This article is from: Sixiang Gangyin
01 The Genes of Hunting and Fleeing
Pattern recognition is a fundamental way humans understand the world. Apples come in red, green, or yellow; they vary in size and shape—round, pointed, whole, halved, sliced, or even just the core. Humans first build a mental model of an apple and then recognize it regardless of its form.
However, patterns range from simple to complex—some are innate abilities, while others require long-term learning to establish.
When someone begins investing for the first time, they identify opportunities using the most primal pattern: hunting prey and evading predators. The instinctive response to hunting is excitement, driving aggressive action; the instinctive response to predators is fear, prompting flight.
These instincts are encoded in our genes by our ancestors and stem from emotional responses in the hypothalamus rather than cognitive processing in the cerebral cortex. These two instinctual reactions have the highest priority because opportunities are fleeting and demand rapid responses.
Thus, new investors instinctively activate this pattern: when stock prices plummet sharply, fear triggers immediate selling; when prices surge rapidly, excitement akin to hunting drives impulsive buying.
However, more evolutionarily advanced animals face increasingly complex environments where predators and prey are not always clearly distinguishable. Misjudgment can be fatal—reckless attacks may lead to death, while indiscriminate fleeing may exhaust an animal to the point of starvation.
Similarly, genuine trend-driven market movements in the stock market are extremely rare; most periods consist of sideways or choppy trading ranges. Relying on simplistic 'buy high, sell low' behavior often results in catastrophic losses.
Therefore, investors must develop more sophisticated 'opportunity-risk recognition models' through deliberate learning. An investor’s skill level ultimately reflects their pattern recognition capability, which manifests along two distinct dimensions:
1. At equal recognition speed, the more complex model built through long-term learning prevails.
2. Between equally complex models, the one with faster recognition speed wins.
02 Deficiencies in Human Pattern Recognition
Human nature always leads investors to favor superficial patterns that are immediately apparent. To reassure themselves, they even glorify this tendency with phrases like 'simplicity is the ultimate sophistication.' In the comment sections of public accounts, people often tell me, 'What you’re saying is too complicated—it’s just that one thing, right? It can be summed up in a single sentence.'
Simple patterns aren’t valued for their usability—they’re valued for speed. No matter how fast your brain is, can it possibly outpace a computer?
Quantitative strategies don’t just fall from the sky. Some claim that quantitative firms analyze winning strategies from stock-trading competitions and convert them into quant models. While this is somewhat exaggerated—such conversion isn’t that straightforward—it’s roughly accurate: effective methods used in the stock market are transformed into strategies that computers can understand and execute. These strategies then leverage superior computational power to scan the entire market for opportunities and execute trades at unmatched speeds, ultimately outperforming human 'masters.'
However, not all investment approaches can be converted into quantitative strategies. Fundamental analysis isn’t merely about calculating simple metrics; it involves constructing highly complex, almost 'black-box'-like models in the human mind. Moreover, corporate operations are inherently slow-moving variables—no matter how fast your computer is, it offers little advantage here.
Thus, the path of 'speed conquers all' in investing has already been sealed off by quantitative trading. The only viable direction left for discretionary human investing is toward greater complexity—toward 'black-box pattern recognition' so intricate that even the investor cannot fully articulate it.
Another threat posed by quantitative strategies lies in their ability to correct cognitive biases inherent in the human brain—they can identify 'spurious causality, illusory patterns, and genuine randomness.'
Humans are meaning-seeking creatures. Our brains constantly strive to establish causal relationships between phenomena and events to fulfill our need for control over the world. Yet the real world is full of randomness, and most phenomena lack obvious causes. Consequently, the human brain has evolved a mechanism to avoid randomness: we distrust coincidence and forcibly reinterpret observed random events as meaningful causal relationships.
When the stock market declines, intuition blames short-selling mechanisms—so people point fingers at the supposedly evil securities lending (zhuan rong tong) system and call for restrictions on short selling. But after imposing those restrictions, the market keeps falling—so it must be quant trading; thus, they propose capping quant trading speeds. If it still falls, the blame shifts to excessively high valuations of new listings, and suddenly the solution becomes to liberalize 'securities lending' again…
The stock market is falling—surely it’s because there are too many IPOs and regulation is too lax, so let’s suspend IPOs. But if it keeps falling, then it must be because there are too many ST-listed companies and regulation is too strict; thus, a suspension is recommended. Yet if the market still declines after the suspension, it’s because listed companies have grown outdated, and we should therefore loosen IPO restrictions to allow high-quality firms to list...
Most people prefer to seek explanations from things that align with their intuition. In the age of mass media, only intuitively understandable causes gain widespread traction. Deeper, more complex reasons are neither comprehensible nor of interest to most—they’d rather assume that if they can’t make money, someone must be to blame; and if no villain can be found, then the system itself must be flawed...
Another characteristic of the human brain is its preference for accepting patterns that appear intuitively obvious:
Because the previous two crashes of small-cap stocks led to sharp market declines, investors bought into small caps again, and when regulators eventually stepped in to reassure the market, they made profits. So this time, they’re repeating the same strategy. However, such overly simplistic patterns tend to attract an increasing number of participants each time they recur, leading to three possible outcomes:
1. The decline isn’t deep enough to allow buying: the market doesn’t fall far enough before early buyers step in, resulting in limited rebound potential;
2. Buying at a halfway point: if you manage to buy near the bottom, it likely means selling pressure is even heavier than in the prior two episodes—implying a larger, hidden crisis beneath the surface. The market may pause briefly at this level before resuming its downward trend;
3. Buying the wrong thing: you successfully buy at the bottom, and the broader market rebounds—but the sectors that actually rise are those that hadn’t fallen in the first place, while the ones you bought continue to decline.
