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
- The market has a historical trend of upward growth over time, even with expected short-term fluctuations. This IPS leverages a long-term view that GDP growth, market indices, and the market cap of well-governed companies will rise in parallel over time.
- Rationale: Sustainable GDP growth signals economic health and typically correlates with stable corporate growth and market advancement. This alignment with macroeconomic indicators is foundational for long-term investing, as it enhances predictability and reinforces the growth potential of selected investments.
2. Inherent, Constant Risk
- Recognizes that risk is a constant in all markets and strategies. The focus will be on maximizing reward relative to this fixed risk through rigorous self-discipline, selection of growth-oriented investments, and sound risk management.
3. Objectification of Investments
- Each investment element is treated as an object with specific attributes. Just as in object-oriented programming, these elements grow, evolve, and develop over time, acquiring new characteristics through polymorphism while encapsulating complexities. By recognizing the growth thresholds — critical markers that investments must reach to maintain survival and progress — this approach ensures that investment strategy remains aligned with dynamic market shifts.
Investment Philosophy
Understanding Flag Bearers vs. Flag Bearer Candidates:
- Flag Bearers are companies that have achieved significant milestones (e.g., market cap milestones like $1 trillion, $2 trillion) and are established leaders in the market.
- Flag Bearer Candidates are those companies approaching these milestones but have not yet reached them, positioning them on the brink of substantial growth and market influence.
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
- Flag Bearer Candidates are at a crucial point in their growth trajectory — they are striving to reach significant market cap milestones that will propel them into industry leadership. The most substantial gains are often realized not when companies have already achieved these milestones, but during their journey to get there.
- Historically, the appreciation in value from moving towards significant markers, like $1 trillion in market cap, is where the highest return potential lies. Established Flag Bearers may offer stability, but their rapid growth phase has often already peaked, leaving limited upside compared to the transformative potential of Candidates.
2. The Risk-Reward Balance Favors Candidates
- Investing in Flag Bearers typically means buying into stability, but at the cost of reduced upside. In contrast, Flag Bearer Candidates carry a higher risk, but they also offer disproportionately higher rewards. This is because the market anticipates their future growth and potential to become leaders. Since the risk level in long-term investing remains constant, focusing on Candidates maximizes the opportunity for returns, aligning with the strategic goal of achieving substantial growth relative to the inherent risks.
3. Leadership and Innovation Drive Value Creation
- Candidates are often at the forefront of innovation, positioning themselves to capture market share and push boundaries. These companies are striving to prove themselves, often making bold moves and disrupting industries. By investing in Candidates, investors participate in this innovative drive, gaining exposure to the rapid evolution of technology and industry leadership.
4. Market Dynamics and Membership Fluidity
- Market leadership is not static; membership among Flag Bearers and Candidates is fluid. Even the most valuable companies can lose their status, as illustrated by Apple’s journey. Once the leader at $3 trillion, it was eventually overtaken by Microsoft due to shifting dynamics — such as Microsoft’s investment in AI.
- Flag Bearer Candidates today can become the Flag Bearers of tomorrow, while current Flag Bearers can be surpassed or fall behind. This inherent fluidity means that capturing the growth phase of emerging leaders often yields better returns than holding onto companies that may have already seen their greatest growth.
5. Market Corrections and Long-term Resilience
- Investing in Flag Bearers also comes with the risk of market corrections, particularly when these companies advance too far without sufficient support from the overall economy (e.g., GDP). Flag Bearer Candidates, however, often have room to grow into their valuation, and market corrections can present buying opportunities at undervalued stages of their journey.
- When a Flag Bearer reaches a milestone (like $1 trillion), market corrections may bring its valuation back into alignment, while Candidates have the potential to advance through such corrections, achieving new milestones and setting the foundation for sustainable growth.
6. Future-Oriented Analysis Over Historical Financial Data
- Investing in Flag Bearer Candidates requires a future-oriented approach. Instead of focusing on historical financial statements, which only reflect the past, prioritize envisioning where these companies are headed. Creativity and the ability to anticipate industry evolution are essential. It is less about crunching numbers and more about understanding the company’s potential trajectory.
