Fat Pitch Financials

Price To Tangible Book Value Backtest

George — Fri, 23 Mar 2012 15:27:58 +0000

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:

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.
Exclude miscellaneous financial services industry. This is mainly to filter out closed-end funds.
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.
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.

Most Shared Value Investing News – St. Patrick’s Week 2012

George — Sat, 17 Mar 2012 21:43:29 +0000

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

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.

Return on Invested Capital Variation Backtest

George — Mon, 05 Mar 2012 19:05:12 +0000

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:

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.
Exclude miscellaneous financial services industry. This is mainly to filter out closed-end funds.
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.
Total Assets – Cash > 0. This filter insures we are looking at stocks that positive estimates of invested capital.
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.

Most Shared Value Investing News – Week Ending March 3, 2012

George — Sat, 03 Mar 2012 20:30:34 +0000

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

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.

Price-To-Book Ratio (P/B Ratio) Backtest

George — Thu, 01 Mar 2012 12:47:21 +0000

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:

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.
Exclude miscellaneous financial services industry. This is mainly to filter out closed-end funds.
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.
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.

Returns by Market Capitalization Over the Past Decade

George — Tue, 28 Feb 2012 14:44:51 +0000

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:

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.
Exclude miscellaneous financial services industry. This is mainly to filter out closed-end funds.
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.

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.

Price to Current Fiscal Year Earnings Backtest

George — Mon, 20 Feb 2012 16:45:35 +0000

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.

For ease of backtesting, I used the inverse of the price to current fiscal year earnings, which is current fiscal year earnings per share divided by price. I used StockScreen123 to conduct a 10-year backtest of the current fiscal year earnings to price ratio. I filtered out ADRs, non-US companies, companies in the miscellaneous financial services industry category (to mainly filter out closed-end funds), stocks trading below $2, market caps less than $433 million (approximately matching the average cut-off Tortoriello used), and companies that did not have a current fiscal year earnings to price ratio due to missing data. The results are as follows:

Current Fiscal Year Earnings to Price Ratio : Rolling 3-Yr Periods Excess Returns vs. Universe

The 2001 to 2011 backtest results for the 1st quintile are very similar to what Tortoriello found. Tortoriello estimated that the average annual excess return versus the universe from 1988-2007 was 4.7%, while I found the average excess return versus the universe was 4.13% from 2001 to 2011. This excess return is less than the 4.80% I found for return on enterprise value (ROEV) and 4.59% for enterprise value to EBITDA ratio for the 1st quintile. The Sharpe ratio for the top quintile of the 2001-2011 price to current fiscal year earnings estimate backtest was also lower than for the other previously mentioned backtests.

The 5th quintile average excess returns versus the universe were much lower than what Tortoriello found, -5.2% versus -2.2%. This is probably due to the different time periods. My backtest included the major market decline in 2008 and 2009. However, when comparing different valuation ratios over the same time period, the bottom quintile of enterprise value to EBITDA had average excess returns of -5.84% and ROEV had average excess returns of -5.29%.

In general, this backtest of price to current fiscal year earnings indicates that in the past ten years both return on enterprise value and enterprise value to EBITDA ratio models outperformed the P/E ratio using current fiscal year earnings for both the top and bottom 20% of stocks. Given these results, one has to wonder why the P/E ratio is still so popular. Do you use the P/E ratio? If so, why? Please leave you responses in the comments section below.

Current Best Values: Return on Enterprise Value

George — Fri, 27 Jan 2012 13:55:21 +0000

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:

Career Education Corp. (CECO) 0.71
Visteon Corporation (VC) 0.71
Veeco Instruments Inc. (VECO) 0.55
Unisys Corporation (UIS) 0.54
Power-One, Inc. (PWER) 0.53
Momenta Pharmaceuticals, Inc. (MNTA) 0.48
OmniVision Technologies, Inc. (OVTI) 0.42
Vishay Intertechnology (VSH) 0.42
Icahn Enterprises, L.P. (IEP) 0.40
ITT Corporation (ITT) 0.39
Humana Inc. (HUM) 0.37
American National Insurance Company (ANAT) 0.35
Aaron’s, Inc. (AAN) 0.35
WPX Energy Inc (WPX) 0.33
Healthsouth Corp. (HLS) 0.32
Navistar International Corporation (NAV) 0.32
GT Advanced Technologies Inc (GTAT) 0.31
Telephone & Data Systems, Inc. (TDS) 0.29
Micron Technology, Inc. (MU) 0.29
Tesoro Corporation (TSO) 0.28

Data: StockScreen123

It will be interested to check back on these stocks after a few months. Do any of the names above look interesting to you? Please leave your responses in the comments section below.

