Buy Risk

Educational content only — not financial, tax, or investment advice. In active development — how it's built and checked

About

Anyone can learn finance and investing. Much of it is also deeply counterintuitive.

Buy Risk exists because most investing content is either trying to sell you something or assumes you already have an MBA. We think the core ideas are genuinely learnable by anyone willing to spend an afternoon with them, and that they're far more simple and interesting than the hype makes them sound.

A fair note on depth: this is an investing curriculum more than a personal-finance one. Money Basics starts from the true beginning — budgeting, buffers, debt — but most of the site goes well past it, into territory (factor models, withdrawal math, tax mechanics) usually reserved for textbooks. Every term gets defined before it's used; bring curiosity and the tools do the rest.

What we believe

The name is our thesis, with one important qualification: expected return is compensation for bearing risk that cannot be diversified away. Not all risk is paid for.

The risk that everyone holds at once — the market falling, inflation arriving, a recession — cannot be avoided by spreading your money around, because it hits everything at the same time. Since someone must hold it, it carries an expected premium. That is systematic, or compensated, risk.

The risk attached to one company — a failed product, a fraud, a lost lawsuit — is different. It can be diversified away, essentially for free, by owning many companies instead of a few. Because anyone can shed it at no cost, markets don't pay a premium for holding it. That is idiosyncratic, or uncompensated, risk. Concentrating in a handful of stocks raises how much your portfolio moves without raising what you should expect to earn. Some concentrated portfolios do beat the market — but that is the spread of outcomes widening, not a premium being collected, and it cuts both ways.

So the practical form of the thesis is narrower than the slogan: you earn a premium by holding the uncertainty that can't be diversified away, patiently and cheaply, and by not paying for the uncertainty that can. Our diversification and risk-pricing tools show both halves in motion.

How we teach

  • Plain language first. If a concept needs jargon, we define it before we use it.
  • Show, don't just tell. Our tools let you manipulate the variables yourself, because intuition comes from playing, not reading.
  • Evidence over stories. We lean on the boring, well-supported findings of finance (diversification, compounding, and the cost of fees), not hot tips.

How this site is built

Buy Risk is a living project, not a finished textbook. Tools are added and revised often, and the newest ones have had the least wear. If you're citing or teaching from something here, it's worth confirming it still says what you expect it to.

Where the numbers come from. Every historical figure comes from real data rather than invented numbers (the few deliberately synthetic tabs say so): long-run US returns from Aswath Damodaran, daily market and factor returns from the Kenneth French data library, valuations from Robert Shiller, fund data from CRSP, benefit formulas and life tables from the Social Security Administration, and tax parameters from the IRS revenue procedures. Each source is turned into the figures the site needs by a script kept in the repository, so any number here traces back to a published dataset. Licensed data is used under license, and only derived aggregates are ever published.

How the calculations are checked. Wherever an authoritative reference exists, the engines are tested against it. The Social Security calculator reproduces the SSA's own published benefit examples to the dime. The federal tax engine is checked against hand-computed cases and cross-examined against an independent spreadsheet implementation of the same law. Those tests live in the repository and are run before changes ship. The simpler illustrative tools are ordinary arithmetic you can read for yourself.

How it's written. The tools and much of the writing here are produced with heavy use of AI assistance, directed, reviewed, and verified by a human. That's why the checks above exist, and why the full source is public: the claim isn't "trust us," it's "check us."

How it's licensed. The source is public so it can be checked, not so it can be resold. The code is under the PolyForm Noncommercial licence and the writing under CC BY-NC-SA 4.0, which together mean you may read, fork, adapt, and above all teach from any of this, with credit, for free. Classroom and institutional use is explicitly covered — if you're a teacher, this licence was chosen with you in mind. What isn't permitted is selling it, running it with advertising, or putting it behind a paywall. The datasets the tools run on are a separate case: they're aggregates derived from data we hold under licence, so we can publish them here but cannot pass the right to republish them on to anyone else.

Found a mistake? Please tell us. Corrections take priority over new features, and if you're using this in a classroom we'd genuinely like to know: buyriskHQ@gmail.com.

An important note

Everything on Buy Risk is educational. It is not personalized financial advice, and we don't know your situation. Use it to understand how investing works, then make your own decisions, or talk to a licensed professional who can account for your specific circumstances.

