Annotations (12)
“Submergence equals drawdown plus recovery. When you fall off your surfboard and get slammed, the risk is not how deep you go but how long until you surface and get air. We have tools for drawdowns, value at risk, volatility. We do not think enough about the shape of recoveries. Robust portfolios seek to immunize against shocks through hedges and derivatives. Resilient portfolios absorb shocks and recover. We do not build buildings, ecosystems, or train soldiers to be immune to hits.”— Ashby Monk
p. 20
Economics & Markets · Biology, Ecology & Systems · Philosophy & Reasoning
DUR_ENDURING
Resilience beats robustness for long-term systems
“Personal navigation shifted from process-based to data-driven decision making. Previously, the co-pilot who knew shortcuts was valuable because they owned local knowledge. Now Google Maps knows all shortcuts, knows which In-N-Out has the shortest line, and guides you not just to the location but to your actual goal. The shortcut expert is obsolete. Portfolio navigation is undergoing the same transformation.”— Ashby Monk
p. 7
Technology & Engineering · Strategy & Decision Making
DUR_ENDURING
Data platforms replace local knowledge experts
“Technology allows you to hold less cash. That is the unlock. Instead of overdiversifying and using cash as a buffer for uncertainty about pacing and unfunded commitments, you model cash flows with precision. Run scenarios. Optimize the path. Drop from 5% cash to 3%. The one basis point invested in technology delivers one percentage point in return by deploying that freed cash into higher-yielding assets.”— Ashby Monk
p. 8
Operations & Execution · Technology & Engineering · Economics & Markets
DUR_ENDURING
Tech precision reduces cash drag, boosts returns
“100% of your performance and asset allocation is a function of your organizational capabilities. You can't set asset allocation in a vacuum. If you want illiquid private equity or venture capital, you need process for managing commitments, which draw down and return capital over time. You need strong technology to model that out. Asset allocation must follow organizational capability, not precede it.”— Ashby Monk
p. 4
Strategy & Decision Making · Operations & Execution · Leadership & Management
DUR_ENDURING
Asset allocation derives from capability, not vice versa
“The average institutional investor spends one to two basis points of AUM on their tech stack. That tech stack provides the GPS for their portfolio: what do I own, where am I exposed. During SVB, everyone pulled up spreadsheets to figure out exposure. The goal is a dashboard that instantly reveals positioning. Once you have GPS, you can build optimization engines on top. Just as GPS enabled Uber, Lyft, DoorDash, portfolio GPS will enable new verticals in investing.”— Ashby Monk
p. 8
Technology & Engineering · Operations & Execution · Strategy & Decision Making
DUR_CONTEXTUAL
Portfolio GPS unlocks optimization layer
“The production function has four irreducible components: capital, people, process, and information. Capital comes with encumbrances like short-term liabilities or net zero commitments. People are who execute. Process determines how they're empowered, whether through delegation frameworks or investment committees. Information is where and how they gather intelligence. These four components combine to generate performance.”— Ashby Monk
p. 3
Strategy & Decision Making · Leadership & Management · Operations & Execution
DUR_ENDURING
Four inputs drive all investment performance
“Innovation requires organizational space. Efficiency and innovation are different. Pension employees are lectured to be efficient, to act as fiduciaries who cannot spend money wastefully. But innovation is messy. Innovation and failure go together. You cannot run a just-in-time process and expect changes at the last second to arrive on time. You need safe spaces for failure and new incentives that do not create career risk for experimentation.”— Ashby Monk
p. 14
Leadership & Management · Creativity & Innovation · Psychology & Behavior
DUR_ENDURING
Innovation needs slack, not just-in-time processes
“Governance budget must align with risk budget. You need a board with time, capacity, and skills to understand the portfolio and resource the team appropriately. If you take on high risk in your risk budget but your board meets quarterly with limited skills, the governance budget cannot support the risk budget. The mismatch creates failure.”— Ashby Monk
p. 5
Leadership & Management · Strategy & Decision Making
DUR_ENDURING
Align board capacity with portfolio risk
“Crises drive innovation in this industry. The 2001-2003 perfect storm crisis produced liability-driven investing and asset-liability modeling. The 2008 crisis produced factor-based asset allocation. We moved from products that correlate to identifying core factors that drive return and finding diversification there. Technology will reveal mini-crises inside funds, which are opportunities to make change. We need crises because these organizations are monopolistic, conservative, and slow-moving.”— Ashby Monk
p. 13
History & Geopolitics · Creativity & Innovation · Economics & Markets
DUR_ENDURING
Crisis unlocks change in monopolistic systems
“ESG ratings are like Big Macs: easy to get, cheap, taste good, but not clear they make you healthier and you do not know what is inside. Credit ratings work because they associate with probability of default. What probability do ESG ratings provide? A Big Mac cannot be chopped up into meals for an equity analyst. ESG needs facts instead of ratings. Environmental footprint data. Workforce loyalty metrics. Granular factors an analyst can use to make bottom-up decisions.”— Ashby Monk
