This article is from: Thought Steel Seal
01 The Genes for Hunting and Fleeing
Pattern recognition is a fundamental way for humans to understand the world. Apples can be red, green, yellow, large, small, round, or sharp; humans need to first establish a pattern of an apple in their brain, then recognize apples regardless of their form—whether whole, halved, sliced, or even just the core—and identify them as apples.
However, patterns vary in simplicity and complexity. Some are innate abilities, while others require long-term learning to establish.
When someone first begins investing through voting, they use the most primitive patterns to identify opportunities—hunting prey and evading predators. The instinctive reaction brought about by hunting prey is excitement, which drives aggressive action; the instinctive reaction brought about by evading predators is fear, which prompts fleeing behavior.
These are emotional responses left in our genes by our ancestors, based on hypothalamic reactions rather than cognitive processes in the brain. These two instinctual reactions take the highest priority because opportunities are fleeting and demand rapid responses.
Therefore, this pattern is often the first to be activated among novice investors. When stock prices plummet, fear triggers selling; when prices surge, the excitement akin to hunting triggers buying.
However, the more evolved an animal is, the more complex its environment becomes. Predators and prey are not always clearly distinguishable, and misjudgments may result in fatal consequences—blindly attacking could lead to death, and indiscriminate avoidance could cause exhaustion leading to death by starvation.
Similarly, genuine trend-based movements in the stock market are extremely rare, with the majority being oscillating markets. If one adopts a simplistic strategy of chasing gains and cutting losses, it is highly likely that they will lose everything.
Hence, investors need to build more complex 'opportunity-risk identification models' in their minds through learning. The strength of one's investment ability is directly tied to their pattern recognition skills, which develop in two different directions:
1. At the same recognition speed, the complex model established through long-term learning prevails.
2. Similarly complex models are won by those with faster recognition speeds.
02 Flaws in Human Pattern Recognition
Human nature always leads investors to prefer shallow patterns that are immediately visible, and to comfort themselves, they call it 'the simplest truths.' In the comment sections of public account articles, people often come to tell me, you make things too complicated—it’s just this or that, a one-sentence matter.
Simple patterns aren’t about usability but speed. No matter how fast the human brain is, can it outpace a computer?
Quantitative strategies don't fall from the sky. Some say quantitative firms analyze the experiences of stock trading competition winners and turn them into quantitative strategies. This claim is somewhat exaggerated and not that simple, but essentially, it involves transforming various effective methods used in the stock market into strategies that computers can understand and execute. Then, using more powerful processing capabilities to find opportunities across the entire market and executing trades with quicker response times to outperform their 'mentors.'
However, not all methods can be turned into quantitative strategies. Fundamental analysis isn’t simply about calculating indicators; it requires building highly complex models akin to 'black boxes' in the brain. Moreover, corporate operations themselves are slow-moving variables, making even the fastest computers ineffective.
Thus, the path of 'the fastest prevails in all martial arts' has already been blocked by quantitative trading. The only direction left for human subjective investment is towards greater complexity—patterns so intricate they can't even be fully articulated, known as 'black box recognition patterns.'
Another threat posed by quantitative strategies lies in their ability to correct flaws in the human brain, identifying 'false causality, false patterns, and true randomness.'
Humans are meaning-seeking creatures. Our brains constantly strive to explain causal relationships between phenomena and events to satisfy our need for control over the world. However, the real world is full of randomness, and most phenomena do not have obvious causes. As a result, the human brain has developed mechanisms to 'avoid randomness,' refusing to believe in coincidences and distorting observed random phenomena while forcing explanations to establish causality.
When the stock market falls, intuitively, it seems related to short-selling mechanisms, so it must be the heinous securities lending system causing the problem; therefore, short selling should be restricted. But after restrictions are imposed and the market still falls, it must be due to quantitative trading, so limits on the speed of quantitative trading are introduced. Yet the market continues to decline, supposedly because new stock valuations are too high, so 'securities lending' should be relaxed again…
The stock market decline must be due to too many IPOs and lax regulation; then the IPOs were suspended. Still falling? It's because there are too many ST stocks and overly strict regulation, suggesting suspension. After suspension, it's still falling—it's because listed companies have aged, so IPOs should be relaxed to allow excellent companies to go public…
Most people like to look for reasons based on what they can intuitively understand. In the age of mass media, only those reasons that can be understood intuitively will be widely disseminated. As for deeper causes, people neither understand nor want to understand. Anyway, if I can't make money, there must be bad actors; if no bad actors can be found, then the system must be flawed…
Another characteristic of the human brain is its preference for accepting patterns that seem intuitively straightforward:
Due to the previous two crashes triggered by small-cap stock collapses, bottom-fishing in small caps eventually led to regulatory intervention and profits. Thus, this time, the same strategy is applied. However, such overly simplistic patterns attract more participants each time, leading to three possible outcomes:
1. Shallow Decline, No Opportunity to Buy: The market doesn’t fall deeply enough, with some investors buying the dip prematurely, resulting in limited rebound potential.