If you do want personal guidance, the same idea that runs through this site applies to the advice itself: understand what you're paying and what you get for it. Advisors charge in different ways (a flat or hourly fee, a percentage of your assets, or commissions on products), and any of them can be worth it as long as the cost is clear to you. A fee-only, advice-only advisor is one transparent, low-conflict place to start; directories like the Advice-Only Network list them.

Ready to dig in? Jump straight to the compound growth explorer, or browse the full Portfolio Playground.

Get in touch

Questions, corrections, or an idea for a tool? Email us at buyriskHQ@gmail.com.

Data & research

Buy Risk is built on the primary sources of evidence-based finance. Every figure on the site traces back to peer-reviewed research or a public dataset; the works below anchor the tools, grouped by theme.

Portfolio theory

  • Markowitz, H. (1952). “Portfolio Selection.” The Journal of Finance 7(1): 77–91. The founding paper of modern portfolio theory.
  • Roy, A. D. (1952). “Safety First and the Holding of Assets.” Econometrica 20(3): 431–449.
  • Tobin, J. (1958). “Liquidity Preference as Behavior Towards Risk.” The Review of Economic Studies 25(2): 65–86. Introduces the separation theorem.
  • Perold, A. F., & Sharpe, W. F. (1988). “Dynamic Strategies for Asset Allocation.” Financial Analysts Journal 44(1): 16–27. The classic taxonomy of buy-and-hold, constant-mix (rebalancing) and portfolio-insurance strategies. Rebalancing is concave: it does best in volatile but trendless markets and lags in trending ones — and because it buys more as prices fall, it offers less downside protection than buy-and-hold, not more.
  • Perold, A. F., & Schulman, E. C. (1988). “The Free Lunch in Currency Hedging: Implications for Investment Policy and Performance Standards.” Financial Analysts Journal 44(3): 45–50. Argues currency hedging has ~zero long-run expected return, so it lowers risk for free.
  • Dammon, R. M., Spatt, C. S., & Zhang, H. H. (2004). “Optimal Asset Location and Allocation with Taxable and Tax-Deferred Investing.” The Journal of Finance 59(3): 999–1037. Formalizes asset location: hold the more heavily-taxed asset (bonds) in the tax-deferred account.

Asset pricing & the CAPM

  • Treynor, J. L. (1962). “Toward a Theory of Market Value of Risky Assets.” Unpublished manuscript, 1962; published in R. A. Korajczyk (ed.), Asset Pricing and Portfolio Performance, Risk Books, 1999, pp. 15–22.
  • Sharpe, W. F. (1964). “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk.” The Journal of Finance 19(3): 425–442.
  • Lintner, J. (1965). “The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets.” The Review of Economics and Statistics 47(1): 13–37.
  • Mossin, J. (1966). “Equilibrium in a Capital Asset Market.” Econometrica 34(4): 768–783.
  • Fama, E. F., & MacBeth, J. D. (1973). “Risk, Return, and Equilibrium: Empirical Tests.” Journal of Political Economy 81(3): 607–636.
  • Black, F., & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy 81(3): 637–654. The Black–Scholes option-pricing formula.
  • Merton, R. C. (1973). “Theory of Rational Option Pricing.” The Bell Journal of Economics and Management Science 4(1): 141–183.

Factor models

  • Fama, E. F., & French, K. R. (1992). “The Cross-Section of Expected Stock Returns.” The Journal of Finance 47(2): 427–465.
  • Fama, E. F., & French, K. R. (1993). “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33(1): 3–56. The three-factor model.
  • Fama, E. F., & French, K. R. (2015). “A Five-Factor Asset Pricing Model.” Journal of Financial Economics 116(1): 1–22.
  • Jegadeesh, N., & Titman, S. (1993). “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” The Journal of Finance 48(1): 65–91. Documents the momentum effect.
  • McLean, R. D., & Pontiff, J. (2016). “Does Academic Research Destroy Stock Return Predictability?” The Journal of Finance 71(1): 5–32. Across 97 published predictors, returns run ~26% lower out of sample and ~58% lower after publication — the reason historical factor premia are upper bounds, not entitlements.
  • Carhart, M. M. (1997). “On Persistence in Mutual Fund Performance.” The Journal of Finance 52(1): 57–82. Adds momentum as a fourth factor.

