Bottleneck Investing with the AuditAlpha™ Stock Analysis Engine

This course is for private investors who hold positions for months rather than weeks and want a structured way to identify companies that control a bottleneck in a growing ecosystem, and to monitor those positions systematically once they are held. It suits participants of the Short-Term Quality Investing course, but does not require it.

No formal prerequisite is required. Some familiarity with basic fundamental analysis (revenue growth, margins, valuation multiples) and basic chart reading (moving averages, support and resistance) is helpful, but all concepts needed for the course are introduced from first principles.

By the end of the course, you will be able to assess whether a company holds a genuine bottleneck position, whether the timing of an entry is favorable, and which risks could invalidate the thesis, using ten explicit criteria rather than intuition.

You will also learn to monitor a position systematically after entry: to distinguish institutional distribution from ordinary price noise, to recognize the psychological signals that typically accompany a cycle top, and to relate daily news to the assumptions behind each thesis.

The course closes with case studies analyzed at past decision dates, so you can see the framework applied under the conditions and uncertainty that existed at the time.

The course is organized in three parts that build on each other.

* The first part lays the methodological foundation:

what makes a bottleneck, and how ten criteria organized in three layers (Moat, Timing and Risk) turn that question into a verifiable assessment. Each criterion returns a PASS, FAIL or CONDITIONAL verdict with a confidence level, and the verdicts roll up into Moat and Timing scores.

* The second part covers the risk-management suite that runs every week after entry:

the three-tier Technical Status model, the Peak Psychology module and its three signals, the Daily Briefing, and the monthly Bubble Signal Checklist. You will learn to read their outputs and to understand why each signal is weighted as it is, without any involvement in coding.

* The third part investigates practical application:

it applies the framework to case studies at past decision dates, including the full monitoring history that followed each initial assessment.

1 Identifying a Bottleneck: The Initial Assessment

1.1 What Makes a Bottleneck: Ecosystems, Chokepoints and Pricing Power
1.2 The Moat Layer
1.3 The Timing Layer
1.4 The Risk Layer
1.5 Verdicts, Confidence Levels and the Moat and Timing Scores
1.6 The Verdict Table and the Internal Grade
1.7 The Analyst’s Role: Reviewing and Adjudicating the Evidence

2 Monitoring a Position: The Risk-Management Suite

2.1 Technical Status: Institutional Selling, Structural Confirmation and Price Action
2.2 Peak Psychology: Good-News Rejection, Estimate Dispersion and Options Positioning
2.3 The Daily Briefing: Connecting News to Thesis Assumptions
2.4 The Bubble Signal Checklist: Systemic Risk Across an Ecosystem
2.5 Persistent History: Reading a Position’s Trajectory
2.6 Operating the BottleneckLens Engine

3 Case Studies at Past Decision Dates

3.1 The AI Infrastructure Chain: Compute, Memory, Optics, Power and Cooling
3.2 Case study 1 – e.g., TSMC
3.3 Case study 2 – e.g., MSFT
3.4 Case study 3 – e.g., GOOGL

Concepts are built from first principles and developed through ecosystem analysis, monitoring signals and real-company examples, with emphasis on the reasoning behind each criterion and signal rather than mechanical checklist use.

No formal evaluation; self-assessment is encouraged throughout the course through worked examples and case-study exercises.

Participants receive a certificate of completion from Your Science, a ministerially authorized training provider, confirming participation in bottleneck analysis, ecosystem risk assessment and analytical engine operation.

Participants receive over 100 pages of lecture notes containing structured explanations, worked examples, analytical templates and the complete case-study material used during the course.

The training is delivered live via Microsoft Teams webinars and requires no software installation for participants. Each session combines structured instructor-led explanations with practical demonstrations. Participants may also submit questions by email and receive individual responses by email or, where useful, by phone.

The next session will be scheduled in consultation with participants once the first five enrollments are confirmed.

Registration is now open at our website.

The course fee of €449 includes the full 16-hour webinar programme, the training materials, and a complimentary 30-day or 30-run licence to the cloud-based AuditAlpha™ stock analysis engine. The licence will be activated once the platform goes live, currently planned for Q1 2027.