How Do Fund Managers Build a 'Barbell' Investment System That Survives Bull and Bear Markets? — From Sunac's Default to Risk Control Evolution Toward Sharpe Ratio >1.0

Based on search results and professional financial analysis frameworks [1,2,3], I will systematically analyze the construction logic and practical application of the ‘Barbell’ investment system for you.

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The case of Sunac China (01918.HK) is a typical example of risk exposure in China’s real estate industry in the 2020s. According to the latest data, Sunac experienced:

• 2021
  : Cash flow crisis began to emerge
• 2022
  : Public debt default, officially falling into liquidity distress
• November 2023
  : First offshore debt restructuring (billion-dollar scale)
• December 2025
  : Completed full domestic and offshore debt restructuring, reducing debt by approximately 60 billion yuan [2,3]

1. Industry Misjudgment
   : Overly bet on the upward real estate cycle, ignoring policy shift signals (“Three Red Lines” policy)
2. Concentration Risk
   : Overweighting a single industry, lacking diversified allocation
3. Missing Liquidity Management
   : High-leverage model quickly failed during credit contraction
4. Insufficient Fundamental Research
   : Inadequate understanding of the enterprise’s true debt structure and cash flow status

: Investment cannot rely solely on macro judgments or industry beta; it must establish a and .

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The ‘Barbell Strategy’ is a classic portfolio management method whose core idea is

:

Configuration Structure Schematic:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
High Odds Offensive End              Middle Gap              High Certainty Defensive End
(15%-25%)                                   (75%-85%)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• Option Buyer           No allocation to mediocre assets        • High Dividend Stocks
• Deep Contango Index Futures                             • Graded A Merger Redemption
• Event Arbitrage                                  • IC Contango Arbitrage
• Small-Cap High-Growth Stocks                              • Cash Equivalents
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Assume portfolio return is R and risk is σ:

• Return Side
  : E[R] = w₁·R₁ + w₂·R₂ (weighted return of both ends)
• Risk Side
  : σ² = w₁²·σ₁² + w₂²·σ₂² + 2w₁w₂ρ₁₂σ₁σ₂

The key here is controlling overall volatility through

.

1. Convex Return
   : One end provides extreme return potential, the other provides a safety cushion
2. Nonlinear Hedging
   : Avoid systemic risk of linearly correlated assets
3. Liquidity Management
   : Defensive end provides liquidity to handle extreme situations

According to practical cases shown in search results [1]:

• Offensive End (15%)
  : Allocate deep out-of-the-money options, loss locked at 3%, profit potential over 300%
• Defensive End (85%)
  : High-certainty cash cows (Graded A, index futures contango arbitrage)
• Pulse Risk Control
  : Three-dimensional protection (stop-loss line, position management, stress testing)

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The fund manager mentioned by the user experienced an important evolution in investment philosophy, reflected in the redistribution of cognitive weights:

Dimension	Weight for Traditional Stock Pickers	Weight for System Guardians	Core Competency Requirements
Stock Selection Ability	70%	10%	Fundamental research, valuation modeling
Capital Management	20%	40%	Position control, rebalancing, liquidity management
Psychological Control	10%	50%	Emotion management, cognitive bias correction, decision discipline

• Pursue Alpha returns, rely on individual stock selection ability
• Ignore systemic risk exposure
• Overconfidence leads to concentrated positions

• Treat investment as a probability game, not a prediction game
• Establish an ‘antifragile’ mechanism (profit from black swans)
• Limit human weaknesses through quantitative rules

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In traditional active investment, investors have 100% decision-making power, but this is precisely the biggest source of risk:

Decision-Making Delegation Model:
┌─────────────────────────────────────────────────┐
│        Full Investment Decision-Making Process   │
├─────────────┬───────────────────────────────────┤
│   Human Decision    │          Systematic Decision               │
├─────────────┼───────────────────────────────────┤
│ ✗ Emotional     │ ✓ Rule-Driven                        │
│ ✗ Inconsistent     │ ✓ Repeatable                          │
│ ✗ Hard to Replay   │ ✓ Traceable                          │
│ ✗ Affected by Cognitive Bias │ ✓ Statistical Advantage                        │
│   Influence       │                                   │
└─────────────┴───────────────────────────────────┘

Decision-Making Delegation:
- 30% Quantitative Strategy (Automatic Execution)
- 40% Risk Control Rules (Mandatory Constraints)
- 30% Human Judgment (Only Used Outside the System)

