I recently reported on the results of my price-to-book ratio backtest. Shortly after running that backtest, I realized that many value investors probably actually prefer using price to tangible book value. The price to tangible book value ratio is simply the current price of the stock divided by the latest quarterly tangible book value per share. Tangible book value  is defined as book value minus goodwill and intangible assets.

Often goodwill and intangible assets end up on a balance sheet as a result of an acquisition, and unfortunately the more a company overpays for an acquisition, the higher those assets (goodwill and intangibles) end up being reported on the balance sheet. The price to tangible book value ratio to some degree overcomes this issue and more closely represents what common shareholders can expect to receive if the firm goes bankrupt and all of its assets are liquidated at their book values.

Let’s see how well the price to tangible book value ratio performs. I used the data and backtesting tool provided by StockScreen123. This backtest uses the same filtered universe of stocks as my recent P/B ratio 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. Price to Tangible Book Ratio > 0. This filter insures we are looking at stocks that actually have price-to-tangible-book value ratio data.

After these filters are applied, we are left with approximately 2,800 to 3,700 stocks. These are then ranked by the criteria being tested; in this case, we are testing the price to tangible book value ratio. The top 20 percent of stocks ranked by  price to tangible book value 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. 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 the 10-year price to tangible book value ratio backtest are as follows:

Price to Tangible Book Value: Average Excess Returns vs. Universe

Price to Tangible Book Ratio: Rolling 3-Yr Periods Excess Returns vs. Universe

The results are similar to those for the P/B Ratio backtest. The top quintile once again clearly outperformed the market by a significant margin. Moreover, the average excess returns from 2001 to 2011 for the top quintile for price to tangible book value (5.23%) exceed that of the price-to-book ratio (4.89%).  The Sharpe Ratio and Sortino Ratio were also both higher for the 1st quintile of the price to tangible book value versus the P/B ratio. I thought the 5th quintile would also result in lower average excess return for price to tangible book value given that it outperformed in the 1st quintile.  However, the P/B ratio had average excess returns of -3.84% from 2001 to 2011 versus -3.62% for the price to tangible book value ratio.

While I thought the price to tangible book value ratio would be clearly superior to the standard price-to-book ratio, that ended up not being so clear in this backtest.  The only conclusion that could be gleaned from this 10-year backtest is that price to tangible book value might be slightly better at identifying value opportunities than the standard price-to-book ratio for stocks with the lowest price ratios.

Here’s a list of the most shared articles posted on Value Investing News this past week:

1. Great Warren Buffett / Berkshire Hathaway Resources
2. Why George Soros Owns Comverse Technology (CMVT): Stock of the Week
3. Third Point’s 2011 Letter: Rationale for Owning UniCredit, Skyworks, Abercrombie & More
4. Ratios for evaluating turnarounds
5. A Value Investor’s Take on Shorting
6. Join Market Folly’s Third Annual FREE March Madness Bracket Contest
7. 11.0010010000111111011010101000100010000101101000110000100011010011
8. Redacted Version of the March 2012 FOMC Statement
9. What We’re Reading ~ 3/14/2012

Were there any other great value investing articles that were published this past week but are missing from this list? If so, please share them in the comments section below.

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 StockScreen123. 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. 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.

Here’s a list of the most shared articles posted on Value Investing News this past week:

1. Berkshire Hathaway Inc. 2011 Annual Report
2. Top 10 Hedge Funds By Net Gains Since Inception
3. Jeremy Grantham’s 10 Investment Lessons
4. Grantham: 10 Investment Lessons
5. Dan Loeb’s Third Point Starts Apple (AAPL) Stake: Top Positions & Latest Exposures
6. Beta of a Stock is a Meaningless Measure
7. Notes on the 2011 Berkshire Hathaway Annual Report, Part 1
8. Thinking about the Insurance Industry
9. Price-To-Book Ratio (P/B Ratio) Backtest
10. Eric Sprott’s Latest Commentary: 2012 is Year of the Central Bank

Where there any other great value investing articles that were published this past week but are missing from this list? If so, please share them in the comments section below.

The price-to-book ratio (P/B ratio) is a popular valuation ratio. It is calculated by taking the latest stock price and dividing it by book value per share.  Book value is simply the total assets found on the balance sheet minus  liabilities, which is referred to as common shareholder’s equity. To get book value per share all you have to do is divide book value by the number of shares outstanding.

Let’s see how well the P/B ratio performs. I used the data and backtesting tool provided by StockScreen123. This backtest uses the same filtered universe of stocks as my recent market capitalization 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. P/B Ratio > 0. This filter insures we are looking at stocks that actually have price-to-book value ratio data.

After these filters are applied, we are left with approximately 3,000 to 4,000 stocks. These are then ranked by the criteria being tested; in this case, we are testing the P/B ratio. The top 20 percent of stocks ranked by P/B ratio 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. 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 the 10-year  P/B ratio backtest are as follows:

P/B Ratio: Average Excess Returns vs. Universe

P/B Ratio: Rolling 3-Yr Periods Excess Returns vs. Universe

The top quintile with the lowest price-to-book value ratios clearly outperformed the market by a significant margin. The average annual excess return versus the universe of stocks tested was 4.89% from 2001 to 2011. The 5th quintile also underperformed the market by a significant margin. The average annual underperformance was 3.84%. If you look at the graph of rolling 3-year periods above, however, it does not appear that the top quintile (blue line) has continued to outperform in recent years and the bottom quintile has barely underperformed the market since around 2007. Nevertheless, there is a fairly linear relationship between quintile and excess return as shown in the bar graph above. This indicates that there is a strong relationship between the P/B ratio and excess returns. The biggest weakness of the P/B ratio strategy appears to be its volatility. The maximum loss of 52% for the first quintile and the maximum gain of 62% for the bottom quintile clearly indicates that the P/B ratio doesn’t always work.  This volatility is also clearly shown in the relatively low Sharpe ratio of 0.34 for the top quintile versus the Sharpe ratio of 0.32 for the universe.