The above list of low Return on Enterprise Value stocks should only be used as a starting point for further research. Always remember, past performance does not guarantee future results.

Disclaimer: At the time this post was published, I did not own shares in any of the companies mentioned.

Return on Enterprise Value (ROEV) Backtest

George — Tue, 24 Jan 2012 16:40:04 +0000

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.

I used StockScreen123 to conduct a 10-year backtest of the Return on Enterprise Value metric. I filtered out ADRs, non-US companies, companies in the miscellaneous financial services industry category (to mainly filter out closed-end funds), stocks trading below $2, market caps less than $433 million, and companies that did not have a net cash flow/EV ratio due to missing data. The results are as follows:

Return on Enterprise Value: Rolling 3-Yr Periods Excess Returns vs. Universe

As you can see from the table and charts above, stocks ranked in the highest 20% (1st quintile) of Net Cash Flow / EV ratio produced a CAGR of 11.48% from January 1, 2002 to December 31, 2011. Averaging the annual excess returns versus the universe using four different quarterly start dates, the average excess returns for the first quintile is 4.80%. That is just slightly better than the 11.37% CAGR for EBITDA/EV 10-year backtest and the 4.59% average excess returns for the first quintile. The first quintile Sharpe and Sortino ratios for ROEV were almost identical to the EV/EBITDA backtest ratios. The ROEV strategy first quintile outperformed 85% of 1-year periods, while EV/EBITDA only outperformed 72.5% of 1-year periods. More impressively, the ROEV strategy outperformed 100% of rolling 3-year periods. Interestingly, looking at the lowest ranked stocks in the 5th quintile, ROEV averaged -5.29% excess returns versus the universe while EV/EBITDA averaged -5.84%. The bottom 20% of ROEV didn’t quite underperform as much as EV/EBITDA. Given how close the 1st quintile and 5th quintile scores are when comparing ROEV and EV/EBITDA, I’m not really sure you can say statistically that ROEV is better than EV/EBITDA. I really wish I could run a 20- or 30-year backtest on return on enterprise value. (Let us know if you can?)

Given these backtest results, I’m strongly considering working Return on Enterprise Value into my investment process, but it’s still difficult to tell if it is truly better than EBITDA/EV. What do you think? What other fundamentals would you like to see backtested here? Please leave you responses in the comments section below.

Current Best Values: Enterprise Value to EBITDA Ratio

George — Tue, 17 Jan 2012 15:26:09 +0000

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:

American Equity Investment Life (AEL)
National Western Life Insurance (NWLI)
Career Education Corp. (CECO)
First American Financial Corp (FAF)
Unisys Corporation (UIS)
The Hanover Insurance Group, Inc. (THG)
State Auto Financial (STFC)
Veeco Instruments Inc. (VECO)
Hartford Financial Services (HIG)
Power-One, Inc. (PWER)
General Motors Company (GM)
Visteon Corporation (VC)
CIGNA Corporation (CI)
Assurant, Inc. (AIZ)
Vishay Intertechnology (VSH)
American National Insurance Company (ANAT)
Lincoln National Corporation (LNC)
Meadowbrook Insurance Group, Inc. (MIG)
WellCare Health Plans, Inc. (WCG)
Principal Financial Group, Inc. (PFG)
OmniVision Technologies, Inc. (OVTI)

Data: StockScreen123

It will be interested to check back on these stocks after a few months. Do any of the names above look interesting to you? Please leave your responses in the comments section below.

The above list of low EV/EBITDA stocks should only be used as a starting point for further research. Always remember, past performance does not guarantee future results.

Disclaimer: At the time this post was published, I did not own shares in any of the companies mentioned.

Enterprise Value to EBITDA Ratio Backtest

George — Fri, 13 Jan 2012 16:47:38 +0000

The EBITDA to enterprise value (EV) ratio is a widely used valuation multiple to assess the relative value of companies. It is calculated by simply taking earnings before interest, taxes, depreciation and amortization (EBITDA) and dividing by enterprise value (EV). Of course, you need to know the definitions of both of those terms to really be able to understand this stock fundamental.

