Chapter 5 Profitability: How Much of the Pie the Industry’s Leading Companies Can Get
Core Point: The Profit-Distribution Logic of a Mature-Stage Industry
When an industry enters the mature stage and has not found a second growth curve, there are two outcomes: one, it is replaced and enters the decline stage, losing its investment value; two, by virtue of a wide moat it enters “stable-market cyclicalization” and becomes a cyclical industry (the pie can no longer grow, and it only fluctuates with the macro environment). At this point, the core of investing is “careful selection,” focusing on the most profitable links in the industry chain and the leading companies.
Core Analytical Dimension: Competitive Landscape (Horizontal + Vertical)
The competitive landscape determines a company’s profitability level and is divided into two dimensions, horizontal and vertical. Only when both “horizontal competition is not intense” and “vertical bargaining has an advantage” are satisfied can an industry’s companies achieve relatively good profitability.
1. Horizontal Landscape: Distributing the Pie Among Peers
The core indicator is industry concentration, whose pattern of change is related to external factors (economy, policy) and the industry’s own attributes (life cycle, ceiling); after entering “stable-market cyclicalization,” it is mainly related to the capacity cycle.
Key knowledge points:
Capacity and capacity utilization: capacity is a manufacturer’s maximum output; capacity utilization = actual output ÷ maximum capacity, reflecting the efficiency of capacity use.
The four stages of the capacity cycle: demand exceeds supply → capacity surges → capacity clears → landscape improves.
Judging the industry cycle: bottom (leading companies cut production, players exit, analysts don’t recommend); top (leading companies expand capacity, outside players pour in, analysts upgrade ratings).
Exceptions: must be combined with the industry life cycle, and leaders may expand against the trend or proactively start a price war.
2. Vertical Landscape: Distributing the Pie Along the Industry Chain
The industry chain is the upstream-downstream structure formed by industry sectors based on technological and economic linkages; the core of profit distribution depends on “willingness to bargain” and “bargaining power.”
Key knowledge points:
Willingness to bargain: depends on two factors—the value share of the good/service in the downstream, and the industry’s current cycle stage.
Bargaining power: mainly looks at each link’s industry concentration and supply-demand conditions; can be judged by “each link’s gross margin” and “the ability to tie up upstream/downstream funds.”
Chapter 6 Valuation: Valuation Characteristics at Different Industry Stages
Core Premise: The Link Between Valuation and Fundamentals
The four fundamental dimensions sorted out in the first four chapters (feasibility, scalability, defensiveness, profitability) are the basis of valuation, and valuation must be judged comprehensively in light of fundamentals; stock return = dividend income + capital gains, and capital gains = earnings growth + valuation change.
Core Concepts
Earnings per share (EPS): net profit ÷ number of shares, reflecting a company’s profitability level.
Price-to-earnings ratio (P/E): share price ÷ earnings per share (or total market value ÷ net profit), which can be simply understood as “the payback period assuming net profit stays constant.”
Valuation Characteristics at Each Stage of the Industry Life Cycle
Introduction stage: valuation revolves around “dreams” and “stories,” focusing on potential growth space, often using VC valuation methods.
Early growth: performance grows at high speed and consistently beats expectations, valuation rises rapidly, but there is a risk of crowded trading, and valuation can reach 80-120 times.
Late growth: performance growth slows, high valuation becomes hard to sustain, and valuation corrections easily occur.
Maturity: penetration-growth slows, and overall valuation contracts; some leading companies see valuation rise due to an improved competitive landscape (i.e., “leader advancement”), often using cyclical-stock valuation logic.
Decline: three cases—carving out a second growth curve (revalued as the new industry’s introduction stage); stable-market cyclicalization (valuation center references mature markets); being hit by substitutes (profit margin falls, valuation keeps declining; if supply contracts faster than demand, there may be a “last glow” type of valuation spike).
Core Factors Influencing Valuation
From the investor’s angle: focus on “ability (whether there’s a lot of money, influenced by monetary policy)” and “willingness (risk appetite).”
From the asset’s angle: focus on “odds (scalability, profitability)” and “probability (feasibility, defensiveness).”
Chapter 7 External Factors: Which Fuse Does the Industry Still Lack to Ignite
Core logic: external factors keep changing, so one must track the industry with a dynamic eye and iterate the analysis; the four major external factors (political, economic, social-cultural, technological) directly affect an industry’s fundamentals and valuation.
