The Art of Trading: No Predictions, Statistical Advantage, Diversified Dividends, Random Fluctuations (Valued at 10 million)

First, no prediction—can you really succeed in trading?

Second, what is true trend following?

Third, is diversification a conservative strategy or a structural advantage?

Let’s break it down piece by piece.

1. No prediction—can you do well in trading?

The podcast starts with a very challenging question: if you completely give up prediction, can you still trade successfully?

Most beginners assume: trading = prediction.

Before buying stocks, you need a reason—like strong fundamentals, favorable government policies, hot industry trends, or simply the belief that it will go up.

The accuracy of predictions seems to directly reflect a trader’s ability.

Now, let’s use a classic coin flip game as an analogy: imagine you and Nick play—heads, you win $100; tails, you lose $50. The probabilities are 50/50. Would you join?

The obvious answer is yes.

Because over the long run, you can’t lose money. (Mathematically, the expected value is 0.5×100 - 0.5×50 = $25.)

Now, here’s the key question: do you care whether the next coin flip is heads or tails? Would you bet more if you think the next flip is more likely to be heads?

Clearly not.

Because you know it’s purely random; the outcome of a single flip is unpredictable. Even if you lose two in a row, you keep playing because the overall expectation remains positive.

Predicting the next flip isn’t meaningful; it doesn’t improve your long-term gains. You don’t need to predict whether it will be heads or tails—you just keep participating.

Even after a string of losses, the probability structure favors you in the long run. Trading is the same.

Trading isn’t about predicting the future; it’s about repeating a positive expectation structure.

This is fundamentally different from common investment thinking.

Investing involves logical reasoning, fundamental analysis, industry cycles; trading focuses on odds structures, risk-reward ratios, position sizing.

It doesn’t care whether a particular trade is right or wrong; it cares whether this type of signal is profitable over the long term.

Just like a casino never predicts whether the next baccarat game will be banker or player; it only sets the odds. Each bet can win, but the casino always wins in the long run.

If you treat trading as a test where you must get the right answer, you’ll fall into emotional traps; if you see it as a probability game, you focus on the structure.

Nick also shared his trading data from last year: 382 closed trades, average holding period 19 days, win rate about 40%, profit-to-loss ratio slightly above 2:1 (average profit more than twice the average loss). That means 60% of trades are losers, but overall profitable. Because of the asymmetric risk-reward: winning trades earn more, losing trades lose less.

Another interesting detail: Nick controls about 4% of his capital per trade.

What does that imply? It means he doesn’t allow himself to “bet the farm.” Even the best signals are just samples from a statistical distribution.

We often think the advantage of experts lies in judgment; in reality, the bigger advantage is accepting uncertainty.

No prediction doesn’t mean no judgment. It’s about acknowledging that single outcomes are uncontrollable, and focusing energy on the long-term distribution.

2. What is trend following?

When people hear “trend following,” they often think of “buy high, sell low.”

But Nick’s explanation is much more nuanced: trend following isn’t about predicting the trend; it’s about confirming the trend.

Prediction is: I think it will go up. Confirmation is: it is already going up.

Nick uses a vivid analogy: finding a parking spot.

Imagine you drive into a mall parking lot—what do most people do? Probably look for a spot near the exit or elevator.

If you’re a value investor, you might first look at the mall’s map, analyze the layout, and choose the closest spot to the exit.

But a trend trader doesn’t look at the map; they look for where cars are most concentrated—where the most cars are parked.

Why are spots with more cars likely near the elevator? Because everyone’s goal is convenience. The flow of cars reflects collective choice—hidden behind which is the most genuine information.

The underlying assumption of trend trading is: price has already integrated all information.

The market is like a giant prediction market—everyone is betting. The price is the result of this collective vote.

This reflects an efficient market mindset.

You don’t need to know the underlying news or what’s happening; just observe the flow of funds.

For example, passing by a parking spot near the elevator that surprisingly has no cars—would you park there?

From a value investor’s perspective, that’s an undervalued, high-quality, low-traffic opportunity.

But from a trend trader’s perspective, they ignore it. Their logic: since the spot is so good but empty, maybe the elevator is broken or the exit is blocked.

In stocks, this is like stocks with seemingly perfect fundamentals that keep falling—value investors buy the dip, expecting mean reversion; trend traders believe the market is always right, and funds are fleeing for a reason unknown to outsiders.

Trend trading is often criticized as herd behavior. Nick doesn’t deny this; he admits he’s part of the herd: “Follow what’s hot.”

But he emphasizes that trend traders aren’t blindly following the herd like sheep looking at the sheep in front—they’re like “sheep with a drone overhead.”