EBITDA is equal to net income with interest, taxes, depreciation, and amortization added back into it. EBITDA is somewhat useful for analyzing and comparing profitability between companies and industries because it removes differences due to tax rates and eliminates the effects of financing and accounting decisions. However, many value investors shun this fundamental due to its past abuse during the tech bubble. Charlie Munger is quoted as saying, “I think that, every time you saw the word EBITDA [earnings], you should substitute the word “bullshit” earnings.”

Enterprise Value takes into account the entire value of a company. It is equal to market capitalization plus debt minority interest and preferred shares, minus total cash and cash equivalents. However, for this backtest I simplified the formula similarly to the way Richard Tortoriello did in Quantitative Strategies for Achieving Alpha and just used market cap plus debt minus cash.

An advantage of the EV/EBITDA multiple is that it is capital structure-neutral, and, therefore, this multiple can be used for direct cross-companies application. The reciprocate multiple EBITDA/EV is used as a measure of cash return on investment. Given that EBITDA ignores capital structure by excluding interest and EV doesn’t discriminate between equity holders or debt holders, also accounting for the entire firm, this ratio provides a complete snapshot on the relative value of entire companies. You can use EV/EBITDA when you want to see the cash-generating power of the entire firm, and you don’t care whether it’s equity or debt financing this cash-generating operation. That’s why EV/EBITDA is used for pure valuation.

I used StockScreen123 to conduct a 10-year backtest of the EBITDA/EV ratio. I filtered out ADRs, non-US companies, companies in the miscellaneous financial services industry category (to mainly filter out closed-end funds), stocks trading below $2, market caps less than $433 million (approximately matching the average cut-off Tortoriello used), and companies that did not have a EBITDA/EV ratio due to missing data. The results are as follows:

Review the full Enterprise Value to EBITDA ratio backtest spreadsheet on Google Docs.

EV to EBITDA : Rolling 3-Yr Periods Excess Returns vs. Universe

As you can see from the table and charts above, stocks ranked in the highest 20% (1st quintile) of EBITDA/EV produced a CAGR of 11.37% from January 1, 2002 to December 31, 2011. Averaging the annual excess returns versus the universe using four different quarterly start dates, the average excess returns for the first quintile is 4.59%. That is very similar to the 5.3% Richard Tortoriello reported in Quantitative Strategies for Achieving Alpha for the period 1988-2007. Tortoriello found EV/EBITDA to be one of the most consistent (in terms of Sharpe Ratio) factors he tested. In the recent paper, Analyzing Valuation Measures: A Performance Horse-Race Over the Past 40 Years, Wesley Gray and Jack Vogel also found the EBITDA/EV ratio to have been the best performing metric historically. They found that it outperforms many investor favorites such as price-to-earnings, free-cash-flow to total enterprise value, and book-to-market.

Do you use the EV/EBITDA ratio? If not, why not given these results? What other fundamentals would you like to see backtest here? Please leave you responses in the comments section below.

Book Review: Quantitative Strategies for Achieving Alpha

George — Tue, 10 Jan 2012 21:25:24 +0000

Over the holidays, I read Quantitative Strategies for Achieving Alpha by Richard Tortoriello. I found this book to be very different than most of the books I’ve read on investing. Tortoriello spends a substantial portion of the book walking the reader though his process for evaluating various stock fundamentals. He then tests each of these fundamentals using twenty years of high quality backtesting data from the Standard & Poor’s Compustat Point in Time database. Unlike other books that discuss investment theory and qualitative analysis, this book is very empirical. The stock fundamentals Tortoriello analyzed include many value investor favorites. Based on that alone, I think many intermediate to advanced value investors could benefit from this book. Beginners might find this book a bit overwhelming.

While Quantitative Strategies for Achieving Alpha would obviously appeal to so called “quants”, I also think it is a book that value investors should not overlook. Tortoriello introduces the book by stating, “This book was written with qualitative investors in mind, particularly those who wish to “understand” the stock market from a quantitative (empirical) point of view and who desire to integrate quantitative screens, tests, or models into their investment process—or simply into their thinking.” While quantitative approaches often tend to focus on technical analysis, this book will appeal more to value investors given that it examines the theory of why each fundamental factor works and it relegates technical analysis to just one chapter on price momentum.

From page 39 of The Science of Hitting.