Market efficiency & active management

  • Dickson, J. M., & Shoven, J. B. (1995). “Taxation and Mutual Funds: An Investor Perspective.” Tax Policy and the Economy 9: 151–180. Quantifies how fund turnover and distributions erode after-tax returns.
  • Samuelson, P. A. (1965). “Proof That Properly Anticipated Prices Fluctuate Randomly.” Industrial Management Review 6(2): 41–49.
  • Fama, E. F. (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance 25(2): 383–417.
  • Fama, E. F. (1991). “Efficient Capital Markets: II.” The Journal of Finance 46(5): 1575–1617.
  • Grossman, S. J., & Stiglitz, J. E. (1980). “On the Impossibility of Informationally Efficient Markets.” The American Economic Review 70(3): 393–408.
  • Jensen, M. C. (1968). “The Performance of Mutual Funds in the Period 1945–1964.” The Journal of Finance 23(2): 389–416.
  • Sharpe, W. F. (1966). “Mutual Fund Performance.” The Journal of Business 39(1): 119–138. Introduces the reward-to-variability (Sharpe) ratio.
  • Miller, R. M. (2007). “Measuring the True Cost of Active Management by Mutual Funds.” Journal of Investment Management 5(1): 29–49. The 'fee on the active slice' arithmetic: a fund's expense ratio, charged on the whole portfolio, is a far steeper fee on the part that actually differs from the index.
  • Cremers, M., & Petajisto, A. (2009). “How Active Is Your Fund Manager? A New Measure That Predicts Performance.” The Review of Financial Studies 22(9): 3329–3365. Introduced Active Share — the fraction of holdings that differ from the benchmark. Funds with the highest Active Share beat their benchmarks; closet indexers (low Active Share, active fees) lagged.
  • Petajisto, A. (2013). “Active Share and Mutual Fund Performance.” Financial Analysts Journal 69(4): 73–93. Documents the rise of closet indexing (roughly a third of US equity fund assets by 2009). The 'fee on the active slice' arithmetic the tool uses follows Miller (2007).
  • Sharpe, W. F. (1991). “The Arithmetic of Active Management.” Financial Analysts Journal 47(1): 7–9.
  • Fama, E. F., & French, K. R. (2010). “Luck versus Skill in the Cross-Section of Mutual Fund Returns.” The Journal of Finance 65(5): 1915–1947.
  • Christoffersen, S. E. K., Evans, R., & Musto, D. K. (2013). “What Do Consumers' Fund Flows Maximize? Evidence from Their Brokers' Incentives.” The Journal of Finance 68(1): 201–235. Established the standard construction of monthly fund flows from Form N-SAR Item 28 (new sales minus redemptions), the convention the Watch-the-Money tab follows.
  • Malkiel, B. G. (1973). A Random Walk Down Wall Street. W. W. Norton & Company.