The Four Major External Factors in Detail
1. Political Factors
Domestic: the core is industrial policy, divided into supportive (verbal encouragement, subsidies and tax cuts, direct investment, mandatory use) and restrictive (restricting access, supply, price, audience), such as supply restrictions on highly polluting industries and tax-preference policies for some industries.
International: the ones with greater impact on our country are U.S. monetary policy and foreign policy; deterioration in international relations is often linked to slowing economic growth and contradictions in pie distribution.
2. Economic Factors
Economic cycle: most industries’ profits are related to the economic cycle, with the degree of correlation ranked: finance > cyclical > consumer > growth; staple consumer goods (food and beverages, condiments) are little affected by the cycle and are “inflation-sensitive.”
Level of economic development: affects industrial structure and demand—for example, economic development drives consumption upgrading or downgrading.
Price of money: affects all industries through the three dimensions of money’s purchasing power, interest rates, and exchange rates.
3. Social-Cultural Factors
Demographic characteristics: the core is population aging (by the end of the “14th Five-Year Plan” the population over 60 will exceed 300 million, and by 2035 exceed 400 million), driving demand for medicine and the automation process; the reduction in labor supply spawns demand for industrial automation, while our country’s “engineer dividend” becomes a core advantage of manufacturing.
Cultural change: shifts in consumption concepts, such as rising health demand; the share of traditional soft-drink categories (carbonated, juice) is eroded by packaged water and sugar-free drinks; light-meal restaurants increase.
4. Technological Factors
Technology is the most far-reaching driving force, with three core roles:
Driving innovation stratification: from the bottom layer (infrastructure, hardware) to the top layer (applications, ecosystem participants); the bottom leans toward technological innovation, the top toward business-model innovation.
Reducing costs and increasing efficiency: driving new categories to replace old ones.
Creating new channels: e.g., mobile internet drove e-commerce development and helped small home-appliance industries rise.
Supplement: one can watch global leading companies’ capital expenditure and innovation directions; if several leaders focus on a certain direction, it shows that the technology has won industry recognition.
Chapter 8 Prosperity: How to Foresee Earnings Growth
Core Definition
High prosperity: the industry’s revenue and profit grow fast; low prosperity: growth is slow. The core of tracking prosperity is “using real-time data to verify hypotheses,” helping to predict earnings trends in advance (ahead of financial reports).
Core Requirements for Prosperity Indicators
Reflect fundamentals: can be directly tied to the industry’s performance.
High update frequency: can be updated monthly, weekly, or daily to ensure timeliness.
Tracking Focus for Different Industries
Real estate industry: its industry chain is long and wide, with both upstream (construction machinery, minerals) and downstream (home appliances, furniture) driven by it; the tracking focus is the transmission of the industry’s overall prosperity to upstream and downstream.
Upstream raw-material industries: strongly cyclical, focusing on “price” (which determines profitability) and “inventory” (which reflects supply-demand conditions and how long the price lasts), e.g., a rise in polysilicon prices directly improves industry profits.
Staple consumer industries: rigid demand, e.g., condiments can follow CPI to raise prices; focus on price changes and the stability of consumer demand.
Post-real-estate-cycle industries (home appliances, furniture, autos): demand lags the real estate industry, so one needs to track the transmission rhythm of the real estate industry’s prosperity.
Key Reminder
Tracking indicators requires understanding the logic behind them; one must not “carve a mark on the boat to find the sword.” The advantage of subjective investing is “understanding the logic,” not simply relying on indicators.
Chapter 9 Case Study: A Research Framework for New Energy Vehicles
1. Positioning in the Industry Life Cycle: High-Speed Growth Stage
Core basis for judgment: new-car penetration (not overall ownership penetration), which stabilized above 30% after September 2022 and kept rising in 2023, indicating that the business model has been validated and the industry is in the growth stage (auto replacement cycles are long, and overall ownership penetration is still low).
2. Business Model: Feasible and Innovative
Mainstream model: upstream (lithium mines) → midstream (batteries) → downstream (complete vehicles + in-vehicle value-added services); the essence is “selling goods + selling services,” satisfying the traditional demand for safe travel (replacing fuel vehicles).
Feasibility verification: sales feasibility (large demand elasticity, high frequency); profitability feasibility (high production standardization, can reference listed companies’ financial reports; some companies are still losing money, but there is room for improvement long-term).
