Balancing Stock Selection Ability & System Discipline: Post-Loss Investment System Reconstruction

Based on the background information you provided and the latest professional research, I systematically organize the balance between “stock selection ability” and “system discipline” for you, as well as actionable paths to rebuild the investment system after significant losses.

1. Transformation Framework from “Stock Fundamentalist” to “System Guardian”

• Weight and positioning of stock selection: In your cognitive framework, stock selection accounts for approximately 10%. This does not mean stock selection is unimportant, but rather acknowledges its “ceiling effect”. The systematic approach is to standardize individual stock research (assessment processes for quality, financial discipline, intrinsic value, signals, and confidence), and use platform-based and data-driven methods to improve the “win rate” and “replicability” of stock selection, instead of relying on single judgments or inspiration [1][2].
• Amplification effect of system discipline: Capital management (about 40%) and psychological construction (about 50%) determine the “floor” and “sustainability” of returns. Systematic investment platforms convert individual judgments into collective wisdom of the platform through unified research frameworks, risk management, and data platform operations, improving efficiency and reducing non-systematic risks [1]. In practice, quantitative models iterated and dynamically adjusted over 20 years show that systematization can effectively avoid emotional noise and maintain stable output in volatile environments [2].

2. Steps and Methods to Rebuild the Investment System After Significant Losses

• Psychological Reconstruction (Priority: 50%)

  1. Acknowledgment and review: Objectively record loss-triggering factors (valuation, position, industry/style, macro events, psychological factors, etc.), and distinguish between controllable and uncontrollable factors.
  2. Visualization and dataization: Conduct structured reviews of historical transactions, quantify retracement paths and decision nodes, and convert emotional shocks into inspectable data issues [2].
  3. Rebuilding trust and layered expectations: Adhere to the concept of “slow is fast”, set achievable small goals and phased assessments, and gradually rebuild trust in the system [3].

• Systematization of Capital Management (Priority: 40%)

  1. Multi-asset and multi-strategy: Improve the floor of returns and smooth fluctuations through cyclical rotation of different assets and strategies [3].
  2. Risk budget and position rules: Set concentration limits for industries/individual stocks/styles, dynamic stop-loss and position adjustment rules; use models such as risk parity for dynamic rebalancing, converting “patience” into executable discipline [3].
  3. Strict backtesting and scenario testing: Incorporate extreme scenarios (such as the sharp retracement of some targets from 2022 to 2024) into backtesting to ensure that new strategies can still hold the risk bottom line in stressful environments.

3. Synergy Mechanism Between Stock Selection Ability and System Discipline

• Three Red Lines for Hierarchical Decision-Making: First layer: risk budget and position rules (hard constraints); Second layer: quantitative scoring and signal thresholds (soft constraints); Third layer: exception management for subjective judgments (limited use only when fully supported by data). The superposition of the three layers ensures system stability while retaining necessary flexibility.
• Dynamic Feedback and Model Iteration: Track factor performance and portfolio attribution daily/weekly, continuously optimize factor weights, thresholds, and rebalancing frequency, forming a closed loop of “research—execution—evaluation—optimization” [1][2].
• Long-Term Perspective and Value Anchors: Adhere to fundamentals as the core, use free cash flow, moats, and safety margins as valuation cornerstones, maintain a long-term perspective on market noise, and implement the concept of compound interest at every decision node [4][5].

4. Practical Recommendations and Checklists (Immediately Implementable)

• Capital and Position Checklist:
  1. Is the upper limit of single asset/strategy weight met?
  2. Is the industry and style concentration within the target range?
  3. Are dynamic stop-loss/position adjustment rules written and automatically executed?
• Stock Selection Process Checklist:
  1. Are DCF parameters conservative and consistent (growth rate, discount rate, terminal multiple)?
  2. Is there support from the three elements: cash flow, moat, and safety margin?
  3. Are the scorecard thresholds and signal generation logic transparent and traceable?
• Psychological and Governance Checklist:
  1. Has loss attribution and review formed reusable templates and databases?
  2. Are decision logs and emotional records included in weekly/monthly reviews?
  3. Do team and platform processes implement a unified research framework and risk governance?

5. Differentiated Competitiveness in the AI Era

• AI Enhancement Rather Than Replacement: Use AI and quantitative tools to analyze massive text and alternative data, aggregate signals and empower decisions, improve breadth and win rate, but keep the final rules and boundaries within the framework of human value systems and risk preferences [1][2].
• Balance Between Discipline and Innovation: Under the premise of adhering to free cash flow discounting and long-termism, introduce factor and signal innovations, iterate models in a data-driven way, and make “innovation” part of the system rather than a one-time subversion of the system [5].
• Long-Term Stable Vision: With the simple goal of “getting rich slowly”, convert multi-asset, multi-strategy, and systematic discipline into a replicable long-term return path [3].

References
[1] Jinling API Data
[2] Yahoo Finance Hong Kong - Top AI Quantitative Model Revealed: Using Systematization to Overcome Uncertainty! (https://hk.finance.yahoo.com/news/頂尖ai量化模型首曬-用系統化戰勝不確定性-020003017.html)
[3] BlackRock China - Fan Hua: Integrating Eastern and Western Wisdom to Build a Systematic Investment Platform with Distinctive Features (https://www.blackrock.com.cn/contents/2025/9/12-7f7deb0f4f4b4575b50edb2190376aae.html)
[4] Securities Times - Youmeili Investment: Be a “Longevity Star”, Not a Star! Revealing Stable Practices Through Cycles (http://stcn.com/article/detail/2633231.html)
[5] Global Views Monthly - How to “Be Greedy in Fear”? Insights into Buffett’s Investment Wisdom from the 2008 Financial Crisis (https://www.gvm.com.tw/article/126683)