Practical guidance for modern portfolio managers
A deep dive into the mathematical specification and architectural design of the Nagare Financial Engine, featuring our Yearly Cohort-Based Model and MOIC-First Calibration.
A practitioner-level overview of how Nagare models private funds, public holdings, fees, carry, and Monte Carlo—written for quants and investment analysts.
A practical tour of how Portfolio Companion reads GP reports, updates actuals, and answers portfolio questions—on top of your existing Nagare data.
Most PE software gets carry waterfall wrong. We don't. Here's why accuracy matters and what we fixed in our October 2025 updates.
Stop guessing market parameters for Monte Carlo simulations. Our new Market Data Platform pulls real data from 28 instruments to make your projections more accurate, reproducible, and defensible.
Most tools track funds. Nagare tracks portfolio companies with competing risks analysis, cohort analysis by vintage year, and data-driven insights for better GP selection and fundraising.
Manual data entry is error-prone and slow. Here's how Portfolio Companion reads PDFs, extracts actuals, and updates funds in 5 minutes—not 2 hours.
UHNW investors have mixed portfolios. Siloed tools miss correlation risk. Here's how to track private funds + public holdings + market data in one place.
Stop guessing Monte Carlo parameters. Use historical market data to calibrate alpha, beta, and volatility for realistic private fund simulations.
Carry distributions work completely differently under European (whole fund) vs American (deal-by-deal) waterfall models. Here's how Nagare handles both accurately.
When decisions made today affect wealth 50 years from now, institutional memory isn't a nice-to-have—it's your competitive advantage. Here's how to build it.
Why private fund returns aren't independent of markets—and how to model alpha + beta correctly for realistic P10/P50/P90 outcomes.
When Monte Carlo simulations drive $50M commitment decisions, accuracy matters. Here's how we ensure our engine is statistically sound, benchmarked against industry data, and grounded in real market series from the Nagare Market Data Platform.
You just ran Monte Carlo. Now you're staring at P10, P50, P90. What do these numbers actually tell you? And more importantly, which one should drive your decision?
Most Monte Carlo tools assume private markets are independent of public markets. That's wrong—and it underestimates your risk. Here's why market correlation matters for portfolio forecasting.
Your Excel model says NAV will be $685M. But what's the probability? What if markets crash? What's the downside? Monte Carlo gives you answers you can actually use.
Your Q1 marks arrived in October showing March data. You're making $50M commitment decisions with 9-month-old numbers. Here's why that's broken—and how forward-looking family offices are fixing it.
Family offices manage sensitive financial data for high-net-worth individuals. Here's how to balance security, compliance, and operational efficiency without enterprise-level overhead.
Should you run 10,000 simulations or stick with three scenarios? It depends on what you're trying to answer. A practical guide to portfolio modeling.
Technology adoption in family offices lags years behind corporations. It's not about money or technical capability—it's about trust and having more to lose than gain.
Your senior partner remembers why you passed on that fund in 2018. But when they retire, that knowledge walks out the door. Here's how to preserve what you learn.
Your Q2 statement arrives in October showing March data. You need to make decisions today. Here's how to bridge the gap between stale marks and current reality.
After managing multiple private fund commitments, you'll face these three questions repeatedly. Here's how to build a model that actually answers them.
After building a portfolio across 15+ private fund commitments, I kept hitting the same wall: I couldn't see where we were heading. Here's why I needed more than spreadsheets.
Stop waiting for quarterly reports. Get real-time intelligence.