Market narratives change every day, and you must know what you are buying.

Many investors have witnessed a phenomenon:

The same news can be interpreted in completely opposite ways by the market in a very short period of time.

A while ago, the market circulated rumors that META planned to provide some of its AI computing power externally and was considering developing cloud computing services. At first, some interpreted this as bearish: Did META build too many data centers that it couldn't fully utilize internally, indicating an oversupply of AI computing power?

Later, another narrative emerged: META not only could use this computing power to train its own models but also rent out temporarily idle capacity to other companies, potentially even launching a new cloud computing business. This instead proved that the computing power it had previously built held significant commercial value.

The same event shifted from "computing power oversupply" to "monetizing computing power" in quick succession.

The underlying reality hadn't fundamentally changed; only the market's narrative about it had.

This highlights a very realistic issue in investing:

Market narratives are constantly evolving. If you don't know what you're buying, you'll just swing back and forth with the market's explanations.

The book "Influence" recounts a case study.

A bank with normal operations and good reputation suddenly faced a run on deposits one day.

Bank management was baffled: The bank clearly had no issues, so why did depositors suddenly rush to withdraw their money?

It was later discovered that the cause was absurdly simple.

That day, due to a public transit strike, a large crowd gathered at the bus stop outside the bank.

Some passersby saw so many people standing in front of the bank and mistakenly assumed the bank was about to collapse, with depositors lining up to withdraw their funds urgently.

So, they hurried into the bank and withdrew their own deposits.

People behind them saw others actually withdrawing money and became even more convinced:

This bank must be in trouble; otherwise, why would so many people be queuing?

As a result, more and more people joined the run. A bank that originally had no problems fell into a liquidity crisis simply because everyone believed it did.

The most thought-provoking aspect of this story is:

The initial group wasn't there to withdraw money at all; they were just waiting for the bus.

But those who came later couldn't see the real reason; they only saw others taking action.

Thus, the first person misunderstood the situation, the second believed the first, and the third, seeing the first two withdrawing money, assumed they possessed insider information.

In the end, it looked like hundreds of people collectively confirmed the bank's impending collapse, but in reality, everyone's judgment might have stemmed from that initial misunderstanding.

This is like a group of people standing on the street looking up at the sky. Passersby see this and look up too, and soon more and more people gather. Everyone assumes others see something special, when in fact, the first person might just have had a stiff neck.

Therefore, numbers don't equal evidence; many judgments are often just the same error repeated many times.

This is where social proof is most prone to failure:

We can see what others are doing, but we don't know why they are doing it.

Take a stock price suddenly dropping.

The first seller might just need temporary liquidity, or it could be a fund adjusting positions, passive index rebalancing, options expiration, or a quantitative trade triggering a stop-loss.

But subsequent investors don't know these specific reasons.

They only see the stock price falling and start to doubt:

Is there a problem with the company that I'm unaware of?

Subsequently, the second batch of investors starts selling.

As the stock price drops further, more people believe that those who sold earlier must possess important information.

Eventually, the price drop itself becomes treated as evidence of deteriorating fundamentals.

The same applies during rallies.

When a stock suddenly rises, the market speculates:

Did smart money get wind of positive news early?

More people follow suit and buy in, pushing the price higher. The rise itself reinforces the narrative that "the company must have major positive developments."

By the end, many buyers' reasons are no longer based on understanding the enterprise, but simply:

It keeps going up, so there must be a reason I don't know about.

But this so-called reason can sometimes be as absurd as the bus stop outside the bank.

And the market always finds an explanation for the stock price.

When stocks rise, media outlets say:

The market is optimistic about AI development;
Rising expectations for interest rate cuts;
Increased probability of a soft landing for the economy;
Improving corporate earnings prospects.

When stocks fall, another set of explanations appears:

AI valuations are too high;
Interest rate cut expectations dashed;
Uncertain macroeconomic environment;
Investors taking profits.

Even the same economic data can be interpreted in completely opposite ways.

If economic data is strong and the stock market rises, it's explained as guaranteed corporate earnings; if the stock market falls, it's explained as rising inflation pressures and delayed rate cuts.

If economic data is weak and the stock market falls, it's explained as increased recession risks; if the stock market rises, it's explained as a higher likelihood of Fed rate cuts.

This shows that many short-term market explanations aren't accurate predictions made before price changes, but rather post-hoc rationalizations for events that have already occurred.

Market narratives often follow the stock price.

When prices rise, the market automatically seeks optimistic explanations; when prices fall, it automatically seeks pessimistic ones.

And if you don't know what you've bought, the stock price will make judgments for you.

Suppose an investor buys META simply because:

Everyone says META is a core AI company.

Then, when the market starts telling the "computing power oversupply" story, they are likely to panic sell.

When the market shifts to the "cloud computing monetization" story and the stock price rebounds, they might chase the rally and buy back in.

At this point, while they appear to be investing in META, they are actually just trading market sentiment.

Because they lack their own judgment, they rely entirely on the market's.

When the price rises, they think the company is getting better; when it falls, they worry the company has problems.

But if you truly know what you're buying, your focus will be entirely different. Whether the market interprets the same news as bullish today and bearish tomorrow becomes less important.

Knowing what you've bought includes at least three things:
First, knowing how the enterprise makes money.

You must clearly understand the company's core products, revenue sources, competitive advantages, and cost structure.

Otherwise, you cannot judge whether a piece of news will truly impact the business.

For example, META's core profit still primarily comes from digital advertising. Whether AI computing investments are good or bad ultimately depends on whether they improve ad efficiency, user stickiness, or create new revenue streams, rather than just seeing the words "selling computing power" in the news.

Second, knowing why you bought.

Your investment thesis should be clear, specific, and verifiable.

For instance:

The industry still has long-term growth potential;
The company possesses unreplicable competitive advantages;
Earnings and free cash flow are expected to grow continuously;
Management allocates capital relatively rationally;
The current price hasn't priced in all future growth.

Only when your reasons for buying are clear enough can you determine during a price drop:

Has market sentiment changed, or is my investment logic truly wrong?

Third, knowing what conditions mean you are wrong.

Long-term investing doesn't mean holding regardless of what happens.

What truly matters is clarifying beforehand:

Which fluctuations are normal;
Which are short-term noise;
Which changes destroy the company's long-term value;
Under what circumstances you must re-evaluate or even sell.

If a company loses its competitive advantage, its core product is replaced, management consistently misallocates capital, or financials deteriorate significantly, you should re-evaluate even if the stock price hasn't dropped.

Conversely, if the business operations haven't changed significantly and only the market narrative has shifted, the price drop itself doesn't prove your investment logic is wrong.

Short-term stock prices are market judgments, not objective facts.

Investors certainly shouldn't ignore stock prices completely.

Price determines entry cost and future potential returns. Even the best company can yield poor results if bought at too high a price.

But stock price is merely a quote given by the market at a specific moment, not the final verdict on a company's value.

Short-term prices are determined by many factors:

Market sentiment;
Capital flows;
Macro expectations;
Position adjustments;
Leverage liquidations;
Investor fear and greed;
Imitation of others' behavior.

Long-term prices, however, must ultimately be tested by business results:

Can revenue grow?
Can profits materialize?
Can free cash flow sustain?
Is the return on capital sufficiently high?
Can competitive advantages be maintained?
Has management created shareholder value?

The market can tell countless stories in the short term, but it cannot forge business results over the long term.

Market narratives may change daily.

But long-term investors cannot change their worldview every day along with the market.

Because the most important thing in investing has never been accurately guessing what others will do next, but clearly knowing:

Exactly what you are holding in your hands.