The Analyst's Handbook

Sifter's textbook for student investors and the funds they run: 31 chapters on measuring a portfolio, testing it, researching a stock, setting risk limits, and building a record of dated calls.

By Matt Lang and Bree Lang · About the authors

Free to read, no account needed.

Foundations

  1. How Sifter sees your money. A guided tour of the Portfolio Overview: where every headline number is.
  2. The ledger: transactions to holdings. Buys, sells, dividends, gifts, and honest mistakes: how a pile of transactions becomes your holdings table.

Measured performance

  1. Time-weighted return. The headline return, and why it ignores your deposits on purpose.
  2. Money-weighted return. The return your dollars actually earned, the XIRR search that finds it, and why it disagrees with TWR on purpose.
  3. Volatility, Sharpe, Sortino, drawdown. Risk per unit of bumpiness, and the cost of only counting the bad bumps.
  4. Beta, alpha, and active return. How much of your return was the market, and how much was actually you.
  5. Tracking error and information ratio. How far you deviate from the benchmark, and whether the deviation paid.
  6. Attribution: which holdings earned the return. CTE, MCTE, and the Brinson split of allocation versus selection.
  7. Correlation, diversification, and factor tilts. What diversification actually is, one cell of the correlation matrix at a time, why forty holdings can carry the risk of one, and the factor lens that identifies the bet.
  8. Sleeves. Running a fund of sub-funds: drift, contribution, and TE share.

Models and simulations

  1. Backtest: the synthetic portfolio. What the Simulate tab actually builds, and how it differs from your real book.
  2. Monte Carlo: the fan chart. A thousand invented futures, one fan chart, and the half-sigma-squared tax.
  3. Stress tests, VaR, and CVaR. What a bad day costs, three ways of estimating it, and the fine print.
  4. The optimizer. What mean-variance optimization finds in your data, and what it trades away.

Research

  1. The company snapshot. The wall of stats on the ticker page, and which numbers move without the company doing a thing.
  2. DCF: cash flows and assumptions. Five years of cash flow, a terminal value, and a 5x5 grid of sensitivities.
  3. Comps and Compare. Relative valuation: what cheaper-than-peers does and does not mean.
  4. Quality scores. Earnings Quality and Capital Allocation: useful heuristics with limits.
  5. Charts and technicals. EMA, RSI, MACD, Bollinger: what the indicators compute.
  6. The screener and macro context. Filters as inequalities, and reading the yield curve before you buy.

Risk and decisions

  1. Risk limits. A policy is a set of lines written down before the quarter. How Risk Flags checks a book and each holding against them, and why a line moves only on the record.
  2. What to do with the numbers. One row per metric: the question it answers, the question people wrongly ask it, and the conversation it starts.

The record

  1. What the record is for. Why a transcript stopped separating people, what a record of frozen claims does instead, and how much of this is proven.
  2. The Sprint: setting your confidence. A two-week call is nearly a coin flip, and a hundred of them at a stated confidence are not: try, see the evidence, update by band, and watch the card dial in.
  3. Can we do better? The Sprint workbench. For the day three cards are not enough: pick your own measures, read the pictures behind the numbers, weigh the column, and search the stock's history for the days when your conditions held at once.
  4. The Spread: one stock against another. Take the market out of the claim: one long, one short, one month, and a confidence that only selection can earn.
  5. The Quarter: knowing your own risk. Build a book to a brief, say what its risk will be before the quarter runs, and find out whether you knew what you built.
  6. The Book: the long record. A mandate in one line, a portfolio run for months at real prices, and a reason on every trade. The exercise a reader weighs most.
  7. Calibration. Brier, base rates, and the arithmetic of being right at the rate you claimed.
  8. What the card says. Reading a Sifter Card the way a recruiter reads it, and what it refuses to claim.

Capstone

  1. The full loop. One real holding, taken from ledger to thesis with everything you now know.

About the authors

Matt Lang

Full Teaching Professor, UC Santa Barbara · Lecturer, Stanford University · Co-Founder, Sifter · LinkedIn

Matt Lang is a Full Teaching Professor at UC Santa Barbara and is spending the 2026-2027 academic year as a lecturer at the Initiative for Financial Decision-Making at the Stanford Graduate School of Business. He has spent more than sixteen years teaching economics to undergraduate and MBA students at Stanford University, UC Santa Barbara, UC Riverside, and Xavier University. In 2025, he became the faculty advisor for the UC Santa Barbara Student Investment Fund. He co-created the gamified learning platform Virtual Econ, authored an interactive Introduction to Macroeconomics textbook, developed AI-based discussion activities for the classroom, and created UC Riverside's Economics of Financial Investments course. He holds a Ph.D. in Economics from UC Santa Barbara and a master's from the London School of Economics.

Bree Lang

Full Teaching Professor, UC Santa Barbara · Co-Founder, Sifter · LinkedIn

Bree Lang is a Teaching Professor in the Department of Economics at UC Santa Barbara. She earned a Ph.D. in Economics from UCSB and previously held positions at Xavier University and UC Riverside. Her research background spans urban economics and public finance, with work on subsidized housing and taxation. Bree is driven to make economics learning more engaging, using technology to turn abstract concepts into practical skills. Most recently, she co-created Sifter, a student investment platform that gives student-managed funds real-time portfolio tools, extending a career-long effort to bring meaningful experience into economics education.

Sifter is the authors' own company. It is not affiliated with or endorsed by the universities where they teach.