- Simplicity and Clarity in Decision-Making: The analysis should be intuitive, not overly complex. If an investment opportunity requires intricate calculations or complex models, it is likely over-engineered and diverges from a straightforward, growth-focused strategy.
7. Recognizing When Good Companies Become Bad Stocks
- A good company doesn’t always remain a good stock. A clear example of this is Tesla: a fundamentally strong company that has become overvalued due to excessive market enthusiasm, leading to significant risk. As investors pour in, pushing the valuation far beyond its fundamentals, the stock can enter a “bad phase.” During such times, market corrections are likely, and some investors may take advantage by taking short positions, betting on a decline.
- Understanding the difference between a good company and a good stock is crucial. Good companies may experience periods where their stock price exceeds intrinsic value due to speculative sentiment, making it a risky investment. Investors must evaluate whether the company’s fundamentals justify its price and stay vigilant for Red Flags signaling overvaluation.
Avoiding Market Timing and Chart Overreliance
- The goal of this IPS is not to engage in market timing or excessive reliance on charts for short-term gains. The philosophy of long-term upward movement is based on consistent accumulation of intrinsic value. Investments should be in companies that steadily build up value over time, realizing the magic of compounding growth. Such growth eventually reflects in the chart as a long-term uptrend.
- Investing in advanced economies is a part of this strategy due to their capacity for consistent GDP growth, with stocks acting as leading indicators that reflect this growth. However, the focus is on intrinsic value and business fundamentals rather than daily chart movements.
- The importance of CAGR (Compound Annual Growth Rate) and sustained earnings growth is emphasized over visual analysis of stock price charts. Investors should aim to understand the underlying growth potential that drives these upward trends, rather than getting caught in the short-term price movements or attempting to time entries and exits.
Importance of Long-Term Upward Growth Vision
- The first premise of long-term investing is ensuring that the company has a sustained long-term upward trajectory. It is not enough for a company to grow significantly for a few years if there is a risk that growth will stagnate or regress afterward. The focus is on companies that continually accumulate value, with the resilience to avoid falling back to their previous levels or worse.
- No Stock Is Always a Good Stock: Even the best companies can experience phases when they are not a good stock to hold due to market overvaluation or other factors. Investors must continuously evaluate whether their holdings still represent a good stock at any given time, and not blindly hold on under the guise of long-term investing.
Avoiding the Trap of “Mental Accounting”
- Investors often fall into the trap of mental accounting, arbitrarily dividing their portfolio into parts meant for different purposes. For example, selling portions of a stock in a perceived “box range” (e.g., selling at perceived highs and buying at perceived lows) often leads to inefficient and emotional decision-making.
- True portfolio management focuses on total return and the cumulative performance of the portfolio rather than segmenting investments based on subjective, non-existent boundaries. Mental accounting leads to flawed strategies, akin to trying to only target specific parts of an investment’s potential while ignoring its overall impact.
Recognizing Overvaluation and Responding Appropriately
- Over-shooting occurs when a stock’s price rises well above its intrinsic value due to excessive enthusiasm — this is a sign of entering a “bad phase” despite being a fundamentally strong company. Greater Fool Theory becomes applicable here, where late investors buy in hopes of finding someone else willing to pay an even higher price, often leading to a market top.
- It is essential to differentiate between holding for long-term growth and neglecting signs of overvaluation. Even for long-term investors, there are times when it is prudent to reduce or exit a position if the stock has clearly moved beyond reasonable valuation levels. Avoid confusing “holding on” with “neglecting due diligence”.
- Warren Buffett often avoids companies that enter such speculative phases. His decision to stay away from the tech bubble is an example of recognizing the risks associated with overvalued stocks, regardless of their underlying business potential.
Strategic Risk Management: Weighted Scenario Planning
- One of the core elements of this philosophy is weighted average scenario planning. This method allows for better risk management by mapping out various potential outcomes and assigning probabilities to each scenario. For instance, investing in a biotech firm awaiting FDA approval might carry a 70% probability of approval but also a 30% chance of rejection. Scenario planning helps adjust position sizes and implement hedging strategies to account for these risks.
