A book about building a more connected, explainable, and accountable investment-research process—using machines to expand what we can observe while keeping human judgment at the center of the decision.
How a practical experiment in broader market analysis became an exploration of evidence, accountability, connected research tools, and the evolving partnership between people and machines.
Read the Introduction →
How financial statements, quantitative models, macro context, event intelligence, and persistent company knowledge become an evidence-based investment thesis.
Read Chapter One →
How AMAAS preserves forecasts, collects independent market evidence, and measures real-world outcomes so trust can be earned rather than assumed.
Read Chapter Two →
How AMAAS separates company intelligence from systemic economic risk and connects global forces to portfolio outcomes.
Read Chapter Three →
What AMAAS learned from its first accountability results—and why the model remained frozen while the experiment improved.
Read Chapter Four →
Why accountability begins by defining which securities belong in the experiment and establishing a disciplined, investable primary research universe.
Read Chapter Five →
How simpler navigation, personalized screening, portfolio simulation, and continuous company tracking turn institutional-grade research into a usable decision process.
Read Chapter Six →
How infrastructure, compute, financial data, quantitative analytics, AI, application services, and distribution operate as one integrated financial-technology stack.
Read Chapter Seven →
How ordinary language becomes structured financial computation—connecting research, comparison, historical knowledge, screening, and portfolio construction.
Read Chapter Eight →
What changed when AMAAS expanded accountability beyond Composite Score to test Momentum, Regime-Aware, and multi-signal selection against the same realized market evidence.
Read Chapter Nine →