Interactive Tool

Can you outsmart the market?

Every attempt to beat the market is one of five bets: that you can predict it, time it, deploy into it at the perfect moment, pay someone to pick winners, or that the manager you hired is even really trying. Work through all five and the same answer keeps appearing — then watch the money itself, in the fund industry's own SEC filings, reach the same conclusion. It's exactly why owning the whole market cheaply is so hard to beat.

Six ways to see the same answer

Next move?

Right so far: 0/0
Your timing return: +0.0%
Just holding: +0.0%

The chart is a simulated stock. Study the trend all you like, then predict. The past is already in the price.

Your accuracy across all games

make some predictions…

Six looks, one answer

Predict it. In an efficient market, prices already reflect what's known, so the next move rides on new information: surprises. Guessing them is a coin flip, and a lucky streak isn't skill.
Time it. The market's best days cluster right next to its worst; sit out a handful and decades of growth vanish. Even the "worst timer" who only buys at peaks beats the one who waits in cash.
Deploy it. Got a windfall? Across history, investing it all at once usually beats dribbling it in, because the market rises more often than it falls, so waiting mostly costs you.
Beat it. The SPIVA scorecards show most active funds trail a simple index over time, and the gap widens the longer you look. After fees, the humble index quietly wins.
Pay for it. Even the "active" fund you hire may be a closet indexer — hugging its benchmark while charging active fees. Its Active Share reveals how little of it is truly active, and how steep the real fee on that slice becomes.
Watch the money. For 25 years every US mutual fund reported its monthly dollars in and out to the SEC. That ledger shows an industry churning roughly 37 gross dollars for every net dollar invested — and, in 2016, the first year investors pulled more out than they put in, as money migrated to cheap index funds.

Educational only, not financial advice.

Sources & further reading

How this tool was made

Mixed by design. The “predict the next move” game is synthetic — real charts shown against generated random walks. The rest is real: missing-the-best-days uses actual daily US market returns (Fama–French, 1990–present), lump-sum-vs-averaging uses Shiller's monthly series, the active-fund scorecard combines SPIVA with CRSP survivorship records, and closet indexing uses CRSP and Thomson holdings data. Watch-the-money aggregates the monthly gross sales and redemptions every US open-end fund reported to the SEC on Form N-SAR Item 28, 1993–2017 — 158,232 filings; net flow follows Christoffersen, Evans & Musto (2013).

Research

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.
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.
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.
Malkiel, B. G. (1973). A Random Walk Down Wall Street. W. W. Norton & Company.
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. (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.
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.
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).
Carhart, M. M. (1997). “On Persistence in Mutual Fund Performance.” The Journal of Finance 52(1): 57–82. Adds momentum as a fourth factor.
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.

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.
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.
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.
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 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.
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.
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.
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.

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.

Next in the playground → Behavioral Finance: Your Own Worst Enemy The market doesn't lose you money — your reactions to it do. Watch the behavior gap open up between a buy-and-hold investor and a panic-seller over real market history — then step into the Bias Arcade: eight two-minute experiments, adapted from the classic studies, that measure YOUR anchoring, loss aversion, overconfidence, and more before naming them. Play first; diagnosis after.