. These strategies require timing, knowledge of trends, and the ability to quickly capitalize on new trends as they arise.

This is where the type of AI-driven products that JP Morgan is building could be interesting. These language models can rapidly identify companies that fit a particular trend, and the financial models produced tend to be more accurate than historical data. I recently spoke at length with someone who moved from investments to AI research, and he described the difference in models as such: in the past, you could create models with decent accuracy that were highly explainable; now, you can create models with much higher accuracy, but much lower explainability.

Even though we may not understand fully how they work, we realize that they do work. Better yet, these tools are widely available and are not gated by big banks and institutions. What many large companies are also quickly realizing is that building AI solutions in-house is way harder than anticipated. These models are likely to hallucinate (or spit out bogus information) unless they are properly trained. When it comes to financial products, you can imagine that these hallucinations can lead to several headaches and, in worst cases, regulatory action from the government.

If you're still reading, this is where I get to the point of everything written above. Whether or not JP Morgan is successful in building IndexGPT is irrelevant. By trademarking the name and researching the tech, they have shown that large institutions are looking to capitalize on the rise of AI. Many are also now wisening up to the fact that buying is often a better path than trying to build; in the same way that we go to see a doctor when we're sick, banks can go to engineers who deeply understand this stuff and buy the solutions they know will work.

Moving forward, it is my belief that smart institutional money will start a massive campaign of acquisitions. Several fintech startups leveraging AI will create some truly mind-blowing products, and many will have the option to pursue fast liquidity by offloading to these institutions. This will lead to better products for retail investors as technology enables smarter investing on a larger scale.

There will likely also be several other positive downstream effects of this. Venture capital and private equity funds have been facing liquidity problems for several years now. Fewer companies are going public, and there have been fewer acquisitions in the face of rising interest rates. As we enter a landscape where interest rates may fall and as financial institutions look to acquire new tech for their clients, this liquidity will help these funds return money to investors and raise further funds to continue driving innovation. It becomes a virtuous cycle.

Long story short, it's an exciting time if you have any interest in tech and finance. The power of AI modeling to both change the investment landscape and increase the breadth of investment products available will lead to several exciting years of development. This is particularly relevant with the recent passing of Jim Simons, considered by many to be the father of quant investing and the founder of Renaissance Technologies - but more on that next week.

Other Cool Stuff

  1. A16Z In The Vault podcast with Marco Argenti, Chief Information Officer at Goldman Sachs
  2. Bill Ackman and Ryan Israel of Pershing Square Capital Management at the recent UBS Fireside Chat
  3. Capital Allocators by Ted Seides (to learn more about institutional investing and decision-making)

Important Links

  1. Purchase my goal-setting course 🚦
  2. Connect with me on LinkedIn

Justin Wright

Former chemist, former pro athlete, and current film producer sharing the lessons I've learned along the way.

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