Yes — hedge funds absolutely try to model retail investor behavior, because retail flows can move markets in ways that aren’t fully rational or fundamentals-based. Since 2020 (meme stocks, Reddit, Robinhood), this has become even more important.
Here’s how they approach it:
1. Data Sources Hedge Funds Use
Brokerage flow data: Some funds buy aggregated retail order flow data (e.g., from brokers like Robinhood, TD Ameritrade, Schwab).
Options activity: Retail loves short-dated, out-of-the-money calls (especially post-2020). Hedge funds track these because retail call buying can force gamma squeezes.
Social media & sentiment:
NLP models scrape Reddit (WallStreetBets), Twitter/X, StockTwits, TikTok, Discord.
Transformers classify sentiment and detect spikes in chatter volume.
ETFs & index flows: Retail tends to buy/sell ETFs during volatile markets — this creates predictable flows.
2. Modeling Techniques
Agent-based models: Simulate “representative retail investors” with limited capital, short time horizons, and herding behavior.
Sentiment analysis (NLP):
Transformers (like BERT, FinBERT, GPT-style models) analyze retail chatter to predict buy/sell surges.
Example: surge in “$GME 
” sentiment → hedge funds model potential inflows before price reacts.
Flow models:
Statistical models link retail net buying/selling with short-term price impact.
Example: If retail is 30% net buyers in a small-cap, model says expect +X% move tomorrow.
Behavioral reinforcement learning: Some funds model retail like an RL agent — “reward” is quick gains, “punishment” is losses — to predict repeated behavior (chasing momentum, panic selling, etc.).
3. Real-World Use Cases
Meme stocks (GME, AMC, BBBY, 2021):
Hedge funds tracked Reddit activity with NLP models.
Some got burned (Melvin Capital short squeeze), but others built models to anticipate gamma squeezes and rode the wave.
Options markets: Retail buying weekly calls in Tesla, Nvidia, etc., creates predictable hedging flows from market makers. Quant funds now model this daily.
Crypto: Retail flows dominate crypto, so hedge funds rely on social media sentiment + exchange order book ML models to anticipate swings.
4. Key Insight
Hedge funds don’t model retail investors as “rational agents.” Instead, they treat them as flow + sentiment driven actors, and the models are:
NLP/transformers for sentiment.
Statistical flow models for trading impact.
Agent-based or RL models for behavioral patterns.
The goal isn’t to copy retail, but to front-run or hedge against them.
So in short: no, real-time retail order flow data isn’t public — but there are regulatory reports and costly third-party estimates that institutions use to approximate it.