Innovative model: the battery-swap model (win-win for multiple parties, solving the problems that fast charging harms batteries and charging is slow, e.g., CATL’s battery-swap stations).
3. Market Size Estimation
Core formula: total new-energy passenger vehicle sales = total passenger vehicle sales × new-energy vehicle penetration; where total passenger vehicle sales = total passenger vehicle ownership × average annual stock replacement rate (conservative estimate, focusing on stock-replacement demand).
4. Moat and Competitive Landscape
Moat: traditional fuel vehicles cannot build a moat against new-energy vehicles; within the new-energy vehicle core industry chain (lithium mines → battery materials → batteries → complete vehicles), the mining and recycling, separator, and electrolyte links have relatively high gross margins.
Bargaining power: in the growth stage, complete-vehicle manufacturers have weak willingness to bargain (they’re grabbing market share); later, as growth slows, they will gradually bargain with upstream and downstream.
Horizontal competition: in the late growth stage, one needs to watch leaders’ price wars (eliminating small manufacturers, similar to the air-conditioner industry) and the iteration of new models (new models contribute 35%-47% of sales, far higher than fuel vehicles).
5. Valuation and Risk Warnings
Valuation characteristics: high valuation in the growth stage is normal; the core focus is “trading crowdedness” and “leaders’ price wars.”
Trading crowdedness: seven indicators (trading volume, turnover rate, etc.) combined with equal weights; exceeding the mean + 1.5 standard deviations easily triggers a correction, while below the mean - 1.5 standard deviations with good prosperity is a buy point.
Other influencing factors: oil price fluctuations (rising oil prices benefit new-energy vehicles), consumption preferences (younger consumers more recognize new-energy vehicles’ configurations and direct-sales model), policy support (penetration changes in license-plate-restricted cities), technological progress (solving range and charging issues).
Chapter 10 Research Foundations: Thoughts on the Essence of Research
1. The Essence of Research
The essence of research is the process of “input–processing–output” of information, divided into primary research (experiments, analysis, creating new knowledge) and secondary research (summarizing existing knowledge, presented in new forms); at its core it cannot escape “information.”
2. The Core of Research Creating Wealth: Information Gaps
Research generates profit by creating three kinds of information gaps, with the core goal being “depth gaps”:
Time gaps: obtaining accurate primary information faster than others (scarce, a matter of chance rather than seeking); institutions can gain an edge via crawlers, remote sensing, and offline research (e.g., Muddy Waters shorting Luckin) to obtain “alternative data.”
Breadth gaps: a broader knowledge base (e.g., Charlie Munger’s multidisciplinary mental models); not advisable to pursue breadth first, as one easily falls into “fear of missing out”—one should first go deep in one field, then expand breadth.
Depth gaps: understanding the supply side (industry, company) or demand side (users, market) better than others, which is the core of lasting profit; can be built through deep industry work and field research.
3. Classification of Information
(1) By Depth (the DIKW Model)
Data: raw collected information (e.g., a week’s clock-in times), at the Know-what level.
Information: simple analysis of data (e.g., being on average 15 minutes late each day), at the Know-what level.
Knowledge: distilled action guidance (e.g., needing to get up earlier), at the Know-how level.
Wisdom: distilled underlying logic (e.g., being late stems from insufficient self-discipline), at the Know-why level.
(2) By Breadth
Macro information: macroeconomy, policy, sector rotation, capital preferences, etc.
Meso information: industry-level data and dynamics.
Micro information: company- and product-level information.
(3) By Nature
Factual judgments: objectively existing facts (e.g., “Country A’s tax burden is higher than Country B’s”), with a single truth.
Value judgments: judgments about the good/bad or usefulness of people, things, and events (varying by person, changing dynamically); high-quality value judgments need factual judgments as support.
Chapter 11 Advanced Research: A McKinsey Analyst’s “Information Cooking Method”
Core logic: to solve the two major difficulties of information input (information overload, polarized views), use the three steps “prepping (input) - cooking (processing) - plating (output)” to process information efficiently and convey core views.
1. Prepping: Information Input (Solving the Two Difficulties)
Difficulty 1: Information Overload
Solution: the hypothesis-driven method (the underlying methodology), in three steps:
Step 1: Make a hypothesis (e.g., when researching “consumption downgrading,” hypothesize that “consumption downgrading stems from not having enough money to spend or a shift in consumption willingness”).