As I read through each chapter and flipped through the extensive results tables in the appendix, I started imagining Ted Williams’ strike zone graphic in The Science of Hitting. Could I use Tortoriello’s process and analysis to start building my own strike zone chart of where to find the fattest pitches in the stock market? By the time I made it through the second chapter, I was pretty excited with the idea of using Tortoriello’s method myself. With access to Portfolio123 data and this method, I might just be able to build myself a map of when and where to swing for the fat pitches.

Before I get too far ahead of myself, let me discuss why I like this book so much. Unlike Joel Greenblatt’s The Little Book That Beats the Market, Tortoriello’s Quantitative Stategies for Achieving Alpha provides all the details necessary for replicating his work. The method is clearly and fully described in a 26-page methodology chapter. There are no secret or ambiguous steps to his stock screens. I was never able to replicate Greenblatt’s Magic Formula (and I haven’t seen anyone else replicate his backtest results), but I feel confident that I can replicate Tortoriello’s work. In fact, I’ve already started and will start sharing those results soon.

The method Tortoriello lays out begins with investment basics, building bocks, and finally mosaics. An investment basic in this context is a grouping of investment strategies that generally work. The basics listed by Tortoriello are as follows:

Profitability
Valuation
Cash Flow
Growth
Capital Allocation
Price Momentum
Red Flags

A chapter is dedicated to each of these investment basics in Quantitative Strategies. Each of these basics are then composed of building blocks. Tortoriello states, “A building block is a specific strategy that has investment value and works for a clearly understandable, nonstatistical reason.” Finally, these building blocks can then be pieced together to form a mosaic. The mosaics take the form of two factor screens, multi-factor models, and integrated investment strategies that combine quantitative results with qualitative vetting and risk management.

Each of the building blocks tested in this book are sorted by the value of the factor being tested and then divided into five equal sized portfolios. Each of these quintiles are backtested with 12-month holding periods from 1987 to 2006. 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 test results for the Enterprise Value to EBITDA ratio are provide below as an example of the many result summaries presented in this book.

When two factor tests are constructed, both factors are not weighted equally by Tortoriello. Instead, he first forms a set of stocks based on the first factor and then from that set he selects stocks based on the second factor. Not much rationale is provided for why this method was preferred, except to say that the first factor ends up being weighted more heavily and that the portfolio sizes end up consistent. This is a bit different from the way Greenblatt equally weighted both factors in his Magic Formula.

As you saw in the backtest example, many statistics are provided for each of the quintiles. These include your typical compound annual growth rate, average excess returns, and the percent of periods outperforming. In addition, the author includes some more academic statistics such as Sharpe Ratio, Beta, and Alpha. The author gets points with value investors for disparaging Beta as a measure of risk and its use in calculating Alpha. However, he still provides these statistics with every summary, I guess to cater to some quants that expect to see those stats. In addition to these statistics, many of the basic building blocks are also tested by industry sector.

Tortoriello uses a fairly intuitive and and basic set of criteria to evaluate each of the strategies tested in this book. He write, “Our criteria for a strategy that works are (1) the top quintile outperforms the market by a significant margin; (2) the bottom quintile significantly underperforms; (3) outperformance and/or underperformance have been consistent over the years; and (4) there is some linearity in the performance of the quintiles, indicating a strong relationship between the strategy and excess returns.” In addition, he also mentions that he prefers strategies with low volatility and low maximum loss for the top quintile and high volatility and high maximum loss for the bottom quintile. Most importantly, Tortoriello doesn’t forget to emphasize that a strategy that worked well in the past may not work as well in the future.

The book concludes with two chapters on pulling together the most successful building blocks. These included, but are not limited to, the following factors: EV to EBITDA, Free Cash Flow to Price, ROIC, Cash ROIC, 52-Week Price Range, 7-Month Relative Strength, External Financing, 1-Yr Reduction in Shares, EPS Score, and FCF Per Share Score. Several two-factor, three factor, and a complex multi-factor model built using Monte Carlo simulation to optimize weightings are presented. The use of Monte Carlo simulation makes me a bit uncomfortable with the potential for data mining and model overfitting, but at least it introduced me to another approach.

While the analysis in Quantitative Strategies for Achieving Alpha is valuable, this book is not an easy read. I would not recommend taking this book to the beach. Instead, I found it ideal to read this book over short intervals in a quiet study or during the quiet morning subway ride I take to work. I found myself referring back to the methods section and some individual building block backtest results several times. This is one of those books you’ll want to own and markup versus borrowing it from the library.