- By applying scenario planning, you elevate decision quality and develop the mental flexibility to adapt to different market conditions. This approach forces rigorous research and encourages humility, which helps investors avoid overconfidence in a single outcome.
Enhanced Decision Quality
- Weighted scenario planning not only helps manage risk but also forces a deep level of analysis. It challenges assumptions, encourages diverse perspectives, and leads to more robust decision-making. Collaborating with advisors or trusted voices often reveals blind spots, resulting in more comprehensive investment strategies.
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
- 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.
- 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
- Rebalancing Triggers:
Regularly reassess portfolio allocation based on economic changes (e.g., GDP revisions or sector performance shifts). Set triggers, such as a 5% drop in ROE or underperformance in a sector, to prompt rebalancing. - Adjustment Frequency:
Quarterly reviews or when a major shift in GDP or company projections occurs.
Cash Management Strategy
Cash Equivalents
- Policy: Minimize cash holdings, instead using low-beta stocks in the Flag Bearer category as cash equivalents.
- Objective: Maintain liquidity and stability while maximizing growth potential.
- Guidelines: Liquidate cash equivalents only when high-potential investment opportunities arise in the Flag Bearer Candidate category, ensuring efficient capital deployment.
Tax and Profit Realization
- Policy: Periodically realize profits and pay taxes, which reinforces gains and provides stability to the overall portfolio.
- Objective: Enhance quality of life through disciplined investment, creating financial security while keeping the focus on long-term goals.
Risk Management
Self-Discipline and Execution
- Policy: Emphasize self-management as the primary risk factor. Maintain a disciplined approach to mitigate risks associated with decision-making and emotional influence.
- Execution Focus: Prioritize the alignment of actions with the outlined strategy, with minimal focus on real-time profit/loss metrics to avoid distraction.
Leverage Avoidance
- Policy: Avoid leverage to minimize compounded risks.
- Objective: Maintain financial resilience by managing investments without leveraging, given the inherent risks associated with long-term growth strategies.
Logging and Documentation
Self-Reflection and Use of GPT
- Policy: Keep comprehensive records of investment decisions and strategies in an essay-like format to promote self-reflection.
- GPT Utilization: Use GPT selectively in the refining process, avoiding dependency on it for initial drafts. Maintain independent, critical analysis as the core of decision-making.
Record-Keeping
- Guidelines: Document every decision and thought process for future reference, challenging and validating insights to continuously improve strategic understanding.
Quality of Life Considerations
Balancing Investment with Life Goals
- Objective: The ultimate purpose of investing is to improve quality of life, both financially and personally.
- Policy: Engage in activities outside of the market that bring joy and maintain mental resilience.
- Guidelines: Regularly evaluate whether the investment journey contributes positively to overall well-being, adjusting as necessary.
Performance Review and Benchmarking
Evaluation Metrics
- Growth Benchmarks: Assess performance against major market indices and the progress of Flag Bearer Candidates relative to their milestones.
- Long-term Focus: Use GDP and other economic indicators of countries invested in as markers for evaluating growth alignment.
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.
- Above Average GDP Growth (High Growth)
- Key Factors:
- AI-driven productivity boom
- Global innovation breakthroughs
- Improved income distribution
- Geopolitical stability
- Low-interest rate environment
- Probability Estimate: 30%
- Expected GDP Growth Rate: 4-5% annually
- Average GDP Growth (Steady Growth)
- Key Factors:
- Gradual AI integration into industries
- Steady innovation across sectors
- Moderate geopolitical risks
- Normal interest rates
- Probability Estimate: 50%
- Expected GDP Growth Rate: 2-3% annually
- Below Average GDP Growth (Low Growth or Recession)
- Key Factors:
- AI adoption slowdown or regulation hurdles
- Increased geopolitical tensions (trade wars, conflicts)
- High-income inequality reducing consumer demand
- High-interest rates curbing investments
- Probability Estimate: 20%
- Expected GDP Growth Rate: 0-1% annually
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.
- Above Average GDP Scenario:
- Expected Market Return: 10-12%
- Rationale: High growth drives corporate earnings across sectors like technology and industrials.
