Step 1: Define Value Investing Criteria

Before diving into the technical aspects of building an AI agent, it's essential to understand the fundamental principles of value investing. This step lays the foundation for your entire project by defining the criteria your AI will use to evaluate companies.

What is Value Investing?

Value investing is an investment strategy that involves picking stocks that appear to be trading for less than their intrinsic or book value. Value investors actively seek stocks they believe the market has undervalued.

Key Value Investing Principles

The following principles form the core of value investing strategy:

Intrinsic Value vs. Market Price
Low Price-to-Earnings (P/E) or Price-to-Book (P/B) Ratios
Strong Free Cash Flow (FCF)
Low Debt-to-Equity (D/E)
Consistent Earnings Growth
High Return on Equity (ROE)
Economic Moats (qualitative factor)

Detailed Explanations

Intrinsic Value vs. Market Price: The fundamental concept that a stock's true value may differ from its current market price. Value investors look for stocks trading below their intrinsic value.

Low P/E or P/B Ratios: These ratios help identify potentially undervalued stocks. A low P/E suggests you're paying less for each pound of earnings, while a low P/B indicates the stock may be trading below its book value.

Strong Free Cash Flow: Companies with strong FCF have money left over after covering operating expenses and capital expenditures, indicating financial health.

Low Debt-to-Equity: Lower D/E ratios suggest the company is financing more of its operations through equity rather than debt, reducing financial risk.

Consistent Earnings Growth: Steady growth in earnings over time indicates a stable business with good long-term prospects.

High ROE: Return on Equity measures how efficiently a company uses its equity to generate profits. Higher ROE indicates better management efficiency.

Economic Moats: These are competitive advantages that protect a company from competitors, such as brand recognition, patents, or network effects.

Translating Principles into Measurable Indicators

For your AI agent to evaluate companies based on value investing principles, you need to translate these concepts into measurable indicators that can be programmatically assessed.

Python: Defining Value Investing Criteria

# Define value investing criteria thresholds
value_criteria = {
    # Price ratios
    'pe_ratio': {'max': 15, 'weight': 0.15},  # Price-to-Earnings ratio
    'pb_ratio': {'max': 1.5, 'weight': 0.15},  # Price-to-Book ratio
    
    # Cash flow metrics
    'fcf_yield': {'min': 0.05, 'weight': 0.15},  # Free Cash Flow Yield (FCF/Market Cap)
    
    # Debt metrics
    'debt_to_equity': {'max': 0.5, 'weight': 0.1},  # Debt-to-Equity ratio
    
    # Growth metrics
    'earnings_growth': {'min': 0.05, 'weight': 0.1},  # 5-year earnings growth rate
    
    # Profitability metrics
    'roe': {'min': 0.15, 'weight': 0.15},  # Return on Equity
    
    # Dividend metrics
    'dividend_yield': {'min': 0.02, 'weight': 0.1},  # Dividend Yield
    
    # Qualitative factors (will require NLP later)
    'economic_moat': {'min': 0, 'max': 5, 'weight': 0.1}  # Scale of 0-5
}

# Function to calculate a simple value score
def calculate_value_score(company_data):
    score = 0
    max_score = 0
    
    for criterion, details in value_criteria.items():
        if criterion in company_data:
            max_score += details['weight']
            
            # For metrics where lower is better (like P/E ratio)
            if 'max' in details and not 'min' in details:
                if company_data[criterion] <= details['max']:
                    score += details['weight']
            
            # For metrics where higher is better (like ROE)
            elif 'min' in details and not 'max' in details:
                if company_data[criterion] >= details['min']:
                    score += details['weight']
            
            # For metrics with a range (like economic moat)
            elif 'min' in details and 'max' in details:
                if company_data[criterion] >= details['min'] and company_data[criterion] <= details['max']:
                    # Proportional score within range
                    normalized = (company_data[criterion] - details['min']) / (details['max'] - details['min'])
                    score += details['weight'] * normalized
    
    # Normalize to 0-100 scale
    if max_score > 0:
        final_score = (score / max_score) * 100
    else:
        final_score = 0
        
    return final_score

# Example company data
example_company = {
    'pe_ratio': 12.5,
    'pb_ratio': 1.2,
    'fcf_yield': 0.07,
    'debt_to_equity': 0.3,
    'earnings_growth': 0.08,
    'roe': 0.18,
    'dividend_yield': 0.03,
    'economic_moat': 4
}

# Calculate value score
score = calculate_value_score(example_company)
print(f"Value Investing Score: {score:.2f}/100")

Interactive Exercise: Define Your Own Criteria

Consider which value investing criteria are most important to you. Think about how you would adjust the weights in the code example above based on your investment philosophy.

For example, if you believe that low debt is the most critical factor, you might increase the weight of the debt-to-equity ratio.

Exercise: Customize Value Criteria

Try modifying the weights in the code above to match your investment priorities. Here are some questions to consider:

Which metrics do you think are most important for identifying undervalued companies?
Would you add any additional criteria not listed in the example?
How would you adjust the thresholds (min/max values) based on current market conditions?

Remember that there's no single "correct" set of criteria - different value investors emphasize different factors based on their experience and risk tolerance.

Knowledge Check

Which of the following is NOT typically considered a value investing criterion?

Low Price-to-Earnings (P/E) ratio
Strong Free Cash Flow (FCF)
High Trading Volume
Low Debt-to-Equity ratio