This book has inspired me to test out many of the stock fundamentals that are popular with value investors. Within the next few days, I’ll provide my own backtest of the Enterprise Value to EBITDA ratio from 2001 to 2011 using StockScreen123. What other fundamentals would you like to see tested?

Closing Out 2011, Kicking Off 2012

George — Tue, 03 Jan 2012 14:43:22 +0000

Happy new year fellow investors! I hope you enjoyed the holidays as much as I did. It was great spending time with my family this past week, taking a break from work, and reflecting on the future.

2011 was a challenging year, especially for fellow investors and website owners. The long hoped for economic recovery hasn’t really materialized yet. Web ad sales have been terrible and interest in stock market blogs was flat at best.

Thankfully, the portfolios I track for Fat Pitch Financials did pretty well in 2011. The flagship Fat Pitch Financials Port that I track at Marketocracy returned 5.98% last year. Since inception, it is up 56.58% or 6.35% annualized. The Special Situations Real Money Portfolio had a rough year, but it was still able to return 7.14% in 2011. Since inception, the Special Situations Real Money Portfolio is 187.7% or 25.4% annualized! Finally, the Workouts model at Covestor recovered nicely in the last quarter of 2011, so it was able to return 9.51% in 2011. Since inception, it is up 7.07% or 6.01% annualized (Edit: Workout model numbers updated with end-of-year results). My hope that in 2012 I will start attracting some subscribers to the Workouts model now that it is starting to outperform the market.

I plan on kicking off 2012 with a return to fundamentals. I recently finished reading Quantitative Strategies for Achieving Alpha by Richard Tortoriello. My favorite investment tool, StockScreen123 also just upgraded its backtesting function to include dividends in returns, four new benchmarks that include dividends (total returns), and a new screening function for examining stock splits. I’m planning to combine what I learned in Tortoriello’s book with the new features of StockScreen123 to create a series of posts that examine how well the fundamental ratios most watched by value investors performed over the past decade. My end goal is to create a Ted Williams style strike zone chart that will show me when to swing for the fat pitch stocks in 2012.

What are the “Top 5″ blogs (or online resources) you particularly enjoy reading?

George — Wed, 21 Dec 2011 03:27:11 +0000

Shai Dardashti just sent me a survey asking:

What are the “Top 5″ blogs (or online resources) you particularly enjoy reading?

This is apparently part of Shai’s excellent New Years Resolution for 2012, which is to learn faster and more efficiently. I think this is a great effort. I encourage you to also respond to his survey. Just click on What are the “Top 5″ blogs (or online resources) you particularly enjoy reading? to share your favorite sites.

Here are the responses so far:

Please also share your responses to this survey in the comments section below.

Value Investing Research Group on Mendeley

George — Fri, 09 Dec 2011 03:25:13 +0000

I just started playing around with a an online reference manager and social network for researchers called Mendeley. I’ve found it to be a great tool for organizing my PDFs and online links to academic papers. Mendeley allows you to organize, tag, backup, sync, and annotate your PDFs. The desktop software for Mendeley is easy to use and has a great interface. The really unique aspect to this reference organizing tool is that you can create public groups to collaborate with others interested in the topic you are researching.

I was surprised to find that there wasn’t a value investing group on Mendeley. I solved that little issue real by quickly setting up a group simply called Value Investing. I welcome fellow value investors to join this group and share their collections of papers.

I’m still looking for a collection of Benjamin Graham papers I have somewhere on my hard drive that I will add to the group as soon as I find them. So far I have some classic Piotroski and Altman papers listed in the group. Please add your favorite value investing references to the list. I could see this group becoming a great resource for value investing students and researchers given the excellent collaboration features that Mendeley groups provide.

The Moats Book Seeks Editors

George — Tue, 06 Dec 2011 12:22:51 +0000

The content for Bud Labitan’s Moats: Competitive Advantages Of Buffett& Munger Businesses book is now complete. The book is in the final editing phase. Anyone who wants to proofread, edit, and enhance a chapter can join the editing team by visiting http://www.frips.com/book.htm.