Step 2: Break down the hypothesis (following the MECE principle, mutually exclusive and collectively exhaustive, e.g., “not enough money = earning less + more other expenses”); if lacking understanding, first find review materials or talk with industry insiders.
Step 3: Verify the hypothesis (even if the hypothesis is disproven, one can still reach effective conclusions).
Difficulty 2: Polarized Views (Information Cocoon, Confirmation Bias)
Reasons for polarized views: macro (economic downturn intensifies contradictions), meso (algorithms cause information cocoons), micro (extreme views harvest traffic).
Solutions:
Step 1: Broaden input channels (turn off personalized recommendations, follow media with different views, try to falsify).
Step 2: Do a good job of information selection (distinguish factual judgments from value judgments; don’t blindly negate value judgments, but verify the factual basis behind them).
Step 3: Rationally weight information (avoid confirmation bias, use System 2 for rational thinking, assign reasonable weights to different information, don’t “favor one over another”).
2. Cooking: Information Processing (Distilling Core Views)
Core method: the Pyramid Principle (systematically organizing information to improve logic), in four steps:
Step 1: List the information (complete sentences, not just keywords; can be recorded on sticky notes or in Excel).
Step 2: Look for patterns (distill commonalities, group and categorize, e.g., dividing a company’s cash flow into financing, investing, and operating).
Step 3: Distill key points (distill one core point for each group of information).
Step 4: Summarize the core (distill the highest-level core view, reflecting insightfulness, distinct from a simple summary).
Requirements of the Pyramid Principle: ① depth (distill insights, create extra value); ② logic (use inductive or deductive reasoning; after grouping, arrange by order such as time and rank).
3. Plating: Information Output (Conveying Core Views)
Core goal: grab the audience’s attention and convey views efficiently; the narrative structure must be adjusted according to the audience type. The core framework is SCQR (Situation-Conflict-Question-Resolution), corresponding to the Chinese “empty-rain-umbrella” model (empty = situation, rain = conflict, wet = question, umbrella = resolution).
Adjusting the narrative structure for different audiences:
Passersby: CQSR (conflict and question first, to grab attention, then situation and resolution).
General audience (with some understanding): SCQR (the classic order).
Big boss (time is precious, focuses on results): RSCQ (resolution first, then fill in details).
Direct supervisor (understands the situation, focuses on solutions): directly output the core resolution.
Chapter 12 Research Toolbox: Commonly Used Research Information Sources
Core information source (mentioned in the book): Luobo Investment Research (under Tonglian Data, providing a massive industry database).
Supplement: combined with the earlier text, commonly used information sources also include government departments (Ministry of Commerce, Ministry of Agriculture and Rural Affairs), industry associations (Passenger Car Association, CAAM), research institutions (CRIC, China Index Academy), corporate financial reports, offline research data, etc.
Chapter 13 The New Era of Research: How to Conduct Research with the Help of Generative AI
1. The Impact of Generative AI on Research
Disruptive change: greatly improved efficiency (several days of work can be compressed to a dozen-odd minutes), e.g., OpenAI’s Deep Research feature can directly generate in-depth reports.
Industry impact: triggering waves of layoffs (e.g., BuzzFeed, Stack Overflow, Duolingo layoffs), while creating new positions (AI model safety experts, AI transformation consultants, etc.); those who don’t embrace AI are easily eliminated.
Practical applications: in finance (Morgan Stanley fine-tuned an AI assistant for querying data, summarizing conversations, and drafting emails), market-size estimation (AI’s ability is no less than a McKinsey interviewer of fresh graduates).
2. The Limitations of Large Language Models and How to Respond
Limitation 1: answers are not stable (different answers on repeated questions); response: when needing high-precision answers, set the temperature to around 0.1 (reducing randomness).
Limitation 2: lacks deep reasoning ability (only generates text based on probability, doesn’t understand the world); response: don’t rely on AI for deep decisions, use it only as an auxiliary tool.
Limitation 3: inevitably “gets dumber” (its knowledge base stays at a past training point in time, and updating is costly); response: supplement AI answers with the latest information, and don’t blindly trust AI’s time-sensitive content.
3. Core Reminder
No need to obsess over specific AI tools and their usage (they update and iterate quickly); focus on AI’s auxiliary role and thinking approach in research—embrace change and keep learning.
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