- Average GDP Scenario:
- Expected Market Return: 5-7%
- Rationale: Steady growth sustains returns at historical averages, led by sectors like healthcare and consumer discretionary.
- Below Average GDP Scenario:
- Expected Market Return: 0-3%
- Rationale: Economic stagnation leads to low earnings growth, favoring defensive sectors like utilities and staples.
Step 3: Sector Returns Based on GDP Scenarios
Sector Performance:
Different sectors respond uniquely to macroeconomic conditions.
- Above Average GDP Scenario:
- Technology, Industrials, and Healthcare: Expected to outperform with returns between 15-20%.
- Average GDP Scenario:
- Consumer Staples, Financials, and Healthcare: Expected returns of 5-10%.
- Below Average GDP Scenario:
- Utilities, Consumer Staples, and Healthcare: Expected returns of 2-5%, providing stability during downturns.
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.
- Above Average GDP Scenario:
- Expected Return: 20-30%
- Rationale: Boosted by strong investment in AI and global tech innovation.
- Average GDP Scenario:
- Expected Return: 10-15%
- Rationale: Consistent growth aligned with steady economic conditions.
- Below Average GDP Scenario:
- Expected Return: 5-10%
- Rationale: Slower growth due to weakened investment sentiment.
Flag Bearer Example:
Tech Giant Y – A well-established leader with a market cap over $1 trillion.
- Above Average GDP Scenario:
- Expected Return: 8-10%
- Rationale: Solid returns from continued dominance but limited explosive growth potential.
- Average GDP Scenario:
- Expected Return: 5-7%
- Rationale: Steady performance driven by stable earnings growth.
- Below Average GDP Scenario:
- Expected Return: 0-3%
- Rationale: Valuation risks during economic downturns could limit returns.
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.
- GDP Weighted Average Growth Rate:
- (30% × 4.5%) + (50% × 2.5%) + (20% × 1.0%) = 2.75% expected GDP growth.
- Index Weighted Average Return:
- Above Average (30% × 12%) + Average (50% × 6%) + Below Average (20% × 2%) = 6.8% expected return for the S&P 500.
- Flag Bearer Candidate Example – AI Company X:
- Above Average (30% × 25%) + Average (50% × 12.5%) + Below Average (20% × 7%) = 14.65% expected return.
- Flag Bearer Example – Tech Giant Y:
- Above Average (30% × 9%) + Average (50% × 6%) + Below Average (20% × 2%) = 5.9% expected return.
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
- Bayesian Probability is a way to update our belief about an event happening by combining our initial belief (prior probability) with new evidence (likelihood) to get a revised belief (posterior probability).
- After applying Bayes’ Theorem, the updated decision tree for your investment strategy based on GDP growth looks like this:
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:
- Define Key Metrics and Scenarios: Set up thresholds for company performance metrics like ROE and CAGR, and assign probabilities to various GDP growth scenarios.
- Company Selection: Sort companies into Flag Bearer Candidates (high growth) and Flag Bearers (cash equivalents).
- Scenario Planning: Calculate weighted average expected returns based on different GDP scenarios for each company.
- Portfolio Allocation: Allocate companies into categories based on their performance and manage reallocation when opportunities arise (like reallocating from Flag Bearers to high-growth Candidates).
- Scenario-Based Returns: Adjust the portfolio dynamically based on expected returns from the weighted average scenario planning.
- Portfolio Allocation Ranges: Adjusted allocation for Flag Bearer Candidates (50-70%) and Flag Bearers (30-50%) with validation of the portfolio.
- Weighted Average Scenario Calculation: For each company, weighted average expected return is calculated based on different GDP scenarios and associated probabilities.
- Bayesian Update Function: The bayesian_update function uses Bayes’ Theorem to update the probabilities of GDP scenarios after receiving new evidence, such as a government report on low inflation.
- Updated GDP Probabilities: The function bayesian_update calculates the posterior probabilities for each scenario.
- Weighted Returns Recalculation: After updating the probabilities using Bayes’ Theorem, the new weighted expected returns are calculated using the same tree structure. This will give you the updated return expectations based on the new scenario probabilities.
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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