Artificial Mathematics II: The PhD of Capital is an advanced finance, strategy, and decision-making reference for readers who want to understand how capital actually moves through the modern world.

Building on the foundation of Volume I, this second volume goes beyond basic financial concepts and moves into the institutional tools used to analyze businesses, value assets, structure deals, manage risk, and make decisions under uncertainty.

This volume includes:

• 15 advanced units covering valuation, markets, M&A, LBOs, investment banking, credit, fixed income, derivatives, portfolio construction, macro finance, behavioral finance, game theory, private equity, venture capital, real estate finance, and AI-era strategy

• Advanced valuation frameworks, including DCF analysis, enterprise value, equity value, terminal value, WACC, FCFF, FCFE, comparable company analysis, precedent transactions, control premiums, and valuation triangulation

• Investment banking and deal analysis, including M&A advisory, accretion/dilution, deal structuring, goodwill, purchase price allocation, sources and uses, and transaction process mechanics

• Private equity and LBO modeling concepts, including MOIC, IRR, debt paydown, EBITDA growth, leverage, return drivers, and LBO candidate characteristics

• Credit and financial statement analysis, including quality of earnings, cash conversion, leverage ratios, interest coverage, debt capacity, and forensic accounting red flags

• Markets and risk management topics, including fixed income, duration, convexity, yield curve strategy, derivatives, options, Greeks, hedging, volatility, and institutional portfolio construction

• AI-era finance and corporate strategy, exploring how artificial intelligence is changing analysis, competition, decision-making, capital allocation, and business strategy

• Worked examples and realistic tables that show how formulas and frameworks are applied in practice

• Sensitivity analysis and strategic insights to help readers understand not just the answer, but the assumptions behind the answer

• Key terms and formula explanations throughout the book for quick reference and review

• An advanced formula reference sheet designed for fast study and practical use

• Five full institutional-style case studies covering real-world finance and strategy scenarios

• A 30-day study program that gives readers a structured path through the material

• Practice worksheets and an answer key for active learning instead of passive reading

This is not a motivational finance book. It is a dense, practical, organized learning manual for students, analysts, founders, investors, professionals, and serious independent learners who want to understand the deeper machinery behind money, markets, risk, valuation, and strategy.

Artificial Mathematics II is for anyone who wants to think more clearly about capital — how it is measured, how it is priced, how it is protected, and how it shapes decisions in the AI era.

Lucas Johnson

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