The Jeongtaek's Things

Investment Policy Statement

Jeongtaek Bang

Investment Policy Statement (IPS)

Investor Profile

Objective: The primary objective is to achieve long-term capital appreciation through a disciplined, growth-oriented investment strategy in global markets. The approach focuses on resilient, high-growth companies with sound governance, driven by a forward-looking philosophy that emphasizes innovation and sustainable growth.

Investment Horizon: 20 years, with a focus on maximizing growth potential to ensure a comfortable retirement.

Risk Tolerance: High. Prepared to accept market fluctuations and long-term risks in pursuit of substantial growth over the investment period.

Core Investment Premises

1. Long-term Market and GDP Growth

2. Inherent, Constant Risk

3. Objectification of Investments

Investment Philosophy

Understanding Flag Bearers vs. Flag Bearer Candidates:

Why Focus More on Flag Bearer Candidates than on Flag Bearers?

The central question in strategic investing is why it is often better to focus on Flag Bearer Candidates rather than established Flag Bearers. The answer lies in understanding the dynamics of market leadership, growth potential, and the inherent fluidity of company standings within the market.

1. Growth Potential is Maximized in the Candidate Phase

2. The Risk-Reward Balance Favors Candidates

3. Leadership and Innovation Drive Value Creation

4. Market Dynamics and Membership Fluidity

5. Market Corrections and Long-term Resilience

6. Future-Oriented Analysis Over Historical Financial Data

7. Recognizing When Good Companies Become Bad Stocks

Avoiding Market Timing and Chart Overreliance

Importance of Long-Term Upward Growth Vision

Avoiding the Trap of “Mental Accounting”

Recognizing Overvaluation and Responding Appropriately

Strategic Risk Management: Weighted Scenario Planning

Enhanced Decision Quality

Key Insight: Focusing on Flag Bearer Candidates allows investors to participate in the most dynamic phase of a company’s evolution — the point where it strives to become a leader, captures market momentum, and exhibits transformative growth potential. This approach maximizes returns while managing the inherent risk level of long-term investing, making it the preferred strategy for those who aim to achieve outsized gains as market dynamics shift and new leaders emerge.

Portfolio Allocation

Core Holdings

  1. Flag Bearer Candidates
    • Allocation: 50-70% of the portfolio.
    • Objective: Capture high-growth potential from companies nearing key milestones.
    • Selection Criteria:
      • Innovation-driven, high-growth firms with stable governance structures.
      • Founder-led companies with obsessive-compulsive traits, as these leaders often exhibit exceptional dedication and alignment with long-term growth goals.
      • Risk management using weighted scenario planning to ensure robust analysis of risks and opportunities.
  2. Flag Bearers (Cash Equivalents)
    • Allocation: 30-50% of the portfolio.
    • Objective: Provide stability and liquidity while offering returns.
    • Selection Criteria:
      • Established companies with low beta and a solid market cap standing.
      • Tactical Strategy: Cash equivalents will only be liquidated when high-potential opportunities arise among Flag Bearer Candidates.

Tactical Allocation Strategy

Cash Management Strategy

Cash Equivalents

Tax and Profit Realization

Risk Management

Self-Discipline and Execution

Leverage Avoidance

Logging and Documentation

Self-Reflection and Use of GPT

Record-Keeping

Quality of Life Considerations

Balancing Investment with Life Goals

Performance Review and Benchmarking

Evaluation Metrics

Conclusion: Commitment to Long-Term Resilience

Final Note

This IPS represents a disciplined, high-growth strategy with a focus on resilience, founder-led companies, and forward-thinking investments in Flag Bearer Candidates. The commitment to governance quality, self-discipline, and thoughtful decision-making aligns with the ultimate objective: sustainable long-term growth and quality of life improvement.

Scenario-Based Portfolio Allocation Model

Step 1: Define GDP Growth Scenarios

GDP Scenario Breakdown:

The first step in our scenario planning involves predicting the economic growth (GDP growth) based on different possible macroeconomic conditions.

Step 2: Market Index Returns Based on GDP Scenarios

Market Index Performance:

Once GDP is projected, the expected return of market indices (e.g., S&P 500) is calculated.

