Systematic Investment Framework: Certainty vs High-Odds Asset Allocation

• Certainty (Predictability)
  : Refers to fundamental/macro judgments whose investment logic can be repeatedly verified, such as stable industry fundamentals, predictable cash flow, and clear policy environment. It is suitable as the core position of a portfolio.
• High-Odds (Asymmetry)
  : Refers to opportunities with high uncertainty but potential returns far exceeding risks, usually investment windows brought by early theme evolution, excessive market panic, or technological breakthroughs.
• They Are Not Opposites
  : Certainty provides downside protection, while high-odds bring excess returns; the key to an excellent systematic framework lies in dynamic allocation rather than choosing one over the other through quantitative stratification and capital control.

1. Cognitive System and Investment Philosophy
   (e.g., “10/40/50” Model)
   • 10% Stock Selection
     : Focus on stocks with “high certainty + reasonable valuation” and build a core stock pool through factor scoring (earnings quality, ROIC, cash flow, leverage).
   • 40% Betting Strategy and Capital Management
     :
     • Set strategy weight allocation (certainty strategies vs. high-odds strategies).
     • Introduce “risk budget” and “maximum drawdown tolerance”, such as incorporating daily/weekly/monthly fluctuations into dynamic position adjustments.
     • Use quantile risk control (e.g., VaR/ES) and liquidity red lines to ensure the system responds to extreme events.
   • 50% Psychology
     : Ensure strategy execution through institutionalized decision-making processes, review mechanisms, and behavioral bias reminders (avoid chasing ups and downs, overconfidence).

2. “System Guardian” Architecture Integrating Quant and Subjectivity
   • Quant Module
     : Use factor regression, sentiment indicators, and macro indicators to build signals; estimate “certainty scores” through multi-factor models and iterate with Bayesian updates.
   • Subjective Judgment “Correction”
     : Introduce subjective judgment during irrational phases (e.g., extreme panic or bubbles), but must have clear “trigger conditions”, “time window”, and “exit mechanism” to avoid emotional driving.
   • AI Assistance
     : Use AI to generate insights such as industry public opinion, market structure changes, and supply chain risks, but humans still set boundaries and make final judgments.

3. “Dumbbell Strategy” + Dynamic Balance Mechanism
   • “Dumbbell” Structure
     : One end consists of low-risk, high-certainty assets/strategies (e.g., high ROE leaders, structural industry ETFs), and the other end consists of high-odds events (e.g., AI new materials, specific policy catalysts).
   • Dynamic Balance Methods
     :
     • Rebalancing Frequency
       : Set fixed cycles (e.g., monthly/quarterly) and trigger conditions (e.g., factor extremes, volatility increases) to drive position adjustments.
     • Risk Budget Transfer
       : For example, when the “certainty” component retracts or risk signals rise, automatically reduce high-odds positions and increase defensive positions, and vice versa.
     • Capital Management Tools
       : Use options/bonds for hedging, stop-profit/stop-loss strategies beyond thresholds, and tail risk protection (e.g., volatility derivatives).

• Emotion and Cognitive Introspection
  : Treat the portfolio as a “system guardian”. After each major loss, immediately review with data/discipline to prevent returning to “individual stock fundamentalism” thinking.
• Event-Driven and Structural Themes Go Hand in Hand
  : Continuously monitor technological changes (e.g., AI substitution), policy dividends, and maintain underlying “certainty” assets (e.g., consumer leaders, high-quality finance) as defense.
• Technology and Psychology Parallel
  : Use AI to assist risk control models (volatility warnings, liquidity monitoring) and combine team psychological training (e.g., stress tests, decision simulations) to ensure “unity of knowledge and action”.

If you need to further quantify the construction of a “certainty scoring mechanism” or simulate the risk-return path of the “dumbbell strategy”, you can enable the

to access more factor data, A/B strategy comparisons, and chart visualizations to help optimize the overall framework.

The core of a systematic investment framework lies in

, while achieving steady portfolio growth through dynamic transfer between “high certainty” and “high odds” positions. After learning from painful lessons, if you can structure your cognition and integrate psychology/discipline into the system, you can guard your assets and capture excess returns in the AI era with a “unity of knowledge and action” attitude.