Financial analysis · Python + pandas · Rebuilt in 2026

Which corners of the stock market actually grew the most?

125 US stock market indexes (49 industries, plus portfolios sorted by size, value, momentum, profitability and investment) ranked by growth, risk and worst losses over windows from 1 to 50 years. Data through .

Window

Top 10

    Bottom 10

      What $1 grew to

      Click up to four rows in the table below to compare them with the total market. The scale is logarithmic, so equal steps up mean equal percentage growth.

      Every index

      Click a column to sort, or a row to chart it.

      Do the winners keep winning?

      Each index ranked by growth in one period, then again in the next period of the same length.

      Hardly. Across 125 indexes, how well something did in one 15-year stretch says almost nothing about the next one. That's why chasing last decade's best sector is risky, and why a broad market index is a hard benchmark to beat.

      How it works

      1. Data: Python downloads monthly, value-weighted returns from the Kenneth R. French Data Library, the standard source for academic research on US stocks, and parses the first table of each file.
      2. Analysis: pandas computes each index's compound annual growth, annualized volatility, Sharpe ratio against the risk-free rate, worst peak-to-trough drop, and growth of $1, for any window. Indexes missing any month in a window are left out of it.
      3. Ranking: indexes are sorted by the chosen measure. Ranks from back-to-back windows are compared with a Spearman correlation.
      4. This page: python -m indexer export writes the results to one JSON file. The page only displays it; all the math is in the tested Python package.

      From the command line

      $ python -m indexer rank --years 15 --top 3 --group Industry
49 indexes ranked by cagr, 2011-09 to 2026-08

   #  Index                              Group             CAGR     Vol Sharpe  Max DD     $1 →
   1  Electronic Equipment               Industry         26.6%   22.1%   1.12  -29.9%    34.38
   2  Defense                            Industry         19.5%   20.2%   0.91  -23.5%    14.51
   3  Software                           Industry         18.0%   18.2%   0.92  -39.3%    12.04
   …
  47  Apparel                            Industry          5.4%   22.1%   0.28  -52.6%     2.20
  48  Recreation                         Industry          3.8%   25.2%   0.21  -73.2%     1.75
  49  Coal                               Industry         -6.7%   46.0%   0.05  -97.2%     0.35

Did the winners repeat? Comparing 1996-09 to 2011-08 with 2011-09 to 2026-08: rank correlation 0.12, 3 of the earlier top 10 stayed in the top 10.

      Options: --years, --by cagr|sharpe|max_drawdown|volatility|growth, --group Industry, --end 2008-12. 15 pytest tests cover parsing, every measure, ranking, and the exported results.