2. Buying at a Midpoint: If you successfully buy at the bottom, it’s likely because selling pressure was greater than in the previous two instances, which probably indicates a larger hidden crisis yet to be uncovered. The market may only pause briefly before continuing its downward trend.
3. Wrong Bottom-Fishing: You bought at the bottom, and the broader market rebounded, but the rising sectors were those that hadn’t fallen, while the sectors where bottom-fishing occurred continue to decline.
This reasoning may not necessarily be correct, but if you repeatedly attempt it, sooner or later, you will lose all the profits you made previously.
To make the right decisions, one cannot think like the majority. This does not mean deliberately adopting “contrarian thinking,” which is still a form of mental laziness. Instead, your model should be as complex as possible within the limits of what you can manage.
03 How Investors Improve
The 'danger-food' recognition model is innate, while other recognition models require repeated practice and learning through trial and error.
Take the analysis of company value in value investing as an example:
Based on your experience with good companies in everyday life, you may easily establish a preliminary model when first entering the stock market: a good company = strong demand + high product market share + high gross profit margin.
All good companies are like this, but not all companies that fit this description are good ones, until you encounter a company whose brand and products are in decline, causing you significant losses.
If you do not give up but instead reflect deeply to identify problems in your recognition model, you will likely find answers in books about Buffett. A good company not only needs a high market share but also a moat; it requires not just a high gross margin, but more importantly, strong ROE and ROIC metrics.
Thus, you adjust your model, incorporating characteristics of good companies from Buffett’s perspective, updating your stock pool. However, you then lose money on an overvalued company, realizing that earnings can rise while the stock price falls.
Undeterred, you seek out various books on valuation methods, attempting to add valuation factors into your model. But soon, you encounter a 'value trap' company and suffer again due to overemphasizing valuation.
By now, your experience allows you to gradually strike a balance between fundamentals and valuation, even making profits several times. Just as you feel your recognition model has matured, trouble strikes again: a company with absolute competitive advantages, operating in a booming industry with reasonable valuation, passes every fundamental check without issue. Believing the market is wrong, you keep buying as the price drops, only to see it fall further.
After consulting with industry analysts, you begin to realize that your previous model lacked sufficient analysis of competitive dynamics. It is not enough to assess a company's absolute advantages; one must also consider the industry’s investment intensity, i.e., the capital cycle, as even the best companies cannot resist long-term downward trends.
Although you incurred losses, your model has improved. After this experience, it will be hard for you to lose substantial amounts again. However, you no longer feel as confident as before, realizing that no matter how much you improve, new challenges continue to emerge, such as:
1. Is macroeconomics really as you previously imagined—"studying it once more is a waste of time"?
2. Is the market really as you used to think, constantly making mistakes? Are those truly errors, or are they warnings of future risks?
3. Can you truly face market volatility without fear and wait for the rose of time to bloom? Or was Buffett’s success due to his method itself, or simply a case of massive survivorship bias?
4. Did you really earn alpha in the past? If the market no longer has beta, can you still make money?
……
After your model encounters consecutive losses, you always attempt to incorporate new factors or operational methods into the model. However, most of the time, either these additions fundamentally conflict with the model, or they make the results worse, forcing you to revert to the original approach.
This actually implies that your model has essentially stabilized—it can only be slightly adjusted, with little room for significant improvement. You must accept periodic ineffectiveness unless you are willing to start from scratch entirely.
Therefore, the significance of a model does not lie in being error-free but in establishing a stable profit-generating pattern. Different models excel at identifying different opportunities; some offer high win rates with low payouts, others low win rates with high payouts, some provide low returns with minimal volatility, while others do the opposite. However, generally speaking, the more complex the model, the more stable your excess returns.
04 Investors Have Their Own Weaknesses
The preceding text describes the development process of an opportunity identification model for evaluating a company's basic investment value, leading to the following two conclusions:
1. A genuinely effective identification model is always highly complex and possesses strong scalability.
2. Truly effective identification models will periodically become ineffective, keeping users motivated to pursue new attempts.
Although humanity has long since moved past the prehistoric era, the design of our brains still centers around the core functions of that time—efficient hunting and rapid escape. We instinctively use the fastest mental models, drawing the simplest conclusions for complex situations before us, substituting self-justifying imagination for rational and profound thought.
Ultimately, stock investment has a way of magnifying human weaknesses to an extreme degree—
Those who think too superficially need not be mentioned;
People who always strive to fully understand events forget that investment decisions are made under conditions of incomplete information, often leading them to miss opportunities;
Those with overly strong logical abilities lack the capacity for self-correction—if they start off on the wrong path, their ability to rationalize may deceive themselves;
Those relying too much on intuition lack the ability to upgrade systematically, unable to establish a stable investment methodology;
Individuals overly sensitive to change often lack the willpower to seize and hold onto opportunities;
Patient individuals may inadvertently persist in small errors until they grow into major ones;
Thus, the significance of a model does not lie in being error-free, but rather in keeping one's weaknesses within an acceptable range, thereby stabilizing profitability.
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Editor/KOKO