p. 18
Economics & Markets · Philosophy & Reasoning
DUR_CONTEXTUAL
ESG ratings obscure, not illuminate
“Data are signals. Information is data in context. Knowledge is enough information to generalize. Intelligence is knowledge applied to your life and portfolio. Academics collect small data samples, clean them, study them, publish about them. Then others use those findings to avoid collecting the data themselves. That is over-indexing on knowledge. With data infrastructure, you calibrate information and knowledge directly and make better decisions.”— Ashby Monk
p. 19
Philosophy & Reasoning · Technology & Engineering
DUR_ENDURING
Four-tier knowledge hierarchy
“Sustainability factors, employee satisfaction, environmental footprint, are associated with recovery trajectories. Companies with better ESG profiles recover faster from drawdowns. It is strange that just as we are proving how ESG helps long-term investors drive outperformance, state treasurers are banning ESG integration. We will call it long-term risk management instead.”— Ashby Monk
p. 21
Economics & Markets · History & Geopolitics
DUR_CONTEXTUAL
ESG factors predict recovery speed
Frameworks (3)
The Investor Identity Production Function
Four Irreducible Components of Investment Performance
Every investment organization's performance is a function of four components: capital (with its encumbrances), people (who execute), process (how they are empowered), and information (where they gather intelligence). Asset allocation must follow organizational capability, not precede it. Organizations should assess their capacity across these four dimensions before committing to strategies that require capabilities they lack.
Components
- Assess Capital Constraints
- Evaluate People Capability
- Map Process Infrastructure
- Audit Information Access
- Align Strategy with Capability
Prerequisites
- Honest assessment of current state
- Board buy-in for capability-first approach
Success Indicators
- Asset allocation matches actual execution capability
- Reduced operational failures
- Higher confidence in strategy execution
Failure Modes
- Skipping the assessment and copying peer strategies
- Building process without the people to execute it
- Investing in technology without training people to use it
The Knowledge Hierarchy
From Signal to Intelligence
Information value follows a four-tier hierarchy. Data are raw signals. Information is data in context. Knowledge is sufficient information to generalize patterns. Intelligence is knowledge applied to specific decisions in your portfolio and life. Organizations often over-index on knowledge (expert opinions, ratings) when they should invest in data infrastructure that enables direct intelligence generation.
Components
- Data (Signals)
- Information (Data in Context)
- Knowledge (Generalizable Patterns)
- Intelligence (Applied Knowledge)
Prerequisites
- Understanding of your organization's information flow
Success Indicators
- Clear identification of what tier information exists at
- Reduced reliance on aggregated ratings
- Faster decision-making from better data infrastructure
Failure Modes
- Skipping tiers (trying to jump from data to intelligence)
- Over-relying on third-party knowledge without building data capability
Submergence: Resilience Over Robustness
Measuring Risk as Drawdown Plus Recovery
Risk should be measured as submergence: the time from when an asset dips below target until it recovers, not just the depth of the drawdown. Robust portfolios seek immunity to shocks through hedges and derivatives. Resilient portfolios accept that shocks will hit and optimize for fast recovery. Buildings, ecosystems, and soldiers are built for resilience, not robustness. Portfolios should follow the same logic. Assets with superior recovery profiles (often driven by sustainability factors) deliver better long-term performance.
Components
- Measure Submergence, Not Just Drawdown
- Identify Recovery Factors
- Optimize for Resilience, Not Robustness
- Diversify by Submergence Profile
Prerequisites
- Historical performance data
- Factor data on portfolio holdings
- Ability to model recovery scenarios
Success Indicators
- Portfolio demonstrates faster recovery from market shocks
- Reduced need for expensive hedging
- Improved long-term risk-adjusted returns
Failure Modes
- Measuring only drawdown depth
- Ignoring factor data that predicts recovery
- Confusing resilience with accepting permanent losses
Mental Models (7)
Production Function
Systems ThinkingA production function describes how inputs combine to produce outputs. In economics, capital and lab
In Practice: Ashby Monk applying production function thinking to institutional investor organizations
Demonstrated by Leg-am-001
Bottleneck Theory
Systems ThinkingSystem throughput is constrained by its narrowest point. In manufacturing, the slowest station limit
In Practice: Applied to governance as a bottleneck: board capacity constrains portfolio risk-taking
Demonstrated by Leg-am-001
Platform Effects
Strategic ThinkingPlatforms become more valuable as more users join and contribute data. GPS becomes more useful as mo
In Practice: Explaining how GPS data aggregation and portfolio data aggregation follow the same platform dynamic
Demonstrated by Leg-am-001
Opportunity Cost
EconomicsThe cost of any decision is what you give up by not choosing the next best alternative. Holding cash has an opportunity cost equal to the return you could earn by investing it. Overestimating risk has an opportunity cost equal to the return you forego. Quantifying opportunity cost reveals hidden expenses in conservative decision-making.