Behavioral finance

  • Kahneman, D., & Tversky, A. (1979). “Prospect Theory: An Analysis of Decision under Risk.” Econometrica 47(2): 263–291. The foundation of behavioral finance; losses loom larger than equivalent gains.
  • Tversky, A., & Kahneman, D. (1974). “Judgment under Uncertainty: Heuristics and Biases.” Science 185(4157): 1124–1131. Anchoring, availability, and representativeness — including the wheel-of-fortune anchoring experiment the arcade's 'wheel' game recreates.
  • Tversky, A., & Kahneman, D. (1981). “The Framing of Decisions and the Psychology of Choice.” Science 211(4481): 453–458. Identical outcomes, opposite choices when worded as gains vs losses — the design behind the arcade's framing game.
  • Shefrin, H., & Statman, M. (1985). “The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence.” Journal of Finance 40(3): 777–790. Named the disposition effect the arcade's 'Sell something' game measures.
  • Odean, T. (1998). “Are Investors Reluctant to Realize Their Losses?” Journal of Finance 53(5): 1775–1798. 10,000 brokerage accounts: investors realize gains far more readily than losses, and the winners they sell go on to beat the losers they keep.
  • Arkes, H. R., & Blumer, C. (1985). “The Psychology of Sunk Cost.” Organizational Behavior and Human Decision Processes 35(1): 124–140. Money already spent keeps voting on decisions it can't affect — the arcade's sunk-cost vignettes.
  • Baron, J., & Hershey, J. C. (1988). “Outcome Bias in Decision Evaluation.” Journal of Personality and Social Psychology 54(4): 569–579. Identical decisions judged differently by how the dice landed — the arcade's 'Good call?' game.
  • Alpert, M., & Raiffa, H. (1982). “A Progress Report on the Training of Probability Assessors.” in Kahneman, Slovic & Tversky (eds.), Judgment under Uncertainty. The confidence-interval calibration test (first circulated 1969): asked for ranges they were 98% sure of, subjects' ranges missed the truth over 40% of the time. The '90% sure, right about half the time' version is Russo & Schoemaker (1989).
  • Asch, S. E. (1955). “Opinions and Social Pressure.” Scientific American 193(5): 31–35. The line-length conformity experiments: 75% of subjects denied their own eyes at least once to agree with a group — the arcade's 'crowd' game.
  • Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades.” Journal of Political Economy 100(5): 992–1026. How rational copying snowballs into cascades and manias.
  • Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1990). “Experimental Tests of the Endowment Effect and the Coase Theorem.” Journal of Political Economy 98(6): 1325–1348. The coffee-mug experiments: owners demand about twice what buyers will pay for the identical item — the arcade's 'Yours to sell' game.
  • Fischhoff, B. (1975). “Hindsight ≠ Foresight: The Effect of Outcome Knowledge on Judgment under Uncertainty.” Journal of Experimental Psychology: Human Perception and Performance 1(3): 288–299. Creeping determinism: knowing an outcome inflates how predictable it feels — the arcade's 'You knew it all along' game.
  • Barber, B. M., & Odean, T. (2000). “Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors.” The Journal of Finance 55(2): 773–806. The more households traded, the worse they did: the most active fifth trailed the market by about 6.5 points a year, mostly from trading costs.
  • Russo, J. E., & Schoemaker, P. J. H. (1989). Decision Traps: Ten Barriers to Brilliant Decision-Making and How to Overcome Them. Doubleday. Managers asked for 90%-confidence ranges trapped the true value only about half the time — the calibration result the arcade's quiz reproduces.
  • Tversky, A., & Kahneman, D. (1992). “Advances in Prospect Theory: Cumulative Representation of Uncertainty.” Journal of Risk and Uncertainty 5(4): 297–323. Cumulative prospect theory, and the source of the widely quoted loss-aversion coefficient of about 2.25.
  • Rabin, M. (2000). “Risk Aversion and Expected-Utility Theory: A Calibration Theorem.” Econometrica 68(5): 1281–1292. Turning down small favorable gambles is inconsistent with any plausible expected-utility risk aversion over wealth — the argument that loss aversion, not diminishing marginal utility, explains it.
  • Ptak, J. (2026). “Mind the Gap 2026: US stock fund investors made history; crypto mavens stumbled.” Morningstar Portfolio and Planning Research. The authoritative investor-return-gap study. Over the 10 years to Dec. 2025, the average dollar earned 8.7%/yr vs. funds' 9.9% total return — a ~1.2pp gap; the US-equity gap was the smallest of any category at ~0.4pp, close to this tool's independently computed CRSP medians (~0.3pp for US-equity funds, ~0.5pp across all funds; asset-weighted, our gap is roughly zero). The gap widens with category volatility (sector and alternative funds worst).

Diversification & return concentration

  • French, K. R., & Poterba, J. M. (1991). “Investor Diversification and International Equity Markets.” American Economic Review 81(2): 222–226. The classic documentation of home bias: investors hold far more domestic equity than market weights imply.
  • Evans, J. L., & Archer, S. H. (1968). “Diversification and the Reduction of Dispersion: An Empirical Analysis.” The Journal of Finance 23(5): 761–767.
  • Elton, E. J., & Gruber, M. J. (1977). “Risk Reduction and Portfolio Size: An Analytical Solution.” The Journal of Business 50(4): 415–437.
  • Statman, M. (1987). “How Many Stocks Make a Diversified Portfolio?” Journal of Financial and Quantitative Analysis 22(3): 353–363.
  • Bessembinder, H. (2018). “Do Stocks Outperform Treasury Bills?” Journal of Financial Economics 129(3): 440–457.
  • Bessembinder, H., Chen, T.-F., Choi, G., & Wei, K.-C. J. (2023). “Long-Term Shareholder Returns: Evidence from 64,000 Global Stocks.” Financial Analysts Journal 79(3): 33–63.

