15. Academic Foundation

The scoring methodology in this system is grounded in modern portfolio theory as documented in OpenStax Principles of Finance, Chapter 15: Risk, Return, and the Capital Asset Pricing Model.

Core theoretical foundations¶

Risk and Return (15.1)
Return is measured as (End Price - Begin Price) / Begin Price. Risk is measured as standard deviation of returns over time. Both must be considered together — return without risk context is meaningless.

Diversification (15.2)
Holding multiple uncorrelated assets reduces portfolio standard deviation below the weighted average of individual stock standard deviations. This is why the two-strategy structure (IRA + Brokerage) with different universes matters — it intentionally holds names with lower cross-strategy correlation.

Firm-specific risk can be diversified away. Systematic (market) risk cannot be eliminated through diversification. Investors are only compensated for systematic risk — the foundation of the entire CAPM framework.

CAPM (15.3)
Required_Return = Rf + Beta × (Rm - Rf)

Beta measures systematic risk — how much a stock moves with the market.

• Beta > 1: more volatile than market, higher required return.

• Beta < 1: less volatile than market, lower required return.

• Beta = 1: moves with market.

Important: Different vendors report different betas because they use different time windows (1–5 years) and frequencies (daily/weekly/monthly). AUGO example: yfinance reported 0.31, Etrade showed 2.0 for same stock. V2 will calculate beta directly from regression to eliminate this variance.

Performance Measurement (OpenStax §15.4)

• Sharpe Ratio = (Portfolio Return - Rf) / StdDev(Returns) → Reward per unit of TOTAL risk

• Treynor Ratio = (Portfolio Return - Rf) / Portfolio Beta → Reward per unit of SYSTEMATIC risk (most relevant for this system)

• Jensen’s Alpha = Actual Return - CAPM Expected Return → Did stock selection add value beyond what risk predicted? → The primary long-term validation metric for this methodology

A positive Jensen’s Alpha sustained over 26+ weeks is the evidence base for the newsletter and eventual publication of the methodology.

15.4 Behavioral Finance Layer¶

Primary citation: Barber, B. and Odean, T. (1999). “The Courage of Misguided Convictions.” Financial Analysts Journal, 55(6), 41-55. DOI: 10.2469/faj.v55.n6.2313

The disposition effect: Individual investors are 50% more likely to sell winners than losers. Winners sold continue to outperform. This is the primary driver of individual underperformance.

System design response: Every rule in this methodology exists to prevent a specific behavioral bias:

8% sell rule → prevents disposition effect on losers Power trend rule → prevents premature winner sales 10-point checklist → prevents overconfident entry Volume 1.40x+ → prevents impulse buying Acc/Dis check → prevents buying into distribution 60-second checklist → prevents UTHR-type and CRDO Stage 3 errors

The equanimity connection: Rules replace psychology. Process replaces emotion. Equanimity is the psychological state that allows consistent rule execution when emotions conflict.

This is the behavioral finance layer of the six-layer methodology — not explicitly scored but embedded in every entry and exit rule.

See also §3.4 Academic Foundation — Behavioral Finance.

15.5 Modern Portfolio Theory Framework¶

This system implements a practitioner version of the Carhart Four-Factor Model (1997):

Factor 1 — Market (CAPM): capm_wacc.py | Required_Return = Rf + Beta × MRP Alpha_Gap measures excess return above requirement

Factor 2 — Size: BC20 universe focuses on large cap names (implicit)

Factor 3 — Value: WACC_Spread identifies undervalued vs overvalued P/E, Forward P/E in stock analysis

Factor 4 — Momentum: IBD RS Rating = price momentum factor IBD Composite = earnings + price momentum combined

Original contribution: Beta_Penalty = 1/(1+max(0,β-1.0)) — proprietary Six-layer synthesis with institutional signals Forward-tested live performance data (not backtested) Entry signal scoring (Enhancement 8)

Academic gap filled: No published paper combines all six layers No practitioner framework with forward-tested results This system is the first documented attempt

Why CAPM over other models¶

Alternative models exist (Fama-French 3-factor, Carhart 4-factor, Black-Litterman) but add complexity without proportional benefit for a self-directed investor running a weekly process.

CAPM’s advantages for this use case:

1. Single additional input beyond IBD data (beta, readily available)

2. Transparent and explainable to newsletter subscribers

3. Academically validated since Sharpe (1964)

4. Directly mappable to position sizing decisions

5. Jensen’s Alpha provides clear performance accountability

The Beta_Penalty formula (1 / (1 + max(0, β-1))) is this system’s proprietary application of CAPM to IBD momentum scoring — not found in standard CAPM literature. This is the core IP.

