Simplified Decision Framework for Value Investing in Post-AI World¶

Date: September 11^th, 2025
Author: Daniel Shanklin
Tags: Value Investing, AI Disruption, Investment Framework, Options Trading

TL;DR¶

• Challenge: Traditional value investing needs systematic approach for AI-disrupted markets
• Opportunity: Create measurable framework linking conviction to purchase strategy
• Solution: Three-step process: AI regime detection → conviction scoring → options-based execution
• Ask: Launch paper portfolio using this framework to generate trackable P&L by Q4 2025

The Complete Framework Overview¶

Value investing in the post-AI world requires a systematic approach that's simple enough to execute consistently but sophisticated enough to account for AI disruption. The framework consists of three sequential steps that culminate in measurable P&L through a paper portfolio:

Step 1: Multi-AI regime detection (macro vs micro environment)
Step 2: Conviction scoring (Buffett fundamentals + AI durability assessment)
Step 3: Purchase strategy selection (options-based execution matched to conviction level)

This end-to-end process transforms subjective investment decisions into a repeatable methodology with trackable performance outcomes.

Step 1: Multi-AI Regime Detection¶

Before analyzing individual stocks, determine whether we're in a macro-dominated or micro-dominated market environment. This affects how value investing strategies should be applied.

Current Market Assessment (September 11, 2025)¶

Using consensus from three AI sources:

AI Source	Regime Assessment	Key Indicators	Confidence
Perplexity AI	Micro-Dominated	VIX <20, sector dispersion high, earnings driving moves	High
Claude Deep Research	Micro-Dominated	Low correlations, policy stable, fundamentals matter	High
OpenAI Analysis	Micro-Dominated	Company-specific factors dominating sector trends	Medium-High

Consensus: MICRO-DOMINATED ENVIRONMENT

Implications for Value Investing: - Focus on individual company fundamentals over macro themes - Stock picking and security selection take priority - Traditional value metrics carry more weight - Company-specific catalysts drive returns more than policy changes

Regime Assessment Framework¶

Market Regime	Primary Drivers	Value Strategy Focus	Success Factors
Macro-Dominated	Policy, rates, geopolitics	Asset allocation, sector rotation	Positioning, timing
Micro-Dominated	Earnings, fundamentals, company execution	Individual stock selection	Analysis, conviction

Framework Rule: Only proceed to stock-specific analysis when in micro-dominated environment. In macro-dominated periods, focus on broad positioning and wait for regime shift.

Step 2: Conviction Scoring System¶

For each potential investment, calculate a simple 1-5 conviction score combining Buffett-style fundamentals with AI-enhanced durability assessment.

Buffett Fundamentals Screen (Pass/Fail)¶

Metric	Threshold	Purpose
ROE	>12% sustained	Profitable use of shareholder capital
Debt/Equity	<0.5	Financial stability
Current Ratio	>1.5	Liquidity cushion
Earnings Growth	Positive 3-year trend	Business momentum
Reasonable Valuation	P/E <25, P/B <5	Not obviously expensive

Rule: Must pass ALL criteria to proceed to AI assessment.

Enhanced Assessment (1-5 Scale)¶

Using Claude Deep Research to evaluate five key factors:

Factor	Score 5	Score 3	Score 1
Industry Disruption Timeline	10+ years to disruption	5-10 year timeline	2-5 year disruption risk
Competitive Moat Durability	AI strengthens moats	AI neutral to moats	AI weakens moats
Regulatory Risk	Regulatory protection	Mixed regulatory impact	High regulatory risk
Market Expansion	AI opens new markets	Limited AI expansion	No AI opportunities
Relative Valuation	Bottom 20% cheapest in sector	Middle 40-60% vs peers	Top 20% most expensive

Quality Score: Average of first four AI factors (1-5)
Price Score: Relative valuation factor (1-5)
Final Conviction: MIN(Quality Score, Price Score) - ensures no great company at terrible price gets high conviction

Quality Gates (Pass/Fail Filters)¶

Before final conviction scoring, companies must pass two critical gates designed to avoid common value investing failures:

Gate 1: Momentum Filter¶

Purpose: Prevent value traps where fundamentals are deteriorating faster than price reflects

Criteria (Must pass at least 1 of 2): - 6-month EPS revision trend: NOT in bottom quintile vs sector peers - 6-month price momentum: NOT in bottom quintile vs sector peers

Action if Failed: Cap final conviction at 2 regardless of quality/price scores

Gate 2: Accounting Red-Flag Filter¶

Purpose: Avoid companies with low-quality earnings or poor governance

Red Flags (Any one triggers rejection): - Accruals ratio >80^th percentile vs industry (aggressive accounting) - Auditor resignation in past 12 months - Share count growth >5% annually without commensurate ROIC improvement - Frequent non-GAAP adjustments or one-time charges