While the price-to-book ratio can indicate the potential for value opportunities, it doesn’t always work. Do you use the P/B ratio regularly? If so, please share with us why you prefer this ratio in the comments section below.

Market capitalization is simply the total dollar value of all of a company’s outstanding shares of stock. It is often referred to as market cap for short. You can calculate market cap for a company by taking the current market price for a share of stock and multiplying it by the number of shares outstanding for that company. The size of a company is often measured by market capitalization and the media is routinely fascinated by which company currently has the highest market cap.

Stocks are often lumped into categories based on market cap. Those categories often include the following:

• Mega Cap (> $100 billion)
• Large Cap (>$10 billion)
• Mid Cap ($2 to $10 billion)
• Small Cap ($300 million to $2 billion)
• Micro Cap (< $300 million)
• Nano Cap (< $50 million)

These market cap size category definitions vary from source to source and can change over time. As a fat pitch value investor, I’m most interested in seeing if there is difference in returns for stocks based on market cap. Past studies have indicated that some market cap categories outperform other categories. Often small cap stocks are cited as generally outperforming large cap stocks. However, these market cap differences change over time and the performance differences may change with the business cycle.

I’m not one for really relying on past studies. I decided to do my own backtest. I used the data and backtesting tool provided by StockScreen123. This backtest will be the first one of a series of backtests that differ from my Richard Tortoriello inspired backtests. 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.

After these filters are applied, we are left with approximately 3,000 to 4,000 stocks. These are then ranked by the criteria being tested; in this case, we are testing market cap. The top 20 percent of stocks ranked by market cap is 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. 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 the 10-year  market cap backtest are as follows:

Market Cap: Average Excess Returns vs. Universe

Market Capitalization: Rolling 3-Yr Periods Excess Returns vs. Universe

As you’d expect, the largest 20 percent of stocks underperformed the other stocks by 1.89 percent. The surprising result was the outperformance of the second quintile. The second quintile currently includes stocks with market caps between $4.2 billion and $1.5 billion. These are basically mid cap stocks. The CAGR of the second quintile from December 31, 2001 to January 1, 2012 was 8.48% and the annual average outperformance of the four seasonal starting points was 0.34%.

The one thing I didn’t expect was the underperformance of the bottom quintile, the micro cap stocks.  However, this underperformance might have more to do with the relatively short 10-year backtest. If you look at the rolling 3-Yr periods excess returns versus the universe chart, the red line represents the bottom quintile.  From 2003 to about the third quarter of 2005, the micro cap category actually outperformed the universe.  The micro caps really get hit during the financial crisis and that is the main reason they underperform in this backtest.

The micro cap category results provide a good reminder that past performance is no guarantee of future results.  My goal with this backtest and future backtests is only to identify potential spots to look for fatter pitches.

Do prefer stocks from a certain market cap category? If so, please share which category you prefer and why. I’m also interested in your feedback regarding the 6 filtering criteria I used for this backtest. I plan on using those filters for future backtests, so it’s important that I get your feedback now. Please share your thoughts in the comments section below.

Another widely used valuation ratio that Richard Tortoriello examined in his book Quantitative Strategies for Achieving Alpha is the P/E ratio. It is probably the most recognizable and used stock valuation metric. There are several variations on this popular fundamental ratio, including using trailing twelve-month earnings per share, one-year forward earnings per share estimate, and the use of enterprise value instead of market price. Tortoriello decided to present the backtest result for the P/E ratio that uses current fiscal year earnings, since he found it to have the most consistent performance.

Read the rest of this entry »

The results for the Return on Enterprise Value backtest were very impressive, so I thought readers would be interested in seeing a list of the top 1% of stocks ranked based on Net Cash Flow / Enterprise Value.  Here are the current results: Read the rest of this entry »

Return on Enterprise Value (ROEV) is a stock valuation ratio that can be useful for comparing the values of different companies. It is simply net cash flow divided by enterprise value. Net cash flow is net profit plus amounts charged off for depreciation, depletion, and amortization.

I was unfamiliar with this stock fundamental until Ken Faulkenberry of AAAMP Blog left the following comment on my Enterprise Value to EBITDA Ratio Backtest post:

Great post; glad to read someone uses this strategy in this day of “passive management”. This kind of valuation of stocks needs much more attention!

Personally I prefer Net Cash Flow / Enterprise Value which would include interest expense. I have written an article titled “Best Stock Valuation Calculation to Value Company Shares is ROEV” for anyone interested.

I decided to backtest Net Cash Flow / Enterprise value and compare it to the excellent results of the EV / EBITDA Ratio backtest.

Read the rest of this entry »

Given that we recently backtested the highly effective Enterprise Value to EBITDA ratio that was presented in Quantitative Strategies for Achieving Alpha, I thought folks might be interested in seeing the current results for this screen. Here are the top 1% stocks ranked on EV/EBITDA: Read the rest of this entry »