You hear the term thrown around.
But have you ever asked — what exactly is “hidden” in a Hidden Markov Model?
Here’s the answer: Market sentiment.
You can’t go around polling every trader,
“Hey, how are you feeling about Tesla today?”
“Bullish on Microsoft?”
But that unspoken sentiment is exactly what drives price.
And while you can’t observe it directly… you can infer it — using logic, structure, and data.
That’s what the Hidden Markov Model is built for.
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🤖 Step 1: Modeling the Invisible — Market Mood
In this experiment, let’s assumed one core idea:
Sentiment is the only factor that influences price.
It’s an oversimplification, yes — but a powerful one.
He defined 3 hidden sentiment states:
🙂 Bullish → returns tend to be positive
😐 Neutral → returns hover around zero
☹️ Bearish → returns tend to be negative
These are hidden states — we don’t see them.
But the model observes price + volume, and back-calculates what state the market is likely in.
Then, it models how that state transitions into the next one.
🔁 Step 2: The Sentiment Cycle
This is where the “Markov” part kicks in.
The model learns transition probabilities:
If today’s sentiment is Bullish, what’s the chance tomorrow turns Bearish?
From one year of Tesla stock data, the model learned a surprisingly clean rhythm:
Today | Most Likely Tomorrow |
|---|---|
🙂 Bullish | ☹️ Bearish (69%) |
☹️ Bearish | 😐 Neutral (67%) |
😐 Neutral | 🙂 Bullish (87%) |
📈 A cycle:
Bullish → Bearish → Neutral → Bullish again.
That’s not just predictive — it’s interpretable.
You start to see market structure not as noise… but as rotation.
📊 Step 3: Emission Probabilities — What Returns Come from What Mood?
Each hidden state also maps to a normal distribution of returns.
So if sentiment is Bullish, we expect returns from a positively-skewed distribution.
Bearish? The opposite.
Neutral? Centered near zero.
Each state has a mean and standard deviation — accounting for volatility.
And all of this is captured with just 14 parameters.
#PARANS = (3-1) + (32 - 3) + 3×2 = 14 PARAMS
🧮 Why This Matters
HMMs are lean.
This model only used:
2 initial sentiment weights
6 transition probabilities
6 return distributions
Total: 14 parameters
✅ Fast to train
✅ Low risk of overfit
✅ Easy to explain
Compare that to a neural net with hundreds (or thousands) of weights — and suddenly, this simple model becomes very practical.
💵 Step 4: The Live Test — $1,000 on the Line
This wasn’t theory. It was tested with real money.
The setup:
Rank 500 S&P stocks using the HMM-predicted next-day returns
Select the top 10
Invest $100 into each — total $1,000
Hold for 1 day
The result?
📈 +0.2% return
vs
🪙 +0.02% from randomly picking 10 stocks
That’s a 10x improvement — using a basic rule-based model.
No fancy tech.
Just logic, data, and clear structure.
🤖 Step 5: Compare It to a Neural Net
The researcher also ran the same test using a Recurrent Neural Network (RNN) — a more complex model.
RNN Result: +0.4% return
So yes — the RNN outperformed.
But there’s a catch:
Model | Return | Transparency | Overfit Risk |
|---|---|---|---|
HMM | +0.2% | ✅ High | ✅ Low |
RNN | +0.4% | ❌ Black Box | ⚠️ Higher |
The RNN gives you output — but no explanation.
You’re flying blind.
With the HMM, you get something better:
💡 A map of how the market thinks.
And when volatility hits?
That clarity is what keeps you steady.
🧠 Final Takeaway
Even a basic quant model — when grounded in logic — can outperform the market.
This isn’t about complexity.
It’s about control.
The HMM may be simple… but it teaches us:
How sentiment shifts
How to model forward
How to build systems that don’t rely on hope
That’s what we care about.
Because in trading, guessing is expensive — but structure pays.
If someone forwarded you this and you want more quant breakdowns like this, straight to your inbox:
This is where serious traders get sharper.
Not by chasing signals — but by mastering the system behind them.
Data is King
Sizing is everything!
Sean