In Practice: Applied to cash drag: the opportunity cost of holding 5% cash vs 3% cash is substantial over decades
Demonstrated by Leg-am-001
Loss Aversion
PsychologyThe pain of losing is psychologically about twice as powerful as the pleasure of gaining.
In Practice: Explaining why pension fund employees avoid innovation
Demonstrated by Leg-am-001
Resilience vs. Robustness
Systems ThinkingRobust systems resist damage by being strong enough to withstand shocks. Resilient systems absorb da
In Practice: Applied to portfolio construction: resilient portfolios that recover fast beat robust portfolios tha
Demonstrated by Leg-am-001
Constraints Liberate
Decision MakingClear constraints force clarity and creativity. Unlimited options create paralysis and poor decisions. The best strategies emerge when you honestly acknowledge what you cannot do. Pretending you have no constraints leads to overextension and failure. Accept the constraint, then optimize within it.
In Practice: Asset allocation should follow organizational capability constraints, not ignore them
Demonstrated by Leg-am-001
Connective Tissue (2)
GPS navigation transition: from local knowledge experts to collective data intelligence
Personal navigation underwent a fundamental shift from process-based to data-driven decision-making. Before GPS and Google Maps, the person who knew shortcuts had value because they possessed local knowledge. Now, Google Maps has indexed the planet and knows not just routes but real-time conditions, traffic, and even which restaurant has the shortest line. The shortcut expert is obsolete. Portfolio navigation is undergoing the same transformation. The shift is from relying on people who have experience (local knowledge) to systems that aggregate data from all market participants (collective intelligence). Just as GPS enabled new businesses (Uber, DoorDash, Lyft), portfolio GPS will enable new investment optimization engines.
Ashby Monk explaining how technology changes the role of expertise in investment organizations, using the GPS-to-Google Maps evolution as an analogy for portfolio technology infrastructure
Big Mac analogy for ESG ratings: cheap, accessible, unclear nutritional value, unknown ingredients
ESG ratings resemble Big Macs: easy to obtain, inexpensive, superficially satisfying, but with unclear actual benefit and opaque composition. You do not know what is inside (potential yoga mat materials), and consuming them may not improve health despite tasting good. ESG ratings fail to provide the actionable granularity that equity analysts need. Credit ratings work because they map to default probability. ESG ratings lack equivalent predictive clarity. The solution is to move from aggregated ratings (Big Macs) to raw facts (ingredients): specific environmental footprint data, workforce loyalty metrics, operational resilience factors. An analyst cannot decompose a Big Mac into useful components; similarly, an ESG rating cannot be decomposed into actionable investment signals.
Ashby Monk critiquing the ESG ratings industry and explaining why aggregated scores are less useful than granular factor data for investment decision-making
Key Figures (4)
Gordon Clark
1 mentionsAcademic researcher on pension fund governance
Academic who has done foundational work on pension fund governance best practices, particularly around board structure and governance budgets
- Developed the concept of governance budget as a constraint on risk-taking
Roger Irwin
1 mentionsGovernance researcher
Keith Ambacher
1 mentionsGovernance researcher
Andrew Ang
1 mentionsAcademic and practitioner in factor-based investing
Glossary (1)
submergence
DOMAIN_JARGONThe period an asset spends below target, from drawdown start to full recovery
“Submergence equals drawdown plus recovery”
Key People (4)
Gordon Clark
Academic researcher who developed governance budget framework
Roger Irwin
Researcher on pension fund governance
Keith Ambacher
Researcher on pension fund governance
Andrew Ang
Academic who developed factor-based asset allocation
Concepts (3)
production function
CL_ECONOMICSEconomic model showing how inputs combine to generate outputs
liability-driven investing
CL_FINANCIALInvestment strategy matching assets to liability duration and characteristics
factor-based asset allocation
CL_FINANCIALPortfolio construction focusing on underlying risk factors rather than asset classes
Synthesis
Synthesis
Migrated from Scholia