second-level-thinking: Скилл анализа инвестиций

--- name: second-level-thinking description: > Apply Howard Marks' Second Level Thinking framework to investment decisions. Use this skill whenever the user is analyzing an investment opportunity, evaluating a trade thesis, stress-testing a conviction, or asking whether a stock/asset/market is actually as attractive as it looks. Also trigger when the user wants to challenge their own reasoning ("am I just following the crowd?"), wants to identify what the market is mispricing, is debating whether a consensus view is already fully reflected in price, or asks about risk/reward asymmetry, market cycles, or contrarian positioning. The skill channels Marks' philosophy: superior returns require being different AND right — and that starts with understanding what everyone already believes. --- # Second Level Thinking — Howard Marks Framework The market is a discounting machine. Outperformance comes from being *right about something the market is wrong about*. Second-level thinking asks: **What does the current price imply? Is that belief justified? And what is everyone missing?** ## Research First Do the work before the framework. Assertions without data are opinions. **Search for**: SEC filings (10-K, 10-Q), earnings transcripts, capex disclosures, ROIC trends, interconnection queue data (FERC/EIA), fab lead times, labor market stats (BLS), and comparable historical cycles (telecom 1990s, shale, cloud infrastructure). Cite sources. When data is unavailable, say so — that's more valuable than a fabricated number. --- ## The Seven Stages ### 1 — Decode the Consensus Reverse-engineer the price. If the current valuation is rational, what growth, margin, and terminal assumptions must hold? Back it with data: consensus EPS, analyst targets, implied revenue growth. Identify prevailing sentiment — crowded long or unloved? ### 2 — The Second-Level Challenge Interrogate the consensus through three lenses: - **Information asymmetry**: Data or channel checks the market hasn't weighted correctly - **Analytical asymmetry**: Different unit economics, non-consensus moat view, misunderstood costs - **Behavioral asymmetry**: Extrapolation bias, loss aversion, narrative capture, neglect, recency For each: is this a real edge, or a story the investor tells themselves? ### 3 — Supply/Demand Economics The stage most analyses skip. Demand can be real and the investment still bad if the market ignores what it costs to supply that demand. **Demand reality check**: Validate TAM bottom-up (unit economics × customers, not "X% of $Y trillion"). Find S-curve penetration data. Check pricing power under customer concentration. Assess substitution timeline — the consensus systematically underestimates arrival speed. **Supply-side bottlenecks**: The market prices revenue without pricing the friction to produce it. - *Capex intensity*: Get capex-to-revenue ratios from 10-K filings. What's the incremental capex per $1B of new revenue? Is it rising? - *Physical lead times*: Power interconnection queues (3-7 years, per FERC data), fab construction (3-5 years, $10-20B+), warehouse/logistics timelines. Find the actual queue data. - *Human capital*: Specialized talent (AI researchers, power engineers, fab technicians) doesn't scale on demand. Compare historical hiring rates to growth plan requirements. - *Supply chain*: Single-source dependencies, geopolitical concentration, regulatory queues create hard growth ceilings. The question isn't whether growth is possible — it's *how long it takes* and *what it costs*. A five-year buildout priced as a two-year story is a valuation risk. **Diminishing marginal returns**: Pull ROIC/ROIIC trends over 3-5 years. Is ROIIC declining? Compare ROIC to cost of capital — growth that earns below WACC destroys value. Watch for the "crowding in" dynamic: more capital chasing the same resources drives up input costs and erodes margins. Frame as: "ROIIC declined from X% to Y%, suggesting the next investment phase generates lower returns than priced in." ### 4 — Risk Asymmetry Map the full probability distribution, not just upside/downside: - **Bull / Base / Bear cases** with explicit probability weights - Feed supply-side findings from Stage 3 into scenarios — "capex overrun + timeline delay" is a more credible bear case than generic "things go wrong" - Use historical base rates for megaproject cost/schedule overruns (Flyvbjerg's database, McKinsey) **The Marks question**: Is the ratio of potential gain to potential loss, weighted by probability, actually attractive? More upside than downside in dollar terms can still be a bad bet if the bear case is probable or catastrophic. ### 5 — Cycle Positioning Where are we in the macro/credit cycle? This determines starting price and error-correction time. - Late-cycle (expensive, tight spreads, euphoria) vs. early-cycle (cheap, stressed, fear) - Marks' pendulum: greed end (play defense) or fear end (get aggressive) - Capital abundance compresses expected returns; scarcity creates opportunities - How does the cycle affect *this specific thesis*? ### 6 — The Structural Edge Test The hardest question: **Why do you have an edge here?** Three real edges exist: informational (you know something legal the market doesn't), analytical (you've modeled it better), behavioral (you can stay rational when others can't). If the honest answer is "no clear edge" — don't expect outperformance. ### 7 — The Verdict Synthesize into a clear conclusion: - **Consensus view**: One sentence - **Second-level view**: What the market gets wrong and why - **Supply/demand finding**: The key physical or economic friction being underweighted - **Edge**: Informational / analytical / behavioral — specific - **Risk/reward**: Probability-weighted, grounded in Stage 3 scenarios - **Cycle context**: How conditions affect required margin of safety - **Conviction**: High / Medium / Low — and what moves it - **Thesis-breakers**: Key variables to monitor --- ## Output Format Structured analysis across all seven stages. Use numbers, cite sources, name biases explicitly. No "on one hand / on the other hand" hedging. Channel Marks: skeptical, rigorous, honest about uncertainty. If the user hasn't shared enough, ask one focused question before proceeding. --- ## Failure Modes (First-Level Thinking in Disguise) - **"Obviously undervalued"** — If obvious, it's already priced in - **Quality ≠ investability** — Great business at terrible price = terrible investment - **Demand ≠ returns** — A $100B market can produce sub-WACC returns if capex is too high - **Flat ROIC projection** — Projecting today's returns on tomorrow's larger capital base without evidence returns won't compress - **"Temporary" constraints** — Power grids need 10-year cycles, talent pools are genuinely thin, permit queues aren't shrinking. Test with data before accepting the "temporary" framing - **Asserting without citing** — All quantitative claims need a specific source - **Ignoring the cycle** — No thesis exists in a vacuum - **Symmetric framing** — "50/50 upside/downside" without probability weighting isn't analysis