ChatGPT experienced one of the fastest consumer adoption curves in tech history, reaching 400 million users by March 2025. Its intuitive interface and broad capabilities made it a go-to tool for students, professionals, and everyday users. It’s now widely used for general research, writing assistance, coding, and summarizing complex topics. With continuous updates and integrations, ChatGPT has become embedded in daily workflows across industries.
The application of AI in financial research sees ChatGPT being increasingly used to summarize SEC filings and earnings calls transcripts. This promises significant time savings from hours of reading dense 100-page 10-Ks or skimming through 4 or 5 transcripts. Analysts and investors rely on it to expedite diligence and generate informed investment questions.
The allure of leveraging Generative AI, such as ChatGPT, for investment research is undeniable, promising unprecedented speed and data synthesis. However, a prudent approach of 'trust but verify' is paramount, as several inherent limitations can lead to significant analytical pitfalls.
There are four major problems with using ChatGPT for investing research:
The quality of your output is directly related to the quality of your inputs or sources. We often say, “garbage in, garbage out.”
Pulling sources from Investing.com, MSNBC, and a random Seeking Alpha article. All of these articles are written by journalists or freelancers for clicks and ads, rather than being helpful to investors or traders. These articles are also known for their high error rates, as well as misleading or biased narratives.
Crucially, ChatGPT lacks direct, real-time access to essential primary sources:
These primary sources are the foundation for all research, as these documents are legally mandated and audited by multiple parties.
ChatGPT is a consumer-grade, general-purpose AI wrapper built on top of OpenAI’s large language model; it’s a software application that leverages the OpenAI LLM.
Being both general-purpose and focused on consumers, ChatGPT must cater to a wide range of users, including programmers, copywriters, individuals seeking therapy, language learners, image generators, travelers planning itineraries, and hobbyist chefs searching for new recipes, among others.
A lot of those use cases require ChatGPT to be creative. By allowing ChatGPT to be creative, it opens up the possibility & increases the probability of hallucinations (i.e., taking facts and making fiction). Sadly, as investors, we cannot invest in fiction.
This is a first-principles issue with using ChatGPT: creative work and highly accurate factual work inherently conflict.
When you build any AI wrapper, you have to set the “degree of randomness” parameters: temperature, top-K, and top-P. If those are set too “tight,” then ChatGPT can’t write excellent copy, tell you a fantastic recipe that works with the 10 ingredients in your fridge, or generate cartoon-like images, etc.
So, ChatGPT has set the parameters to be reasonably creative. However, reasonably creative means a certain level of inaccuracies from time to time.
Investment research demands specific, often nuanced, workflows that general-purpose AI like ChatGPT isn't equipped to handle reliably:
ChatGPT’s LLM models have a context window of 128,000 to 200,000 tokens. With a bit of math, this translates to approximately 210–260 pages of written text (equivalent to two 10-Ks) that can be inserted into the chat input box at a time. Therefore, there is a limit to the amount of information ChatGPT can analyze at one time. This does not even include the prompt instructions that need to be passed to the model. Fundamental analysis is not just a matter of two documents and done.
An alternative method is to upload a few 10-Ks into ChatGPT.
This method introduces significant analytical risks:
And if the uploaded documents do not contain the data, then your analysis is out of luck.
We understand that your time is valuable, and in a world drowning in data, it’s essential to surface accurate and actionable insights.
Unlike consumer-grade, general-purpose AI that requires you to provide context, Enlo AI operates on a vast, curated repository of a decade's worth of SEC filings, transcripts, presentations, and historical financials for over 1,300 top companies tradable on US stock exchanges. This means you're not just summarizing isolated PDFs; you're querying an interconnected financial knowledge base.
We have parsed, organized, and pre-processed the filings into tens of millions of data points; sometimes transforming presentation images into actionable insights. You gain insights grounded exclusively in primary source data; no need to sift through blogs or forums.
Our system doesn't just use one LLM; we employ an average of seven specialized models per chat, each with parameters tuned for factual accuracy. Each step of our 'Enlo Algorithm' is optimized for financial analysis, from reasoning to double verification and arithmetic checks.
Crucially, every datapoint is cited directly to its source document, giving you instant verification and the ability to dive deeper with confidence – a level of transparency and reliability essential for professional decision-making.
Ready to experience the future of financial research? Learn more about Enlo AI.
Powerful Tools. Clear Signals. Smarter Decisions.
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