A commenter on Fat Pitch Financials recently suggested that I take a look at shareholder yield. After reading my “How Does a Change in Shares Outstanding Impact Stock Returns?” article, they suggested that dividend investors might also be interested in shareholder yield, and also the related Mebane Faber version of shareholder yield.
I was not familiar with this fundamental metric, so of course it caught my attention. Mebane Faber explores the concept of shareholder yield and expands upon it in his book, Shareholder Yield: A Better Approach to Dividend Investing. In that book, he examines how you can enhance dividend yield with the concepts of buyback yield and net debt paydown yield.
The Mebane Faber Shareholder Yield is defined as follows:
Meb Faber Shareholder Yield = Dividend yield + Buyback yield + Net debt repaid yield
While this formula is simple, it does not work well when companies conduct stock splits. If instead we use total expenditures on dividends plus net stock buyback cash plus change in total debt divided by market capitalization, we don’t need to worry about changes in share count due to stock splits.
For this backtest, I calculated the Mebane Faber Shareholder Yield as follows:
= (Dividends Paid TTM + (Total Debt PYQ – Total Debt Q) + (Equity Purchased TTM – Equity Issued TTM))/ Market Capitalization
I feel that the Meb Faber shareholder yield is really a measure of how shareholder friendly a company is when disbursing its profits. The expectation is that the higher the shareholder yield the better the shareholder return. The inverse is also likely true. If a company does not issue dividends, increases debt, and issues new shares, I would expect stock returns to be low. To test these assumptions, I ran an annually rebalanced backtest of this Meb Faber shareholder yield metric.
I used the data and backtesting tool provided by Portfolio123. 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 a similar filtered universe of stocks as my recent change is shares outstanding backtest. The only major difference is that I increased the minimum market capitalization to $1 billion to better match the size of companies in the S&P 500 equal weight benchmark that I use for these backtests. I’ve designed the filtering criteria for this backtest 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 minimum daily total amount traded over the past 42 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.
- Shareholder Yield != NA. We want to make sure we are only looking at companies that have valid data for the components of shareholder yield.
After these filters are applied, we are left with approximately 1,600 to 2,800 stocks. These stocks are then ranked by the criteria being tested; in this case, we are testing shareholder yield. The lowest 20 percent of stocks ranked by shareholder yield 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 shareholder yield in red and the S&P 500 Equal Weight Index in blue. The first quintile includes the companies that have negative shareholder yield and the 5th quintile includes the companies that had the highest shareholder yield.
Meb Faber Shareholder Yield Backtest Returns (2000 – 2015)
The top 20% of stocks as ranked by shareholder yield outperformed the benchmark in the 2000 to 2015 time period.
Summary of Results for Shareholder Yield Backtest
The first quintile exhibited a negative annualized return, especially in the years after 2008. The average excess returns versus the S&P 500 equal weight benchmark were a negative 1.79%. In contrast, the 5th quintile outperformed the benchmark 75% of the time and produced average excess return of 5.63%. This was very similar to results for the 5th quintile of the reduction in shares outstanding backtest. However, this shareholder yield backtest did not exhibit a smooth increase in average excess returns from the 1st quintile to the 5th quintile. The average excess returns bounced from negative to positive and back to negative between the 1st and 5th quintiles. It appears that there is not a strong signal associated with negative shareholder yield and stock returns.
I’m interested in seeing how the Meb Faber shareholder yield competes with the simpler dividend and backback shareholder yield. I plan on doing that backtest next.
I encourage you to try backtesting other variations of shareholder yield. Just sign up for a free 30-day trial at Portfolio123 and report your findings in the comments section below. I made my “Meb Faber Shareholder Yield backtest” screen public on Portfolio123, so you can search for it and try it out on the site.
I am also looking for other fundamentals to backtest. Share your suggestions in the comment section and I’ll add it to my backtesting queue.