Action if Failed: Automatic rejection regardless of all other scores

Framework Logic: Quality gates → 5-factor scoring → MIN rule → final conviction

Conviction Examples¶

Example 1: Industrial Software Company

Buffett Screen: PASS (ROE 18%, Debt/Equity 0.2, solid fundamentals)
Quality Gates: PASS (positive EPS revisions, clean accounting)
AI Quality Score: Industry Disruption (4), Moat Durability (5), Regulatory (4), Market Expansion (4) = 4.25
Price Score: Trading at 40^th percentile vs sector peers = 3
Final Conviction: MIN(4.25, 3) = Conviction 3

Example 2: Traditional Retailer

Buffett Screen: PASS (meets all financial criteria)
Quality Gates: PASS (momentum neutral, no red flags)
AI Quality Score: Industry Disruption (2), Moat Durability (2), Regulatory (3), Market Expansion (2) = 2.25
Price Score: Trading at 10^th percentile (very cheap) = 5
Final Conviction: MIN(2.25, 5) = Conviction 2

Example 3: Cheap Tech Stock (Value Trap)

Buffett Screen: PASS (strong balance sheet, profitable)
Quality Gates: FAIL (bottom quintile EPS revisions, declining fundamentals)
Result: Automatic conviction cap at 2, avoiding value trap despite cheap valuation

Step 3: Purchase Strategy Selection¶

Match instrument selection to conviction level using options-based execution framework from yesterday's analysis:

Conviction Score	Strategy	Capital Allocation	Expected Outcome
5 (Highest)	Call Options	0.5-1% NAV	200%+ upside, -100% risk
4 (High)	Call Spreads	1-2% NAV	100%+ upside, -100% risk
3 (Moderate)	Equity + Call Overlays	3-5% NAV	Enhanced equity returns
2 (Low)	Small Equity Position	1-2% NAV	Standard equity exposure
1 (Very Low)	No Position	0% NAV	Preserve capital

Strategy Implementation Details¶

Conviction 5 - Call Options: - Buy at-the-money calls, 90-day expiration - Size to lose maximum 1% of NAV if wrong - Target 3-5x returns on successful calls

Conviction 4 - Call Spreads: - Long lower strike, short higher strike - Spread width targeting 2:1 reward/risk - 60-90 day expiration cycle

Conviction 3 - Equity + Overlays: - Core equity position (3-4% NAV) - Add call options for 20-30% position enhancement - Maintain equity through volatility

Framework Advantage: Systematic approach eliminates emotional decision-making and provides clear risk/reward parameters for each conviction level.

Decision Process Flow¶

flowchart TD
    A[Weekly AI Regime Check] --> B{Macro vs Micro?}
    B -->|Macro Dominated| C[Wait - Focus on Asset Allocation]
    B -->|Micro Dominated| D[Begin Stock Analysis]

    D --> E[Buffett Fundamentals Screen]
    E --> F{Pass All 5 Criteria?}
    F -->|No| G[Reject - No Position]
    F -->|Yes| H[Quality Gates Check]

    H --> I{Pass Momentum Gate?<br/>EPS revisions + price momentum}
    I -->|No| J[Cap Conviction at 2]
    I -->|Yes| K{Pass Accounting Gate?<br/>Clean financials + governance}
    K -->|No| G
    K -->|Yes| L[5-Factor Assessment]

    L --> M[Claude Deep Research:<br/>AI Quality: 4 factors<br/>Price: Relative valuation]
    M --> N[Calculate Scores<br/>Quality = Avg of 4 AI factors<br/>Price = Valuation percentile]

    N --> O[Final Conviction = MIN Quality, Price]
    J --> O
    O --> P{Conviction Score?}

    P -->|1-2| Q[No/Small Position<br/>0-2% NAV]
    P -->|3| R[Equity + Call Overlays<br/>3-5% NAV]
    P -->|4| S[Call Spreads<br/>1-2% NAV]
    P -->|5| T[Call Options<br/>0.5-1% NAV]

    Q --> U[Paper Portfolio Entry]
    R --> U
    S --> U
    T --> U
    U --> V[Track P&L Performance]
    V --> W[Monthly Framework Review]
    W --> A

    style A fill:#e1f5fe
    style H fill:#fff3e0
    style M fill:#f3e5f5
    style U fill:#e8f5e8
    style V fill:#fff3e0

External Data Processes Required¶

Daily/Weekly Data Feeds:

