A trader scans another red candle on his Bloomberg terminal. Another day, another loss. Another reminder that the market is random, unpredictable, impossible to beat.

But what if it's not?

What if you had a model-just math, no magic-that could consistently generate 50%+ annual returns?

In 1969, two researchers named JS and Baum published a paper that claimed exactly that. The title was dry: Probabilistic Models for and Prediction of Stock Market Behavior. The content was explosive.

They proposed a trading method that could generate annual gains of at least 50 percent. Not through insider trading. Not through luck. Through probability.

Few listened. Fewer understood. And the paper faded into obscurity.

But the ideas didn't die. They just went underground. And today, the biggest quant funds in the world are using variations of what JS and Baum discovered fifty years ago.

The Forgotten Paper

JS and Baum weren't household names. They weren't running hedge funds or managing billions. They were researchers working on a problem that Wall Street had declared unsolvable: can you predict the stock market?

The prevailing wisdom said no. The Efficient Market Hypothesis (EMH) was gaining traction. Markets are random walks. Past prices don't predict future prices. You can't beat the market consistently. Anyone who claims otherwise is either lying or lucky.

JS and Baum disagreed. Not because they thought markets were predictable in the traditional sense. But because they thought markets were probabilistic systems with hidden patterns.

Their paper flew under the radar. Too technical for most traders. Too threatening for academics invested in EMH. Published at a time when computing power was limited and data was expensive.

But the core claim was undeniable: a methodological framework that could yield 50% annual gains.

What Made It Different

Most trading strategies at the time fell into two camps: fundamental analysis (study the company) or technical analysis (study the chart).

JS and Baum proposed a third way: probabilistic modeling.

Here's the difference:

Fundamental analysis says: this company is undervalued, so the stock will go up.

Technical analysis says: this pattern usually precedes a rally, so the stock will go up.

Probabilistic modeling says: given the current state of the market and the historical transitions between states, there's a 65% chance the stock goes up and a 35% chance it goes down. Bet accordingly.

It's not about certainty. It's about odds.

Think of it like counting cards in blackjack. You're not predicting the next card. You're calculating the probability distribution of possible cards and betting when the odds are in your favor.

Markets aren't entirely efficient. But they're not entirely random either. They're statistical systems. And statistical systems have patterns. Not deterministic patterns. Probabilistic patterns.

The Mechanism

JS and Baum's approach was built on Markov chains and conditional forecasting.

Here's the simplified version:

Markets exist in different states. Bull market. Bear market. High volatility. Low volatility. Trending. Ranging.

Each state has a probability of transitioning to another state. A bull market might have a 70% chance of staying bullish tomorrow, a 20% chance of becoming neutral, and a 10% chance of turning bearish.

These probabilities aren't fixed. They change based on observable conditions: volume, volatility, momentum, breadth.

JS and Baum's model tracked these state transitions over time. It didn't predict what would happen. It predicted the likelihood of what would happen. And it bet when the odds were favorable.

The key insight: markets have memory. Not in the sense that past prices determine future prices. But in the sense that the current state of the market influences the probability distribution of future states.

This is different from a random walk, where each step is independent. And it's different from deterministic models, where you claim to know what will happen next.

It's probabilistic. You don't know. But you can calculate the odds. And if you bet consistently when the odds are in your favor, you win over time.

The 50% Return Claim

Most hedge funds aim for 7-15% annual returns. The S&P 500 averages about 10% over the long term.

JS and Baum claimed their model could generate at least 50% annually.

That's not a typo. Fifty percent.

How? By identifying high-probability transitions and betting aggressively when the odds were favorable. By avoiding trades when the probability distribution was flat. By compounding gains over many small, favorable bets.

The model wasn't designed to be right every time. It was designed to be right more often than it was wrong. And when it was right, to win more than it lost when it was wrong.

This is the Kelly Criterion in action: bet proportionally to your edge. If you have a 60% chance of winning and the payoff is even, you bet. If you have a 50% chance, you don't.

JS and Baum's model was essentially a sophisticated probability calculator that told you when to bet and how much.

Why Wall Street Ignored Them

If the model was so good, why didn't everyone use it?

Several reasons:

1. Too complex. In 1969, most traders didn't have the mathematical background to understand Markov chains and conditional probability. The paper was written for statisticians, not traders.

2. Too threatening. The Efficient Market Hypothesis was becoming orthodoxy. Academics had built careers on the idea that markets are random. A paper claiming you could beat the market by 50% annually was heresy.

3. Too expensive. The model required significant computing power and data. In 1969, that meant mainframes and punch cards. Most traders didn't have access.

4. Too risky. Even if the model worked, it required discipline. You had to follow the probabilities, not your gut. You had to bet when the model said bet, even when it felt wrong. Most traders couldn't do that.

So the paper was filed away. Cited occasionally. Mostly forgotten.

But the ideas survived.

What If They Were Right?

If JS and Baum were right, the implications are massive.

The Efficient Market Hypothesis is wrong. Not completely wrong. But wrong enough. Markets aren't perfectly efficient. They're approximately efficient with exploitable inefficiencies.

Skill matters. You can beat the market. Not through luck. Not through insider information. Through better models. Better probability calculations. Better discipline.

Math beats intuition. The best traders aren't the ones with the best gut instincts. They're the ones with the best models.

And here's the thing: we now have evidence that they were right.

The Modern Proof

Renaissance Technologies. Two Sigma. DE Shaw. Citadel.

The biggest, most successful hedge funds in the world are all quant funds. They all use probabilistic models. They all treat markets as statistical systems with exploitable patterns.

Renaissance's Medallion Fund has averaged 66% annual returns since 1988. After fees.

That's better than JS and Baum's claim. And it's done using the same fundamental approach: probabilistic modeling of market state transitions.

Machine learning is just probabilistic modeling at scale. Neural networks are just sophisticated probability calculators. The entire quant revolution is built on the foundation JS and Baum laid in 1969.

They were right. Wall Street just wasn't ready to listen.

Why It Still Matters Today

Markets today are faster, noisier, more complex. But they're still not perfectly efficient.

As data gets cheaper and compute gets stronger, probabilistic trading is becoming the norm. Not just for hedge funds. For retail traders too.

You can now run models on your laptop that would have required a supercomputer in 1969. You can access data that was impossible to get fifty years ago. You can backtest strategies in minutes that would have taken months.

The barrier isn't access anymore. It's understanding.

JS and Baum's paper is a historical blueprint. It shows you how to think about markets probabilistically. How to calculate odds. How to bet when the edge is in your favor.

For modern traders, it's a reminder: markets are games of odds, not certainties. You don't need to predict the future. You just need to calculate the probabilities and bet accordingly.

For investors, it's a lesson in humility: the market can be beaten. Just not the way most people think.

The Takeaway

JS and Baum never launched a hedge fund. They never managed billions. They never became famous.

But their ideas laid the groundwork others are now racing to catch up with.

In a world obsessed with certainty, it's often probability that wins.

You don't need to know what will happen. You just need to know the odds. And if you bet consistently when the odds are in your favor, you win over time.

That's not magic. That's math.

And fifty years ago, two researchers proved it could generate 50% annual returns.

Wall Street ignored them. The quants didn't.

Maybe it's time the rest of us paid attention.