Valuation & long-run returns

  • Shiller, R. J. (1981). “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” The American Economic Review 71(3): 421–436.
  • Campbell, J. Y., & Shiller, R. J. (1988). “Stock Prices, Earnings, and Expected Dividends.” The Journal of Finance 43(3): 661–676.
  • Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press.
  • Dimson, E., Marsh, P., & Staunton, M. (2002). Triumph of the Optimists: 101 Years of Global Investment Returns. Princeton University Press.
  • Jordà, Ò., Knoll, K., Kuvshinov, D., Schularick, M., & Taylor, A. M. (2019). “The Rate of Return on Everything, 1870–2015.” The Quarterly Journal of Economics 134(3): 1225–1298.

Retirement & withdrawal

  • Bengen, W. P. (1994). “Determining Withdrawal Rates Using Historical Data.” Journal of Financial Planning 7(4): 171–180. Origin of the “4% rule.”
  • Cooley, P. L., Hubbard, C. M., & Walz, D. T. (1998). “Retirement Savings: Choosing a Withdrawal Rate That Is Sustainable.” AAII Journal 20(2): 16–21. The “Trinity study.”
  • Guyton, J. T., & Klinger, W. J. (2006). “Decision Rules and Maximum Initial Withdrawal Rates.” Journal of Financial Planning 19(3): 48–58. The guardrails approach: flexing spending with decision rules (capital-preservation and prosperity rules) lets you start at a higher withdrawal rate without running out.
  • Shoven, J. B., & Slavov, S. N. (2014). “Does It Pay to Delay Social Security?” Journal of Pension Economics & Finance 13(2): 121–144.

Data

  • Kenneth R. French Data Library, Tuck School of Business, Dartmouth College. Data © Eugene F. Fama and Kenneth R. French. The library publishes no formal license; we ship derived series (daily market returns, regional monthly returns, factor summaries) with attribution, not the library's files.
  • Form N-SAR filings (Item 28 monthly fund flows), 1993–2017, US Securities and Exchange Commission, via EDGAR. Public SEC filings — the semi-annual census form for registered funds, rescinded June 1, 2018 (replaced by Forms N-CEN and N-PORT), which is why the series ends there. Filings parsed into a research database by Kellogg Research Support, Northwestern University; monthly industry aggregates computed by the author. US government records, public domain.
  • AQR Factor & Quality-Minus-Junk / Betting-Against-Beta datasets, AQR Capital Management. Datasets 'Quality Minus Junk: Factors, Monthly' and 'Betting Against Beta: Equity Factors Data, Monthly'. AQR's site terms prohibit reproducing or publishing its content without written consent, so the series are not redistributed: only a handful of long-run annualized summary figures (facts about the factors) are quoted here, with attribution to AQR and to the underlying papers.
  • Liquidity factor (traded), Ľuboš Pástor & Robert F. Stambaugh. The traded liquidity factor (LIQ_V) of Pástor & Stambaugh (2003), monthly from January 1968, from Ľuboš Pástor's Chicago Booth data page. No usage terms are published; we ship its long-run annualized premium only.
  • Active Share dataset (US equity mutual funds), Antti Petajisto. Used per the author's terms, which require citing this website as the source and citing Petajisto (2013). We ship asset-weighted aggregates only; the raw fund-level panel is licensed and not redistributed.
  • CRSP US Stock Database, Center for Research in Security Prices, LLC, via WRDS. Source: CRSP®, Center for Research in Security Prices, Booth School of Business, The University of Chicago. Used with permission. All rights reserved. Accessed through WRDS under an academic subscription; only aggregate, non-identifiable statistics are published here — never a per-security series.
  • CRSP S&P 500 Indexes & Daily Constituents, Center for Research in Security Prices, LLC, via WRDS. Source: CRSP®, Center for Research in Security Prices, The University of Chicago. Used with permission. All rights reserved. We ship only index-level aggregates — month-end concentration, index returns, and decade snapshots of the top-10 tickers by weight; no per-stock time series is redistributed. Pending written confirmation of the licence's scope for public educational use.
  • CRSP Survivor-Bias-Free US Mutual Fund Database, Center for Research in Security Prices, LLC, via WRDS. Source: CRSP®, Center for Research in Security Prices, The University of Chicago. Used with permission. All rights reserved. The behavior gap is computed from fund monthly returns and net assets; only universe-level aggregates and a few anonymized illustrative cases (described by era and category, not named) are published — no per-fund panel is redistributed. Pending written confirmation of the licence's scope for public educational use.
  • IRS Revenue Procedures (annual inflation adjustments) & H.R.1 (2025), Internal Revenue Service / US Congress. Federal brackets, standard deductions, capital-gains thresholds, EIC and Child Tax Credit parameters, and saver's-credit tiers by tax year, plus Medicare IRMAA tiers (CMS). Collated via the community-maintained Case Study Spreadsheet (Mr. Money Mustache forums), whose marginal-rate analysis inspired this tool; our engine is validated against it.
  • Thomson Reuters Mutual Fund Holdings (s12), LSEG / Refinitiv, via WRDS. Used under license. Fund portfolio overlap is computed from quarterly holdings filings; only anonymized fund-level summaries are published (Fund A–L, ordered by assets) — no names, no positions. Pending written confirmation of the licence's scope for public educational use.
  • U.S. Stock Markets 1871–Present and CAPE Ratio, Robert J. Shiller, Yale University. The spreadsheet (ie_data.xls) behind Irrational Exuberance, republished by its author with a disclaimer and no usage terms. We ship monthly real total returns and the CAPE series derived from it, with attribution.
  • Historical Returns on Stocks, Bonds, Bills & Real Estate — United States, Aswath Damodaran, NYU Stern School of Business. Annual series from 1928 (histretSP.xls), published by the author without stated usage terms; used with attribution.
  • Federal Reserve Economic Data (FRED), Federal Reserve Bank of St. Louis. Series retrieved from FRED and cited to their originators per FRED's terms of use: Treasury yields (Board of Governors), CPI (BLS), 30-year mortgage rates (Freddie Mac PMMS), consumer credit rates (Board of Governors). The S&P Cotality Case-Shiller home-price index (© S&P Dow Jones Indices) is marked 'pre-approval required' on FRED, so only three headline statistics computed from it (long-run appreciation, worst drawdown, rolling-return range) appear here; the index levels are not republished.
  • FTSE Global All Cap Index — Factsheet, FTSE Russell (LSEG). Monthly factsheet for the whole-world index Vanguard's VT tracks (© LSEG; reproduction of the factsheet requires permission). Only its headline region weights and constituent count are cited here, with attribution; the site records the as-at month it used.
  • Total World Stock ETF (VT) — Portfolio composition, The Vanguard Group. Region allocations used to cross-check and seed the market-cap breakdown.
  • Publication 550: Investment Income and Expenses (Wash Sales), Internal Revenue Service. The wash-sale rule (IRC §1091) and the “substantially identical” standard. The IRS does not define the term for funds tracking different indexes.
  • Consumer Price Index (CPI) series, U.S. Bureau of Labor Statistics, via FRED. Annual price levels by spending category.
  • 2026 Retirement Plan Contribution Limits (COLA), Internal Revenue Service. Elective deferral, catch-up, and IRA limits and phase-outs, indexed annually.
  • Rev. Proc. 2025-19 (2026 HSA & HDHP limits), Internal Revenue Service. Inflation-adjusted HSA contribution limits and HDHP parameters for 2026.
  • Publication 915: Social Security and Equivalent Railroad Retirement Benefits, Internal Revenue Service. The worksheet for how much of a Social Security benefit is federally taxable.
  • 2026 Medicare Parts B & D Premiums (IRMAA), Centers for Medicare & Medicaid Services. Income-related monthly adjustment amounts and tiers, based on MAGI from two years prior.
  • Prioritizing investments, Bogleheads wiki. The community 'order of operations' the Next Dollar ladder follows.
  • Daily Treasury Par Yield Curve Rates, U.S. Department of the Treasury. The daily CMT par yields, fetched client-side from Treasury's public CSV feed (no API key).
  • Actuarial Life Tables & Benefit Data, U.S. Social Security Administration, Office of the Chief Actuary. Period life table, wage index, bend points and COLA history. US government work in the public domain.
  • Trends in the Expenses and Fees of Funds, Investment Company Institute. Li, Lei (2026), 'Trends in the Expenses and Fees of Funds, 2025,' ICI Research Perspective 32(1), © Investment Company Institute. ICI permits brief, attributed excerpts; the asset-weighted average expense ratios quoted here are such excerpts, linked to the report, and no underlying data are republished.
  • SPIVA U.S. Scorecard (Year-End 2025), S&P Dow Jones Indices. © S&P Dow Jones Indices LLC. S&P DJI prohibits redistribution or reproduction of the report without permission; only a few specific figures are transcribed here, with attribution, as brief factual quotation.

Educational use only, not financial advice. Every figure traces back to the sources above or to the inputs you set — the full method and the source code are public.