1. Level 1 - Hard Rules
   (Automatic Execution):
   • Auto-reduce position when stop-loss line is triggered
   • Single asset weight cap (e.g., 10%)
   • Reduce leverage when portfolio volatility exceeds limit

2. Level 2 - Quantitative Signals
   (Semi-Automatic):
   • Technical indicator buy/sell signals
   • Volatility threshold warning
   • Correlation monitoring

3. Level 3 - Human Judgment
   (Restricted Permissions):
   • Only used in extreme situations not covered by the system
   • Requires double confirmation mechanism
   • Decisions need to be recorded and replayed

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Sharpe Ratio = (Rp - Rf) / σp

Where:
- Rp = Portfolio Return
- Rf = Risk-Free Rate
- σp = Portfolio Volatility (Standard Deviation)

Goal: Sharpe >1.0 means for every unit of risk taken,
you get >1 unit of excess return

According to search results, the fund manager achieved the following performance indicators:

• Sharpe Ratio
  : >1.0
• Maximum Drawdown
  : -13.3%

• Barbell strategy offensive end: Small positions in high-odds targets boost overall returns
• Defensive end cash flow: High-dividend, arbitrage strategies provide stable returns
• Dynamic adjustment: Adjust weights of both ends based on market conditions

• Negative correlation allocation: Low correlation between offensive and defensive ends
• Volatility management: Control overall portfolio volatility within 15%
• Tail risk hedging: Option protection to prevent extreme declines

• After adopting the barbell strategy in 2025:
  • Obtain stable returns from the defensive end (85%) (assuming 8%)
  • Obtain excess returns from the offensive end (15%) (assuming 20%)
  • Portfolio return: 0.85×8% +0.15×20% =9.8%
  • Assume risk-free rate is 3%, portfolio volatility is6.8%
  • Sharpe = (9.8% -3%)/6.8% =1.0 ✓

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: Spend 80% of energy on individual stock research
: Establish a reusable stock selection framework

:

• Fundamental filter: ROE, free cash flow, moat assessment
• Valuation model: DCF, relative valuation, PEG
• Risk exclusion mechanism: Debt ratio, cash flow断裂 risk, corporate governance

f* = (bp - q)/b

Where:
f* = Optimal position ratio
b = Odds (profit/loss ratio)
p = Win rate
q = Loss rate (1-p)

:

• If a strategy has a 60% win rate and 2:1 odds
• f* = (2×0.6 -0.4)/2 =40%
• Actual Application
  : Use 0.5×f* =20% position considering estimation error

:

• Time trigger: Adjust weights quarterly
• Amplitude trigger: Adjust when weight deviates from target by ±5%
• Volatility trigger: Reduce position when portfolio volatility exceeds limit

###3. Psychological Control (50%): The Hardest Part of Investment

:

Bias Type	Performance	Countermeasure Mechanism
Confirmation Bias	Only look at information supporting one’s own views	Pre-set falsification tests
Loss Aversion	Unwilling to stop loss when losing	Mandatory stop-loss rules
Anchoring Effect	Stick to purchase price	Focus on relative value
Overconfidence	Overestimate own prediction ability	Decision recording and replay
Herd Effect	Blindly follow the crowd	Independent thinking framework

:

1. Decision Log
   : Record logic, emotion, and result of each transaction
2. Regular Replay
   : Review decision quality monthly, not just results
3. Stress Testing
   : Assume response plans for extreme scenarios
4. Third-Party Perspective
   : How would you operate if it were someone else’s account?

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##7. Investment Competitiveness in the AI Era: Understanding Business Essence

In the AI era, the unique value of fund managers lies in:

• AI can process data, but it is difficult to understand the essence of business models
• Case: Sunac’s risk lies in the fragility of the ‘high leverage + pre-sale system’ model when policies shift
• This requires comprehensive judgment of business logic, policy environment, and industry cycle

• Management interviews, grassroots research, supply chain information
• These require human intuition and experience accumulation

• AI is trained on historical data and may fail in new crises
• Human crisis handling experience is crucial in extreme situations

┌─────────────────────────────────────────┐
│        AI-Enhanced Decision Framework   │
├─────────────────┬───────────────────────┤
│   AI Excels At        │    Humans Excel At            │
├─────────────────┼───────────────────────┤
│ ✓ Data Processing      │ ✗ Business Essence Understanding         │
│ ✓ Pattern Recognition      │ ✗ Logical Reasoning             │
│ ✓ Execution Discipline      │ ✗ Creative Thinking           │
│ ✓ Emotional Neutrality      │ ✗ Value Judgment             │
└─────────────────┴───────────────────────┘

