Every week, someone posts on social media about using ChatGPT for stock predictions. The responses are always confident. The results are always random. This isn't a prompt engineering problem - it's a fundamental architecture mismatch.
The Training Data Problem
Large language models are trained on text. Billions of words from books, articles, websites, and conversations. When GPT-4 or Claude encounters "AAPL stock," they've seen thousands of articles about Apple's stock price. They've read analyst opinions, news stories, and Reddit debates.
What they haven't processed is the actual data: the numerical time-series of Open, High, Low, Close, and Volume (OHLCV) that defines price movements. They've read the commentary track, not watched the actual movie.
LLMs have seen articles ABOUT markets, not actual market data. They've read opinions about price movements, not the price movements themselves.
Consider the difference. A financial model trained on market data sees patterns like:
# Actual market data (what prediction models need)
timestamp,open,high,low,close,volume
2026-01-28 09:30,185.50,186.20,185.30,185.90,1250000
2026-01-28 09:31,185.90,186.45,185.80,186.30,980000
2026-01-28 09:32,186.30,186.50,186.10,186.25,1100000
...
An LLM sees text like:
"Apple stock rose 2% today on strong earnings..."
"Analysts predict AAPL will hit $200 by Q3..."
"Reddit users debate whether Apple is overvalued..."
These are fundamentally different types of information. One is the signal. The other is noise about the signal.
The Hallucination of Certainty
Ask ChatGPT whether a stock will go up, and you'll get a confident-sounding response. This is the second problem: LLMs are optimized to produce fluent, coherent text. They're not optimized to say "I don't know" or to quantify their uncertainty.
> User: Will NVDA stock go up tomorrow?
> ChatGPT: Based on NVIDIA's strong position in the AI
chip market and recent positive momentum, there are
several factors that suggest potential upside. However,
it's important to consider market volatility and broader
economic conditions...
[Sounds confident, provides zero predictive value]
This response pattern-matches to financial analysis text from the training data. It sounds like something an analyst might say. But it contains no actual prediction grounded in data - because the model has no mechanism to analyze numerical time-series.
LLMs are trained to produce fluent text, not accurate predictions. They'll sound confident even when they have no basis for a prediction.
The Film Critic Analogy
Asking an LLM for stock predictions is like asking a film critic to direct a movie. The film critic has read thousands of reviews. They understand narrative structure from description. They can articulate what makes a great film.
But they've never operated a camera. They don't know how to light a scene. They can't direct actors. The skill of analyzing films (text about films) is fundamentally different from the skill of making films (the actual craft).
Similarly, LLMs have "read" financial analysis. They can generate text that sounds like financial analysis. But they've never actually analyzed the underlying numerical data that financial predictions require.
What About Fine-Tuning?
"Can't you just fine-tune an LLM on financial data?" This is a common question, and the answer reveals the depth of the architectural mismatch.
LLMs process information through tokenization - breaking text into subword units. The token "185.50" is processed as a sequence of characters, not as a numerical value that sits on a number line between 185.49 and 185.51.
# How an LLM "sees" a price
"185.50" -> ["1", "8", "5", ".", "5", "0"] # Just text tokens
# How a numerical model sees a price
185.50 -> [position on continuous scale, relationships to other values]
This isn't a minor detail - it's the core representation. You can fine-tune an LLM on financial text until the training loss looks good, but you're still teaching it to produce text patterns, not to model numerical relationships.
The Right Tool for the Job
This isn't about which AI is "better." LLMs are extraordinary tools for language tasks. They can summarize documents, answer questions, write code, and have conversations. These are legitimate, valuable capabilities.
But financial prediction requires:
- Native numerical processing - treating numbers as values, not text
- Time-series awareness - understanding sequential dependencies in data
- Probabilistic output - quantifying uncertainty rather than hallucinating confidence
- Tabular data architectures - models designed for rows and columns, not sentences and paragraphs
At Kunkafa, we build models specifically for this domain. Our architectures are designed from the ground up for tabular, time-series data. We don't adapt language models - we use the right tool for the job.
The question isn't 'which AI is smarter?' It's 'which AI architecture matches the problem?' For financial prediction, that's not LLMs.
The Bottom Line
If you're using ChatGPT for stock tips, you're getting sophisticated-sounding text generated by pattern-matching against financial articles. You're not getting predictions grounded in actual market data analysis.
The next time someone tells you "AI can predict the market," ask which AI. The architecture matters more than the hype.