The book currently includes over 70 businesses. These include the following:

Acme Brick Company, assigned to Adam Ward, UNO-CBA.
American Express Co. (AXP), Dr. Maulik Suthar, Gujarat, India.
Applied Underwriters, assigned to Adam Ward, UNO-CBA.
Ben Bridge Jeweler, assigned to Beryl Chavez Li, University of Manchester, UK.
Benjamin Moore & Co., assigned to Mr. Jack Wang CPA, Lexico Advisory.
Berkshire Hathaway Group
Berkshire Hathaway Homestate Companies, assigned to Beryl Chavez Li, University of Manchester, UK.
BoatU.S., assigned to Peter Chen, Singapore.
Borsheims Fine Jewelry, assigned to Tariq Khan, UNO-CBA.
Buffalo NEWS, Buffalo NY,
Burlington Northern Santa Fe Corp. assigned to David Leoy.
Business Wire, assigned to Larry Harmych.
BYD, assigned to Kevin Walsh, UNO-CBA.
Central States Indemnity Company, assigned to Azalia Khousnoutdinova, UNO-CBA
Clayton Homes, assigned to Erin Sestak, UNO-CBA.
Coca Cola (KO) assigned to Sebastian Jung, UNO-CBA
ConocoPhillips (COP), assigned to Adam D. Studts, PE, UNO-CBA.
CORT Business Services, assigned to Erin Sestak, UNO-CBA.
Costco Wholesale (COST), assigned to Jubin Jacob, AUC-SOM.
CTB Inc., assigned to Todd Sullivan.
Fechheimer Brothers Company, assigned to Ben Albaitis.
FlightSafety, assigned to Mark Murillo, Memorial High School
Forest River, assigned to Richard Konrad, CFA, Value Architects Asset Management.
Fruit of the Loom®, Dr. Maulik Suthar, Gujarat, India.
Garan Incorporated, assigned to Dr. Edwin Fuentes
Gateway Underwriters Agency, assigned Daniel Rudewicz, CFA of Furlong Financial, LLC.
GEICO Auto Insurance assigned to Florian Beil, UNO-CBA
General Re, assigned to Raghu Dasari, UNO-CBA.
H.H. Brown Shoe Group, assigned to Mervyn H. Teo (Singapore)
Helzberg Diamonds, assigned to Natalja Callahan, UNO-CBA
HomeServices of America, assigned to Sebastian Jung, UNO-CBA
IBM
International Dairy Queen, Inc., assigned to Tariq Khan, UNO-CBA
Iscar Metalworking Companies, assigned to Kevin Walsh, UNO-CBA.
Johns Manville, assigned to Manpreet Singh Saran.
Johnson & Johnson (JNJ), Beryl Chavez Li. & Jubin Jacob.
Jordan’s Furniture, assigned to Zehao Sun.
Justin Brands, Dr. Maulik Suthar, Gujarat, India.
Kraft Foods (KFT), assigned to Andrea Tagart, UNO-CBA.
Larson-Juhl, assigned to David Arthur Dawes
Lubrizol, with Scott Thompson, MBA.
M&T Bank Corp (MTB), Cliff Orr, Kellogg-Northwestern.
Marmon Holdings, Inc., assigned to Robert Williams.
McLane Company, Dr. Maulik Suthar, Gujarat, India.
Medical Protective, assigned to Michael Murillo, KCUMB
MidAmerican Energy Holdings Company, assigned to Dr. Maulik Suthar, Gujarat, India.
MiTek Inc., assigned to Mr. Jack Wang CPA, Lexico Advisory.
Moody’s (MCO), assigned to Raghu Dasari, UNO-CBA.
National Indemnity Company, assigned to Jen Iwanski, UNO-CBA.
Nebraska Furniture Mart, assigned to Julie Rosenbaugh, UNO-CBA.
NetJets®, assigned to Christian Labitan.
PacifiCorp., assigned to Beryl Chavez Li, University of Manchester, UK
Precision Steel Warehouse, Inc., assigned to Adam D. Studts, PE, UNO-CBA.
Procter & Gamble (PG), assigned to Beryl Chavez Li, University of Manchester, UK
RC Willey Home Furnishings, assigned to Azalia Khousnoutdinova, UNO-CBA.
Richline Group, Daniel Doyon, Purdue University.
Scott Fetzer Companies, Cliff Orr, Kellogg-Northwestern.
See’s Candies, assigned to Jen Iwanski, UNO-CBA.
Shaw Industries, Richard Konrad, CFA, Value Architects Asset Management
Star Furniture, assigned to Pamela A. Quintero.
The Pampered Chef® assigned to Julie Rosenbaugh, UNO-CBA.
TTI, Inc., assigned to Peter Chen, Singapore.
United States Liability Insurance Group, assigned to Stephen Chan, University of Manchester, UK.
US Bancorp (USB), assigned to Richard Konrad, CFA, Value Architects Asset Management.
USG Corp (USG),
Wal-Mart (WMT) with Florian Beil, UNO-CBA.
Washington Post (WPO), assigned to Andrea Tagart, UNO-CBA.
Wells Fargo (WFC), assigned to Natalja Callahan, UNO-CBA.
Wesco Financial Corporation, assigned to Stephen Chan, University of Manchester, UK.
XTRA Corporation, assigned to Ryan M Shuck, UNO-CBA

