A reduction in shares outstanding can occur as a result of a company stock buyback. A buyback is when a company repurchases its stock with the goal of reducing the number of its share on the market. As you can imagine, a reduction in shares outstanding is likely to impact stock returns. However, I was not sure that the percent reduction of shares outstanding would be reflected in future stock returns. Could it be that stock prices already reflected the news of previously announced buyback programs? I decided to test this out with a backtest.

For this backtest, I calculated the percent reduction is shares outstanding as follows:

% Reduction in Shares Outstanding = -100 * ((SharesCurrentQuarter -SharesPreviousYearQuarter) / SharesPreviousYearQuarter

I multiplied by negative 100 in the formula above in order to make the share reductions a positive percentage number and increases in shares outstanding a negative number. This would place companies that have issued more shares within the past year via a secondary stock offering, warrants, or stock options into the first quintile. I expected that these stocks would underperform the market given the dilution of each shareholder’s equity.

I used the data and backtesting tool provided by Plus500. The Portfolio123 backtesting eliminates the problem of survivorship bias by using point-in-time and retaining data on stocks that have gone to zero. This backtest uses the same filtered universe of stocks as my recent 5-Year Average Return on Equity Backtest. I’ve designed the filtering criteria for this backtest specifically for individual investors and with a focus on enhancing data quality. The filters include the following criteria:

No OTC stocks. Stocks not traded on the New York Stock Exchange, NASDAQ, or American Stock Exchange markets are excluded. The quality of fundamental stock data for OTC can be somewhat lower and less timely that that for stocks traded on major exchanges. No ADRs. Fundamental data for foreign American Depositary Receipt can include errors due to currency exchange, different accounting standards, and share count. Liquidity test. The average daily total amount traded over the past 60 trading days must be larger than $100,000. This amount was selected so that a $1 million dollar portfolio could hold 100 positions and that each new $10,000 position would not exceed 10 percent of a day’s trading volume. The liquidity test also ensures that the backtest has reliable market price information for any of the stocks that are being tested. Market Cap > $50 million. Nano cap stocks are excluded to help improve data quality. This filter also ensures that positions in a modest sized portfolio never exceed one percent of shares outstanding or the available float for a company. Price > $1. True penny stocks are excluded due to various information issues and manipulation of these stocks. Closing Price(365 days ago) != NA. We want to make sure we are only looking at companies that have been trading for a least a year. We do not want to examine the change in shares outstanding of any new IPOs or split-offs. SharesQ > 0. This filter insures we are looking at stocks that actually have valid data the number of common shares outstanding from the most recent quarterly report. SharesQPreviousYear > 0. This filter insures we are looking at stocks that actually have valid data the number of shares outstanding from the previous year’s quarterly report so we can calculate the percent share reduction over the past year.

After these filters are applied, we are left with approximately 3,200 to 4,000 stocks. These stocks are then ranked by the criteria being tested; in this case, we are testing percent reduction in shares outstanding. The lowest 20 percent of stocks ranked by percent reduction in shares outstanding are placed in the first quintile and the next 20 percent in the second quintile and so forth until we have five portfolios of stocks. The portfolios are rebalanced every 12-months and compounded annually to more realistically replicate what an individual investor might be expected to do to avoid higher short-term capital gains tax and trading costs. The following 5 charts display the quintile returns for percent reduction in shares outstanding in red and the S&P 500 Equal Weight Index in blue. The first quintile includes the companies that increased the number of shares outstanding and the 5th quintile includes the companies that had the highest percent reduction in shares outstanding.

Percent Reduction in Shares Outstanding Backtest Returns (2000 – 2015)

The first quintile clearly and consistently underperforms the S&P 500 equal weight benchmark as expected.

The top 20% of stocks that reduced shares outstanding clearly outperformed the benchmark in the 2000 to 2015 time period.

Summary of Results for the Reduction in Shares Outstanding Backtest

The first quintile barely exhibited a positive annualized return. The stock of these companies that increased shares outstanding underperformed the market in 75% of the years in this 16-year test period. The average excess returns versus the S&P 500 equal weight benchmark were a negative 3.34%. In contrast, the 5th quintile outperformed the benchmark 81.25% of the time and produced average excess return of 5.54%.

These results are similar to the 1929 to 1998 backtest conducted by William R. Nelson of the Federal Reserve Board in his paper titled, Evidence of Excess Returns on Firms That Issue or Repurchase Equity. In Table 9 of his paper, he found that the top 5% of companies that had reduction in shares outstanding returned 20.0% after one-year while companies in the bottom 5% that increased shares outstanding only returned 11.9 percent after one-year. That’s a 8.1% difference. My backtest indicated a 11.33 percent difference between the first and fifth quintiles in the 2000 to the end of 2015 time period.

I encourage you to try backtesting other equity structure related formula variations, and maybe even test out the percent reduction in shares outstanding in combination with valuation and debt ratios. Just sign up for a free 30-day trial at Portfolio123 and report your findings in the comments section below.