Best Practice:
- AI is responsible for: Data screening, signal generation, risk monitoring
- Humans are responsible for: Strategy design, exception handling, value assessment

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##8. Discounted Free Cash Flow (DCF) and Investment Essence

:

Enterprise Value = Σ [FCFt/(1+WACC)^t]

Where:
FCF = Free Cash Flow = Operating Cash Flow - Capital Expenditure
WACC = Weighted Average Cost of Capital
t = Time Period

1. Cash Flow Is King
   : Profits can be manipulated, but cash flow cannot
2. Long-Termism
   : DCF focuses on long-term value creation ability
3. Margin of Safety
   : Conservative DCF assumptions provide downside protection

:

• Traditional DCF model assumptions:

  • ✓ Going concern assumption
  • ✓ Capital availability
  • ✓ Market efficiency

• Actual Situation of Sunac:

  • ✗ Cash flow断裂 (pre-sale fund supervision)
  • ✗ Financing channels closed
  • ✗ Equity dilution due to debt restructuring

: DCF needs to combine and to consider value realization paths under extreme situations

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##9. Building a Complete Risk Control System That Survives Bull and Bear Markets

┌──────────────────────────────────────┐
│  Pre-Risk Control Checklist          │
├──────────────────────────────────────┤
│1. Single asset weight cap (e.g.,10%) │
│2. Industry concentration limit (e.g.,<30%) │
│3. Liquidity requirement (cash + high-liquidity assets >20%) │
│4. Debt ratio screening (exclude high-debt targets) │
│5. Valuation margin of safety (require 30% discount) │
└──────────────────────────────────────┘

Real-Time Monitoring Indicators:
- Portfolio volatility > target value → Reduce position
- Maximum drawdown >-10% → Trigger warning
- Correlation突变 >0.8 → Check concentration
- VaR(95%) exceeds limit → Reduce risk exposure

Extreme Situation Response Plan:
- Market crash >20%: Activate hedging tools
- Individual stock default: Stop loss immediately and re-evaluate
- Liquidity crisis: Activate cash reserves
- Black swan event: Restart stress testing

The concept of ‘antifragile’ was proposed by Nassim Taleb, referring to a system that not only resists pressure but becomes stronger under pressure.

:

1. Option Protection
   : Purchase put options as insurance
2. Tail Risk Arbitrage
   : Reverse layout during market panic
3. Nonlinear Return Structure
   : Limited loss, unlimited profit
4. Evolution Under Stress Testing
   : Optimize the system after each crisis

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##10. Practical Advice: How to Build Your Own Barbell System

:

• What is the maximum drawdown you can accept? (e.g.,-15%)
• Expected annual return? (e.g.,10-15%)
• Investment period? (e.g., over 3 years)
• Tolerance for volatility?

:

Asset Class	Allocation Ratio	Return Expectation	Risk Characteristics
High Dividend Stocks	30%	6-8%	Low volatility
Bonds/Fixed Income+	25%	4-5%	Very low risk
Money Market Fund	15%	2-3%	Risk-free
REITs	10%	5-7%	Medium-low volatility
Gold ETF	5%	0-5%	Inflation hedge

:

Asset Class	Allocation Ratio	Return Expectation	Risk Characteristics
Tech Growth Stocks	10%	15-30%	High volatility
Option Strategy	5%	-100%~300%	Extreme distribution
Emerging Theme	5%	20-50%	High uncertainty
Merger Arbitrage	5%	8-15%	Event-driven
Special Opportunities	5%	Uncertain	Scenario-dependent

:

Buy Conditions (All Must Be Met):
1. Valuation below 30th percentile of historical distribution
2. Fundamental score >80 points
3. Technicals not in a downward trend
4. Portfolio weight not exceeding cap

Sell Conditions (Any Triggered):
1. Valuation above70th percentile of historical distribution
2. Fundamental deterioration (e.g., earnings miss)
3. Stop-loss line triggered (single stock -15%)
4. Find better alternative opportunities

:-
[ ] Is the portfolio Sharpe ratio up to standard?
[ ] Is the maximum drawdown within tolerance?
[ ] Do the weights of both ends need adjustment?
[ ] Are there new risk exposures?
[ ] How is the decision quality? Any violations?

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