Return on Assets (ROA) is a fundamental measure of profitability based on how much net income is generated by a company’s assets. Return on Assets is calculated as follows:
Return on Assets = Net Income / Average Total Assets
Return on Assets recently came up in a discussion regarding the Piotroski F-Score Backtest. Return on assets is part of the third test for the Piotroski F-Score. That test check to see if current ROA is higher than previous year ROA. A reader noted that in his experience, higher ROA actually seems to lead to lower alpha in bactest. I was curious to test this out.
Let’s take a look at a backtest of the Return on Assets ratio to see how it performs. 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 the same filtered universe of stocks as my recent Piotroski F-Score 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.
- ROA != NA. This filter insures we are looking at stocks that actually have valid ROA numbers.
After these filters are applied, we are left with approximately 3,300 to 4,100 stocks. These stocks are then ranked by the criteria being tested; in this case, we are testing Return on Assets. The lowest 20 percent of stocks ranked by ROA 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 Return on Assets in red and the S&P 500 Equal Weight Index in blue. The first quintile includes the companies that had the lowest ROA and the 5th quintile includes the companies that had the highest ROA.
Return on Assets Returns – 2000 – 2014
Summary of Results for the Return on Assets Backtest
* Average excess returns were analyzed starting in each month with 12-month holding periods (197 sample periods). This avoids the potential for seasonal reporting bias.
This backtest for ROA reveals that the first quintile underperforms the S&P 500 Equal Weight Index benchmark. These companies typically are unprofitable, so it is not surprising that stock returns for these companies would underperform. The second through fifth quintiles have higher than average annual excess returns but the excess returns do not increase consistently as ROA increases.
I noticed that stocks in the lowest quintile clearly under performed the benchmark. The median ROA for this first quintile is -14.79 while the median for all the other quintiles are positive. It appears the most powerful feature of ROA is associated in identifying stocks with negative returns on assets to avoid poor performers. I also noticed that companies with average, or in this case median ROA, likely outperform stocks that have either higher or lower returns on assets.
Given that the amount of assets required to earn net incomes varies considerably between heavy industries, such as mining or manufacturing versus the service sector that may require very few physical assets. ROA might be best used for comparing companies within industries or sectors.
What are your thoughts on the Return on Assets fundamental? Do you use ROA or some variation of it in your own stock analysis?