From a macro perspective: you need to know whether the herd on the east side is growing or the herd on the west side is dispersing. You’re not trying to pick the fastest runner, but which herd is forming a gathering.

Entry timing: you shouldn’t be the first sheep (that’s gambling), nor the last to jump in. Your goal is to join when the herd is just starting to gather but before it reaches the cliff.

This logic explains why trend traders dare to chase the rally. They’re not trying to buy at the bottom, where the trend might not be confirmed yet.

They prefer to wait until the trend has risen 10% and the signal is confirmed, then enter to enjoy the best part of the move. They’re even willing to pay a higher price because “expensive” itself confirms the trend.

Of course, this sounds simple but is hard to execute.

Because trend following involves frequent stop-losses. You’ll be shaken out by oscillations. You’ll experience consecutive losses. You’ll doubt whether your rules work.

But as long as the system’s expectation is positive, one big trend can offset previous small losses.

The podcast also mentions a practical issue: most of the time, markets are not trending, and strategies wear down. The real profits often come from rare extreme moves. That’s why trend traders diversify across markets.

3. The benefits of diversification

In investing, diversification is a repeatedly emphasized principle. Most people understand it as “don’t put all eggs in one basket” to reduce risk, but Nick reveals a deeper, more fundamental benefit through a “Shan Hai Jing mythical beast experiment”: under the assumption of market randomness, diversification itself can generate positive returns—an inherent mathematical result, not just risk control.

Let’s do a simple math exercise: suppose the market has an equal chance to double or drop 50%. You buy two stocks with 1 dollar each—one doubles, one halves. What’s your total?

Answer: 2.5 dollars. Your original 2 dollars grows by 25% through this random fluctuation.

Why? Because the downside is limited to zero (total loss), while the upside is unlimited. This asymmetry in gains and losses makes diversification’s sum effect produce positive returns.

For example, a 10% drop requires an 11% rise to recover; a 20% drop needs a 25% increase; a 50% drop needs 100% gain.

The losses require higher gains to recover, but spreading capital across multiple assets means some will soar, covering others’ losses, and even generating overall growth.

This is the statistical advantage of diversification: it appears to expose you to multiple assets’ volatility, but in fact, it exploits the asymmetry of gains and losses, earning predictable returns from mathematical laws.

To verify this, Nick built a virtual world called “Shan Hai Jing,” with 240 mythical beasts (stocks). Their names are bizarre, and their daily price changes are completely random.

Note: purely random. No fundamentals, no earnings reports, no market makers, no logic. Their price changes follow a log-normal distribution with mean zero. Long-term, such a set of random numbers should break even.

But the astonishing result: the “Shan Hai Jing Index,” composed of these 240 random beasts, rose nearly 230% over 26 years, with a relatively stable upward trend.

Why can purely random assets, when diversified, generate long-term profits?

The answer is the sum effect of diversification—amplifying the asymmetry of gains and losses. Individual assets can fall to zero, but some will soar without limit, pulling the entire index upward over time.

The core conclusion of this experiment: much of the alpha of many indices comes from diversification, not from compound interest or inflation. The diversification benefit is severely underestimated by markets.

In real markets, the benefits of diversification are further amplified. Because actual market movements are not purely random—they tend to trend, and herd effects make the fat tails more pronounced.

Upward moves tend to be larger, while declines can be sharper, but diversification still captures more upside and offsets downside through the sum effect.

That’s why Buffett advocates index investing: indexes inherently implement diversification by removing the worst performers and including the best, allowing the statistical advantage to persist.

When you don’t predict individual assets, diversification becomes an inevitable choice. You don’t need to know which will become ten-baggers; you just need to be there when it happens.

This is a probabilistic patience.

Summary: From a prediction world to a probabilistic world

Putting these three pieces together, you see a complete framework:

No prediction is an acknowledgment of single-event uncertainty; trend following is respecting price behavior; diversification is leveraging statistical structure.

They’re not isolated techniques but a worldview.

We’re too accustomed to linear causal thinking: because A, then B. Because fundamentals improve, then stock prices rise. Because interest rates fall, then assets revalue.

But markets are more like complex systems. They are the result of probability superposition, emotional games, and information diffusion. In such systems, prediction is often an illusion.

The only thing you can truly grasp is the statistical structure.

It doesn’t tell you what to buy tomorrow, nor specify particular assets, nor promise high win rates.

It simply brings trading back to a more fundamental level: if the future is unknowable, how do we build an advantage in the face of uncertainty?

The answer is: design structures, not opinions.

Markets will never give you certain answers, but they will give you enough samples.

Your task isn’t to guess whether the next trade is right or wrong, but to design a system where, after a hundred trades, the answer naturally emerges.

At that moment, you stop trying to control the future and learn to coexist with randomness.