Step 3: Sector Returns Based on GDP Scenarios

Sector Performance:

Different sectors respond uniquely to macroeconomic conditions.

Step 4: Individual Company Returns (Flag Bearers and Flag Bearer Candidates)

Flag Bearer Candidate Example:

AI Company X – A high-growth, innovation-driven firm on the verge of reaching significant market milestones.

Flag Bearer Example:

Tech Giant Y – A well-established leader with a market cap over $1 trillion.

Step 5: Weighted Average Scenario Planning

Expected Return Calculation Using Weighted Averages:

The final step involves calculating expected returns by weighting the different scenarios based on their probability of occurring.

Step 6: Incorporating Scenario Planning into a Binomial Tree Model

                         GDP Scenario
                              |
    --------------------------------------------------
   |                          |                      |
Above Average (30%)     Average (50%)        Below Average (20%)
   |                          |                      |
S&P 500 (+12%)        S&P 500 (+6%)          S&P 500 (+2%)
   |                          |                      |
Tech Sector (+18%)    Tech Sector (+10%)     Tech Sector (+4%)
   |                          |                      |
Consumer Staples (+8%)  Consumer Staples (+6%)  Consumer Staples (+3%)

Updated Decision Tree


                                Updated GDP Scenario
                                      |
                ---------------------------------------------
               |                          |                  |
    Above Average (46.7%)       Average (44.4%)       Below Average (4.4%)
           |                          |                          |
    S&P 500 (+12%)        S&P 500 (+6%)         S&P 500 (+2%)
        |                          |                          |
  Tech Sector (+18%)       Consumer Staples (+6%)     Utilities (+3%)
  (AI Co: +20%)              (Company X: +8%)         (Company Y: +2%)

Pseudo Code for Portfolio Allocation with Weighted Scenario Planning and Rebalancing

# Step 1: Define Key Metrics, Scenarios, and Allocation Ranges
ROE_THRESHOLD = 15  # Minimum acceptable Return on Equity (%)
CAGR_THRESHOLD = 10  # Minimum acceptable Compound Annual Growth Rate (%)
INNOVATION_LEVEL_THRESHOLD = 7  # Minimum level for innovation to qualify

# Allocation ranges
FLAG_BEARER_CANDIDATE_ALLOCATION = (50, 70)  # Flag Bearer Candidate: 50-70% of portfolio
FLAG_BEARER_ALLOCATION = (30, 50)  # Flag Bearer (cash equivalents): 30-50% of portfolio

# GDP Scenario Probabilities (prior probabilities)
GDP_PROBABILITIES = {
    "above_average": 0.30,
    "average": 0.50,
    "below_average": 0.20
}

# GDP Growth Rates for Each Scenario
GDP_SCENARIOS = {
    "above_average": 4.5,
    "average": 2.5,
    "below_average": 1.0
}

# Likelihoods for Low Inflation
LOW_INFLATION_LIKELIHOODS = {
    "above_average": 0.7,
    "average": 0.4,
    "below_average": 0.1
}

# Step 2: Define Expected Returns for Index and Sectors Based on GDP Scenarios
INDEX_SCENARIO_RETURNS = {
    "above_average": 12,  # Expected S&P 500 return in above-average GDP growth
    "average": 6,         # Expected S&P 500 return in average GDP growth
    "below_average": 2     # Expected S&P 500 return in below-average GDP growth
}

# Sector-specific scenario returns
SECTOR_SCENARIO_RETURNS = {
    "technology": {
        "above_average": 18,
        "average": 10,
        "below_average": 4
    },
    "consumer_staples": {
        "above_average": 8,
        "average": 6,
        "below_average": 3
    },
}

