FROM EQUATIONS TO CAPITAL

The Physics of Capital

Foundations & Portfolio Theory

The first volume establishes the mathematical and conceptual foundations required for institutional-grade investment decision-making. From the time value of money through advanced portfolio optimization, each chapter builds a rigorous framework with Python implementations and real-world applications.

16 Chapters
450+ Pages
80+ Code Examples
4 Parts
Preview Reader Buy Volume I - $95
Volume I Cover

Table of Contents

16 chapters across 4 parts, building from foundations to advanced applications

PART I

Foundations of Corporate Finance

I

The Language of Capital

Time value of money, NPV, IRR, and the foundations of valuation

32 pages
PART II

Risk and Return

II

Risk, Return, and the Historical Record

Empirical analysis of asset class returns and risk measurement

28 pages III

Portfolio Theory: The Markowitz Revolution

Mean-variance optimization and the efficient frontier

36 pages IV

Capital Asset Pricing Model

Systematic risk, beta, and equilibrium pricing

30 pages
PART III

Factor Models and Market Efficiency

V

Arbitrage Pricing Theory

Multi-factor models and the law of one price

34 pages VI

Empirical Factor Models

Fama-French, momentum, and modern factor investing

32 pages VII

Market Efficiency and Anomalies

EMH, behavioral finance, and tradeable inefficiencies

28 pages VIII

Fixed Income Fundamentals

Bond mathematics, yield curves, and duration

30 pages
PART IV

Advanced Portfolio Theory

IX

Portfolio Optimization in Practice

Constraints, transaction costs, and robust optimization

36 pages X

Risk Management Frameworks

VaR, CVaR, stress testing, and scenario analysis

34 pages XI

Performance Attribution

Return decomposition, alpha generation, and benchmarking

28 pages XII

Dynamic Asset Allocation

Regime switching, tactical allocation, and rebalancing

32 pages XIII

Alternative Investments

Private equity, real estate, and hedge fund strategies

30 pages XIV

Quantitative Equity Strategies

Factor construction, backtesting, and live trading

36 pages XV

Machine Learning in Finance

Supervised learning, feature engineering, and model validation

34 pages XVI

Building Production Systems

From research to deployment: architecture and governance

28 pages

Interactive Companion Notebooks

Every chapter includes production-ready Python implementations

Jupyter Notebooks

80+ executable notebooks with step-by-step implementations of every model and technique.

Decision Lab Integration

Launch notebooks directly into Decision Lab for interactive exploration and scenario analysis.

BUILD Exercises

Hands-on exercises that challenge you to build complete systems from first principles.

Explore Decision Lab

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