15.6 Qualitative Investment Analysis — SimplyWallSt Framework¶

Reference: SimplyWallSt community narrative format Adapted for Six Layers methodology May 2026

The five-element qualitative framework complements the six quantitative layers by answering WHY the business deserves a premium or discount beyond what the numbers alone can show.

Published examples applied to portfolio holdings:

NVDA qualitative thesis (May 2026):

• Thesis: Computing substrate of AI economy

• Proof: $75B data center revenue +92% YoY. Jensen Huang: “demand has gone parabolic”

• Moat: CUDA ecosystem — 4M+ developers. Switching cost is prohibitive at scale.

• Trajectory: Agentic AI inference > training. NVLink networking +64% sequential.

• Mispricing: Market prices as cyclical chip company. Should price as platform (74.9% gross margin).

MRVL qualitative thesis (May 2026):

• Thesis: Hidden infrastructure layer of AI

• Proof: Google custom chip uses MRVL controllers. Amazon Trainium uses MRVL networking.

• Moat: Co-design relationships — 3-5 year cycles. Embedded deep in hyperscaler infrastructure.

• Trajectory: Inference growth > training growth. MRVL networking required for both.

• Mispricing: Discount to pure-play AI software despite more durable co-design revenue cycles.

15.7 Publication Target — Financial Analysts Journal¶

Target: Financial Analysts Journal (CFA Institute) Same journal that published Barber-Odean (1999). Practitioner-academic bridge — highest credibility.

Abstract framework:

“We present a six-layer systematic investment methodology combining IBD momentum signals, CAPM risk adjustment, and rule-based behavioral override mechanisms, tested on live forward performance data over 52 weeks. We find that systematic rule enforcement reduces disposition effect manifestation and that risk-adjusted returns exceed benchmark performance, consistent with Barber and Odean (1999).”

SSRN working paper: target Fall 2026

• Social Science Research Network — free, immediate, citable, timestamped — establishes IP priority date

Series 65: target Winter 2026

• Unlocks paid newsletter tier legally

• Investment Adviser Representative registration

15.8 PMI Portfolio Management Framework — PfMP Parallel¶

The Isomorphic Relationship¶

The PMI Portfolio Management Professional (PfMP) framework describes portfolio management as a continuous adaptive process designed to align organizational investments with strategic objectives. This investment methodology applies the identical framework to financial portfolio management.

Both domains manage portfolios of components (projects vs stocks), allocate limited resources (budget vs capital), apply governance rules (authorization gates vs entry checklists), monitor performance (earned value vs alpha), and optimize through continuous improvement cycles.

The Four Life Cycle Stages¶

Stage 1 — Initiation

• PfMP: Define objectives, authorize portfolio

• System: April 8 Follow-Through Day = portfolio initiation. Three accounts authorized with distinct mandates.

Stage 2 — Planning

• PfMP: Develop roadmap, allocate resources, define governance

• System: Weekly pipeline = portfolio planning cadence. Pre-buy thesis = component authorization gate. 8% sell rule + entry checklist = governance rules.

Stage 3 — Execution

• PfMP: Implement components, manage interdependencies

• System: Execute at IBD pivot points on volume confirmation. Correlation management = interdependency control. Position sizing = resource deployment discipline.

Stage 4 — Optimization

• PfMP: Rebalance, retire underperformers, reallocate

• System: Sell rule = retire underperforming components. Capital recycling = reallocation to better signals. Enhancement iterations = continuous improvement.

The PMIS Parallel¶

PfMP: Portfolio Management Information System (PMIS) = suite of tools for visibility and data integration

This system’s PMIS:

• Data ingestion: ingest_ibd_lists.py, ingest_marketsurge.py

• Data processing: capm_wacc.py, portfolio_analyzer.py

• Performance tracking: performance_tracker.py

• Dashboard: institutional_dashboard.html

• Signal generation: entry_signal_generator.py

• Audit trail: GitHub repository

• Governance output: WEEKLY_JOURNAL.md, TECHNICAL_GUIDE.md

• Executive reporting: saturday_briefing.py (AI narrative)

The Governance Framework Parallel¶

Cross-Domain Contribution¶

No published investment methodology has explicitly mapped to the PMI PfMP framework. This parallel represents an original cross-domain contribution — demonstrating that systematic investment management is a formal application of portfolio management science.

The practitioner who holds PMP/PfMP credentials and applies them to investment portfolio management creates a unique intersection that strengthens academic novelty.

Reference: PMI Standard for Portfolio Management, 4th Edition — Project Management Institute