Market Regime Inputs: VIX levels, sector correlations, earnings dispersion metrics
Individual Stock Metrics: ROE, Debt/Equity, Current Ratio, P/E, P/B ratios, sector percentile rankings
Momentum Indicators: 6-month EPS revision trends, price momentum vs sector peers
Quality Metrics: Accruals ratios, share count growth, auditor changes, non-GAAP adjustments
Options Pricing Data: Implied volatility surfaces, bid/ask spreads, expiration cycles

AI Research Processes:

Perplexity AI Query: "Assess current market regime - macro vs micro dominance based on correlations, volatility, policy uncertainty"
Claude Deep Research: "Analyze [COMPANY] for: industry disruption timeline, competitive moat durability in AI era, regulatory risks, market expansion opportunities, relative valuation vs sector peers"
OpenAI Analysis: "Evaluate whether current market environment favors security selection vs asset allocation"

Portfolio Management Systems:

Paper Portfolio Tracking: Position entry/exit, P&L calculation, performance attribution
Risk Monitoring: NAV allocation limits, stop-loss triggers, correlation analysis
Performance Analytics: Monthly conviction score accuracy, strategy effectiveness by market regime, quality gate effectiveness

Paper Portfolio Implementation¶

Portfolio Structure¶

Target allocation across conviction levels:

Conviction Level	Target # Positions	Max Allocation	Strategy Type
Conviction 5	2-3 positions	3% NAV total	Call Options
Conviction 4	3-4 positions	6% NAV total	Call Spreads
Conviction 3	4-5 positions	20% NAV total	Equity + Overlays
Conviction 2	2-3 positions	6% NAV total	Small Equity
Cash/Waiting	-	65% NAV	Money Market

Total Risk Capital: 35% NAV maximum deployed across all conviction levels

Monthly Review Process¶

Performance Tracking:

Individual position P&L vs conviction score
Strategy effectiveness by conviction level
Regime detection accuracy (macro vs micro calls)
AI durability assessment accuracy vs business outcomes

Framework Refinement:

Adjust conviction scoring weights based on prediction accuracy
Refine purchase strategy allocation based on risk-adjusted returns
Update regime detection criteria based on market evolution

Expected Outcomes and Success Metrics¶

12-Month Performance Targets¶

Primary Metrics:

Overall Returns: 15-25% annual returns with <15% volatility
Conviction Accuracy: Conviction 4-5 positions outperform 70%+ of the time
Risk Management: No individual position loses >50% of allocation
Capital Efficiency: Options strategies achieve 2x+ returns vs equity-only approach

Learning Objectives:

Validate AI regime detection accuracy across different market cycles
Refine conviction scoring methodology based on real outcomes
Optimize purchase strategy selection for different market environments
Build systematic process that can scale to larger capital allocations

Implementation Timeline¶

Phase 1: Framework Setup (Weeks 1-2)¶

Configure AI research queries for regime detection and company analysis
Set up paper portfolio tracking system with P&L calculation
Establish data feeds for fundamental metrics and options pricing

Phase 2: Initial Screening (Weeks 3-4)¶

Run Buffett fundamentals screen across investable universe
Conduct AI durability assessments on 20-25 candidates
Calculate initial conviction scores and build candidate pipeline

Phase 3: Position Implementation (Weeks 5-8)¶

Begin paper portfolio entries based on conviction levels
Execute purchase strategies matching framework guidelines
Establish monitoring and review processes

Phase 4: Performance Validation (Months 3-6)¶

Track framework accuracy vs actual market outcomes
Refine conviction scoring based on performance attribution
Optimize position sizing and purchase strategy selection

Conclusion¶

The simplified decision framework provides a systematic, measurable approach to value investing in the post-AI world. By combining multi-AI regime detection with structured conviction scoring and options-based execution, the framework transforms subjective investment decisions into a repeatable process with trackable performance outcomes.

Key Advantages:

Simplicity: Three clear steps eliminate decision paralysis and emotional trading
Measurability: Paper portfolio generates real P&L data for continuous improvement
Adaptability: AI-enhanced scoring accounts for disruption while maintaining value principles
Scalability: Systematic approach can grow with larger capital allocations

The framework's strength lies not in perfect prediction, but in consistent application of sound principles enhanced by AI insights. Success depends on disciplined execution and continuous refinement based on actual market outcomes.

Next Steps: Launch paper portfolio implementation by October 1^st, 2025, with first performance review scheduled for December 31^st, 2025.

This framework represents a practical evolution of value investing principles for an AI-transformed investment landscape, emphasizing measurable outcomes over theoretical sophistication.