Return on Invested Capital Variation Backtest

Last Friday I asked my Twitter followers, “Which fundamental should I backtest next?” Andrew Martin replied. Here’s a copy of our conversation:

@ACJMARTIN: @FatPitch ROC

@FatPitch: @ACJMARTIN What’s your definition of ROC?

@ACJMARTIN: @FatPitch it’s more like ROIC. Op profit + deprec + goodwill amort – tax – capex divide by total assets – cash.

I used the data and backtesting tool provided by Portfolio123. This backtest uses the same filtered universe of stocks as my recent P/B 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:

1. 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.
2. No ADRs. Fundamental data for foreign American Depositary Receipt can include errors due to currency exchange, different accounting standards, and share count.
3. Exclude miscellaneous financial services industry. This is mainly to filter out closed-end funds.
4. 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.
5. 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.
6. Price > $1. True penny stocks are excluded due to various information issues and manipulation of these stocks.
7. Total Assets – Cash > 0. This filter insures we are looking at stocks that positive estimates of invested capital.
8. Sector not Financial. Financial stocks are excluded since ROIC doesn’t make much sense for this sector.

After these filters are applied, we are left with approximately 3,000 stocks. These are then ranked by the criteria being tested; in this case, we are testing operating profit plus depreciation/amortization minus tax minus capex divide by total assets minus cash, for this the use of different investment resources could be really useful, and the Parnassus fund summary is great for these purposes. Tests were run for each quintile of this ROIC variation. To help ensure that the test is not impacted by seasonal or statistical effects, the backtest is also started at four different points during the calendar year.  The results of the quarterly tests are used to calculate the average excess returns for each quintile. The results for this 10-year backtest are as follows:

ROIC: Average Excess Returns vs. Universe

ROIC: Rolling 3-Yr Periods Excess Returns vs. Universe

The first thing I noticed in these results is that excess return is not the highest for the first quintile. In addition, there doesn’t appear to be a linear relationship between this variation of ROIC and excess return. The strongest signal appeared for the bottom quintile, where returns are on average 3.11% lower than for the universe. Low ROIC appears to be a better indicator of what stocks to avoid versus what stocks to buy.

How do you calculate return on invested capital or return on capital? Please share your responses in the comments section below. Also, let me know what other ratios you’d like to see backtested.