# Step 3: Define Scenario Tree Structure
def build_scenario_tree():
    """
    Build a binomial tree structure for economic scenarios.
    This tree will contain the expected returns for different sectors and indexes based on the GDP growth scenario.
    """
    tree = {
        "GDP": {
            "Above Average (30%)": {
                "S&P 500 Return": INDEX_SCENARIO_RETURNS["above_average"],
                "Technology Sector Return": SECTOR_SCENARIO_RETURNS["technology"]["above_average"],
                "Consumer Staples Return": SECTOR_SCENARIO_RETURNS["consumer_staples"]["above_average"]
            },
            "Average (50%)": {
                "S&P 500 Return": INDEX_SCENARIO_RETURNS["average"],
                "Technology Sector Return": SECTOR_SCENARIO_RETURNS["technology"]["average"],
                "Consumer Staples Return": SECTOR_SCENARIO_RETURNS["consumer_staples"]["average"]
            },
            "Below Average (20%)": {
                "S&P 500 Return": INDEX_SCENARIO_RETURNS["below_average"],
                "Technology Sector Return": SECTOR_SCENARIO_RETURNS["technology"]["below_average"],
                "Consumer Staples Return": SECTOR_SCENARIO_RETURNS["consumer_staples"]["below_average"]
            }
        }
    }
    return tree

# Step 4: Calculate Weighted Returns Based on the Scenario Tree
def calculate_weighted_returns(tree, probabilities):
    """
    Traverse the scenario tree and calculate the weighted expected return for each sector and the index.
    """
    weighted_returns = {}

    for scenario, sectors in tree["GDP"].items():
        # Match scenario probabilities by adjusting the scenario names
        if "Above" in scenario:
            scenario_prob = probabilities["above_average"]
        elif "Average" in scenario:
            scenario_prob = probabilities["average"]
        elif "Below" in scenario:
            scenario_prob = probabilities["below_average"]

        for sector, return_value in sectors.items():
            if sector not in weighted_returns:
                weighted_returns[sector] = 0
            weighted_returns[sector] += scenario_prob * return_value

    return weighted_returns

# Step 5: Define Bayesian Update for Scenario Probabilities
def bayesian_update(prior_probabilities, likelihoods, new_evidence_prob):
    """
    Function to update the GDP scenario probabilities using Bayesian probability.
    new_evidence_prob = P(Low Inflation)
    """
    updated_probabilities = {}
    total_probability = 0

    # Calculate the posterior probability for each GDP scenario
    for scenario, prior_prob in prior_probabilities.items():
        likelihood = likelihoods[scenario]
        updated_probabilities[scenario] = likelihood * prior_prob
        total_probability += updated_probabilities[scenario]  # Denominator (P(B))

    # Normalize the probabilities
    for scenario in updated_probabilities:
        updated_probabilities[scenario] /= total_probability

    return updated_probabilities

# Step 6: Run Bayesian Update with New Evidence (Low Inflation)
updated_gdp_probabilities = bayesian_update(GDP_PROBABILITIES, LOW_INFLATION_LIKELIHOODS, new_evidence_prob=0.45)

# Step 7: Recalculate Weighted Returns with Updated Probabilities
scenario_tree = build_scenario_tree()
weighted_returns = calculate_weighted_returns(scenario_tree, updated_gdp_probabilities)

# Print updated expected returns
print("Updated Weighted Expected Returns Based on Bayesian Update:")
for sector, expected_return in weighted_returns.items():
    print(f"{sector}: {expected_return:.2f}%")

# Step 8: Portfolio Allocation Example
companies = [
    {'name': 'AI Co', 'ROE': 18, 'CAGR': 12, 'innovation_level': 8, 'market_cap': 400_000_000, 'beta': 1.2},
    {'name': 'Tech Giant', 'ROE': 20, 'CAGR': 15, 'innovation_level': 9, 'market_cap': 1_000_000_000, 'beta': 0.8},
    {'name': 'Healthcare Inc', 'ROE': 10, 'CAGR': 6, 'innovation_level': 5, 'market_cap': 1_500_000_000, 'beta': 0.5},
]

portfolio = allocate_portfolio(companies)
print("Allocated Portfolio:", portfolio)

# Step 9: Adjust Portfolio Allocation Based on Updated Expected Returns
adjust_portfolio_allocation(portfolio, weighted_returns)

Key Steps:

This process integrates both risk management and growth opportunity identification using weighted scenarios and a binomial tree model to assess potential outcomes.

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