This reasoning may not always hold true, but if you keep repeating the same approach, eventually you’ll lose all the profits you previously earned.
To make sound investment decisions, you must avoid thinking like the majority. This doesn’t mean deliberately adopting ‘contrarian’ views—that would still be a form of intellectual laziness. Instead, you should strive to build mental models that are as complex as you can effectively manage.
03 How Investors Improve
The 'danger—food' recognition model is innate; all other recognition models require repeated practice and learning through costly mistakes.
Take value investing and company valuation analysis as an example:
Drawing on your everyday experience with good companies, you might easily form an initial mental model when first entering the stock market: a good company = strong demand + high product market share + high gross margin.
All good companies fit this description—but not everything that fits this description is necessarily a good company. This becomes clear only after you encounter a company whose brand and products are in steady decline, causing you significant losses.
If you don’t give up but instead reflect deeply on what went wrong with your recognition model, you’re likely to find answers in books about Buffett: it’s not enough to have high market share—you also need a moat; it’s not just about gross margin—the key metrics are ROE and ROIC.
You then refine your model by incorporating Buffett’s criteria for identifying quality companies and update your watchlist accordingly. Yet again, you lose money on a company that was overvalued, realizing to your surprise that earnings can rise while the stock price falls.
Undeterred, you dive into books on valuation methodologies and attempt to integrate valuation factors into your model—only to soon fall into a 'valuation trap,' losing money once more by placing too much emphasis on valuation alone.
By now, you’ve gained enough experience to gradually strike a balance between fundamentals and valuation, even booking a few profitable trades. Just as you begin to feel confident that your recognition model has matured, trouble strikes again: you invest in a company with clear competitive advantages, operating in a highly favorable industry, and trading at a reasonable valuation. After thorough fundamental checks reveal no red flags, you conclude the market must be wrong—and keep adding to your position as the price drops, only to see it fall further.
After consulting industry analysts, you realize your prior model paid insufficient attention to competitive dynamics. It’s not enough to assess a company’s absolute strengths; you must also evaluate the industry’s investment intensity—that is, the capital cycle. Even the most exceptional businesses cannot withstand a prolonged downward cycle.
Although you lost money, your model has improved once again. It’s now much harder for anyone to inflict major losses on you—but you’re no longer as brimming with confidence as before. You’ve come to realize that no matter how much you improve, new challenges keep emerging, such as:
1. Is macroeconomics really as you previously imagined—'a waste of time every time you study it once more'?
2. Is the market truly as error-prone as you once believed? Are those mispricings actually mistakes, or early warnings of future risks?
3. Can you genuinely remain unfazed by market volatility and wait patiently for 'the rose of time'? Moreover, is Buffett’s success attributable to his methodology itself, or is it largely a case of massive survivorship bias?
4. Were your past profits truly generated from alpha? If the market were to lose all beta going forward, would you still be able to generate returns?
……
Each time your model suffers consecutive losses, you attempt to incorporate new factors or operational methods into it. However, most of the time, these either fundamentally conflict with your existing model or produce even worse results, forcing you to revert to your original approach.
This essentially means your model has already stabilized and can only undergo minor adjustments; significant improvement is unlikely. You must accept its periodic ineffectiveness unless you are willing to completely rebuild it from scratch.
Therefore, the purpose of a model is not to avoid errors altogether, but to establish a stable profit-generating framework. Different models excel at identifying different types of opportunities: some offer high win rates with low payoff ratios, others low win rates with high payoff ratios; some deliver low returns with minimal volatility, while others do the opposite. Generally speaking, the more complex the model, the more stable your excess returns will be.
04 Investors Each Have Their Own Weaknesses
The preceding sections outlined the development process of a basic opportunity-identification model for assessing corporate investment value, leading to the following two conclusions:
1. Truly effective identification models are invariably highly complex and possess strong extensibility.
2. Truly effective identification models will inevitably become ineffective over time, continually motivating users to explore new approaches.
Humanity long ago emerged from prehistoric times, yet our brains remain wired for the core functions of that era—efficient hunting and rapid escape. We still instinctively rely on the quickest mental models, drawing overly simplistic conclusions about complex present-day situations and mistaking self-justifying narratives for deep, rational thought.
Ultimately, stock investing has a tendency to magnify human weaknesses infinitely—
Those who think too superficially need no further explanation;
Those who constantly seek full clarity about every event forget that investing is about making decisions under incomplete information, often causing them to miss opportunities;
Those with overly strong logical reasoning may lack the ability to self-correct; if their initial assumption is wrong, their talent for constructing coherent justifications ends up deceiving themselves;
Those who rely too heavily on intuition often lack the capacity to systematically refine their approach, preventing them from developing a consistent investment methodology;
Those overly sensitive to change frequently lack the resolve to hold onto promising opportunities;
Patient individuals, meanwhile, risk persisting with minor errors until they become major ones;
Thus, as always, the value of a model lies not in avoiding mistakes altogether, but in keeping one’s weaknesses within acceptable bounds—thereby enabling consistent profitability.
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Editor/Stephen