Special Situations Real Money Portfolio Third Quarter 2011 Update

George — Mon, 03 Oct 2011 13:15:45 +0000

The Special Situations Real Money Portfolio ended the third quarter of 2011 with a balance of $38,633.48 when the market closed on September 30, 2011. The portfolio is down 5.69% for the year. In comparison, the total year to date return for the S&P 500 was -8.68% as of September 30, 2011. In general, 2011 has been a rough year for the stock market so far.

Thankfully, the Special Situations Real Money Portfolio has produced a total return of 221.95% since inception back in 2004. The annualized rate of return to date is still a healthy 23.63%.

Special situation opportunities have been exceedingly hard to find. Many of the available deals have been of lower quality and higher risk variety. I am hoping things will turn around soon.

Balance: $38,633.48
IRR: 23.63%
Return: 221.95%
YTD: -5.69%

Summer’s Over

George — Fri, 30 Sep 2011 11:01:39 +0000

My extended summer vacation from blogging is over. I needed a break at the beginning of summer, so I took a couple of weeks off. However, I then found it extremely difficult to get motivated to start up again.

I caught a form of writers block that has been very hard to shake. The economic funk we are in has put me in a very unmotivated mood. My guess is that the same thing is happening to others. This Great Recession must be causing a massive physiological impact across society.

Have these economic times also had an impact on your motivation and mood? How have you handled it?

Inflation Adjuster Android App

George — Fri, 17 Jun 2011 16:38:46 +0000

I just published my first mobile app! It’s called Inflation Adjuster and it works on Android devices. Inflation Adjuster is a quick and easy to use tool that calculates the dollar price equivalent between two years.

Inflation Adjuster calculates what the buying power of a dollar amount in a certain year is worth in another year. This inflation calculator uses the average annual Consumer Price Index (CPI-U) produced by the U.S. Bureau of Labor Statistics.

The CPI represents changes in prices of all goods and services purchased for consumption by urban households. This index value has been calculated every year since 1913. For the current year, the latest monthly index value is used. This past Wednesday the Bureau of Labor Statistics reported that the Consumer Price Index for All Urban Consumers (CPI-U) increased 0.2 percent in May on a seasonally adjusted basis and over the last 12 months, the all items index increased 3.6 percent before seasonal adjustment.

In addition to estimating the buying power of a certain dollar amount, the Inflation Adjuster also reports on the total percent nominal price change and the annualized rate of inflation for the selected time period.

Start discovering the REAL value of prices with this simple handy Inflation Adjuster tool today! Please download this free app to your Android phones and provide some feedback. Be sure to provide the name of the device you used to try my App out on when providing feedback. I’m also planning to make additional apps for Android in the future, so I’m open to any suggestions for my next project.

Festival of Stocks: Colgate-Palmolive, Gambling and Kids

George — Mon, 16 May 2011 13:37:40 +0000

Welcome to the May 16, 2011 edition of the Festival of Stocks. The Festival of Stocks is a blog carnival dedicated to highlighting bloggers best posts on stock market related topics. Fat Pitch Financials is the actual birth place of this online weekly event, so it is always a special occasion when I get the chance to host this roving event.

If you aren’t already familiar with my blog, Fat Pitch Financials, it is a value investing blog with a focus on wide moat companies selling at substantial discounts and Benjamin Graham style workouts. I encourage you to subscribe for free to my blog feed to keep up with my latest postings.

You should also check my Fat Pitch Financials Contributor’s Corner while you are here. It is my premium members only community dedicated to unique arbitrage and special situation opportunities.

Now that you are familiar with Fat Pitch Financials, let’s take a look at this week’s Festival of Stock participants. Here are this week’s entries: