Value investing backtests: Our analysis of 13 AAII Value Strategies

Value investing backtests: Our analysis of 13 AAII Value Strategies

October 20, 2014 Research Insights, Value Investing Research
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(Last Updated On: January 18, 2017)

Does Complexity Imply Value? AAII Value Strategies from 1963 to 2013

Abstract:

We compare the performance of 13 value investing screens used by practitioners against a simple model based on buying stocks with the lowest enterprise multiple. Our sample of value investing screens underperform the simple lowest enterprise multiple strategy. The one exception is the Piotroski F-Score screen, which has similar performance relative to the enterprise multiple strategy. Overall, the evidence suggests that simple value investing models can perform just as well as, if not better than, more complex value investing models.

Alpha Highlight:

Having spent 15+ years conducting value investing backtests to find the holy grail of systematic value investing, I sometimes wonder the following: Is there any concept or idea out there that we HAVE NOT examined?

The problem is that most of the ideas we test come from internal ideas/discussions and/or academic research articles. We rarely look to practitioners as a source for ideas.

Last summer, Meb Faber suggested that we examine the value strategies posted to the American Association of Individual Investors value-based stock screens:

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

We analyze 13 different value screens posted to the site and our favorite valuation metric–EBITDA/TEV.

We assess all of these strategies using our best-in-class backtesting technology and procedures.  At times our approach differs, at the margin, relative to the AAII screens. We also focus on a large liquid universe and incorporate delisting information using the appropriate algorithms. In other words, our study serves as a “second opinion” on the various value strategies proposed on the website.

The strategies we analyze are as follows:

  1. Fundamental Rule of Thumb (FRT).  This screen excludes ADRs, financials, and real estate firms. Passing firms need to have their total liabilities to total assets ratio less than or equal to the universe’s median ratio. The fundamental rule of thumb is constructed by adding earnings yield, retained earnings to book value, and dividend yield. Earnings yield is earnings per share divided by the price of the common stock. Retained earnings to book value is earnings per share minus dividends per share divided by book value per share. Dividend yield is dividend per share divided by the price of the common stock. Final results are top 50 companies with highest fundamental rule of thumb values.
  2. Graham Enterprising Screen (GR_ES). This strategy is loosely based on Ben Graham writings. Criteria are as follows: price to earnings ratio’s rank is less than or equal to 10th percentile (lowest 10% of the universe); current ratio is greater than or equal to 1.5; long-term debt to working capital is between 0 and 1.1; EPS in each of the last five years have been positive; EPS of last fiscal year (and trailing 12 months) is greater than EPS from five years ago; company has paid a dividend over the last 12 months; price-to-book ratio is less than or equal to 1.2.
  3. Graham Defensive Utility (GR_D_U).  This screen only includes companies in the utility sector. Criteria are as follows: long-term debt-to-equity ratio of the last fiscal year is less than 2; EPS in each of the last seven fiscal years have been positive; seven years EPS geometric growth rate is greater than 3%; a dividend has been paid in the last seven fiscal years; price-to-earnings ratio (using average of past 3 year earnings) is less than or equal to the inverse of the AAA yield plus 2; the product of the price-to-earnings ratio multiplied by the price-to-book ratio is less than or equal to 1.5 times the inverse of the AAA yield plus 2.
  4. Graham Defensive Non-Utility (GR_D_NU).  This screen excludes companies in utility sector. Criteria are as follows: long-term debt-to-equity ratio of the last fiscal year is less than 2; long-term debt to working capital is between 0 and 1.1, exclusive; EPS in each of the last seven fiscal years have been positive; seven years EPS geometric growth rate is greater than 3%; dividend has been paid in the current year and in each of the last seven fiscal years; price-to-earnings ratio (using average of past 3 year earnings) is less than or equal to the inverse of the AAA yield plus 2; the product of the price-to-earnings ratio multiplied by the price-to-book ratio is less than or equal to 1.5 times the inverse of the AAA yield plus 2.
  5. Graham Enterprising Investor Revised (GR_E_I).  This screening is slight revision of the Graham Enterprising Screen. Criteria are as follows: price to earnings ratio’s rank is less than or equal to 25th percentile (lowest 25% of the universe); current ratio is greater than or equal to 1.5; long-term debt to working capital is less than 1.1; EPS in each of the last five years have been positive; EPS of the last fiscal year (and trailing 12 months) is greater than EPS from five years ago; company has paid a dividend over the last 12 months; price-to-book ratio is less than or equal to 1.2.
  6. Magic Formula (MF). This screen seeks to find the best combination of value and quality. The screen excludes financial and utility companies. First, companies need to have return on capital greater than 25% (return on capital is calculated from earnings before interests and taxes divided by total tangible capital). Finally, the screen selects 30 stocks with the highest earnings yield (earnings yield is EBIT divided by enterprise value).
  7. Dogs of the Dow 10 (DOW 10). This screen only includes Dow Jones industrial average composite companies. The screen only includes the 10 highest dividend yielding stocks.
  8. Dogs of the Dow 5 (DOW 5).  This screen only includes Dow Jones industrial average composite companies. The screen only includes the 5 highest dividend yielding stocks.
  9. Cash Rich Firms (CRF). This screen excludes financial, utility and real estate firms. Criteria are as follows: EPS of the last fiscal year is positive; stock price is higher than $5.00; total liabilities to total assets ratio of the last fiscal year is less than the industry’s median ratio; long-term debt to total capital ratio of the last fiscal year is less than the industry’s median ratio; cash to price is greater than 20; cash per share is at least 20% of the stock price; net cash (cash after current liabilities) to price is greater than 20; net cash per share is at least 20% of the stock price.
  10. Piotroski High F-Score (FSCORE).  This screen is based on the methodology in Piotroski and So (2012).  Their methodology involves computing 9 signals. Of the nine financial performance signals, four of the signals are based on profitability; three are based on changes in financial leverage and liquidity; and two are based on operational efficiency.   Firms need to be in the top 20% of the universe based on book to market to be included. Firms also need to score an 8 or a 9 on the Piotroski 9 point scale.
  11. Price to Free Cash Flow (PFCF). Financial and real estate companies are excluded. Criteria are as follows: free cash flow per share of each of the last five fiscal years has been positive; price to free cash flow per share ratio is lower than the industry’s median ratio; price to free cash flow per share ratio is lower than the company’s five year average ratio. Final companies are those 30 companies with the lowest price-to-free cash flow per share ratio.
  12. Weiss Blue Chip Dividend Yield (WEISS). Real estate companies are excluded. Criteria are as follows: dividends have been paid in each of the last seven fiscal years; dividends have been increased at least three times and have never been decreased in the last seven fiscal years; numbers of share outstanding in the last fiscal year is greater than or equal to five millions; institutional ownership is greater than 50%; EPS have increased at least four times over the last seven fiscal years; current dividend yield is within 10% of the seven year average dividend yield; current ratio of last fiscal year is greater than or equal to 2; for utility companies the dividend payout ratio of the last fiscal year is less than or equal to 0.85; for non-utility companies, the long-term debt to equity ratio of the last fiscal year is less than or equal to 0.5 and the dividend payout ratio of the last fiscal year is less than or equal to 0.5.
  13. O’Shaughnessy Value Screen (O’SH_V). Utility companies are excluded. Criteria are as follows: the market capitalization is greater than the average market capitalization of the universe; the numbers of shares outstanding from the last fiscal year is greater than the average of the universe; cash flow per share of the last fiscal year is greater than the average of the universe; total sales of the last fiscal year is greater than 1.5 times the average of the universe. Final companies are those 50 companies with the highest dividend yield.
  14. EBITDA/TEV (EBITDA/TEV). All financial firms are excluded. Similar to the Loughran and Wellman (2011), we compute Total Enterprise Value (TEV) as Market Capitalization + Short-term Debt + Long-term Debt + Preferred Stock Value – Cash and Short-term Investments. Earnings before interest and taxes and depreciation and amortization (EBITDA) is computed as Operating Income Before Depreciation  + Non-operating Income. The simple value strategy involves selecting the top decile of firms ranked on EBITDA/TEV (enterprise multiple).

Here are the main results:

AAII_horserace_v01.docx - Microsoft Word_2014-10-20_07-44-00
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

We also run a horse race between EBITDA/TEV and FSCORE–the two top-performing metrics in this study. FSCORE wins!

AAII_horserace_v01.docx - Microsoft Word_2014-10-20_07-45-56
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Summary:

We’ll simply quote our own paper:

We find that more complex value strategies on AAII, on average, underperform the simple EBITDA/TEV ratio. However, the “Piotroski High F-Score Screen (FSCORE),” which is a close approximation to the strategy outlined in Piotroski (2000) and Piotroski and So (2012), has similar performance.

For mid and large-cap firms, an annually rebalanced equal-weight portfolio of FSCORE firms earns 16.74% a year, a 0.70 Sharpe Ratio, and a 0.332% monthly 4-factor alpha. These results are similar for a simple EBITDA/TEV value stock screen, which earns 16.52% a year, a 0.65 Sharpe Ratio, and a 0.370% monthly 4-factor alpha. Overall, the evidence suggests that simple value models can perform just as well, if not better, than more complex value models.

In summary, when it comes to value investing backtests, complexity doesn’t seem to add much value. However, focusing on cheap stocks and using some element of fundamental analysis to seperate winners from losers does seem to add value, at the margin.


Note: This site provides no information on our value investing ETFs or our momentum investing ETFs. Please refer to this site.


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Please remember that past performance is not an indicator of future results. Please read our full disclaimer. The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. This material has been provided to you solely for information and educational purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed by the author and Alpha Architect to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. No part of this material may be reproduced in any form, or referred to in any other publication, without express written permission from Alpha Architect.


Definitions of common statistics used in our analysis are available here (towards the bottom)




About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray received a PhD, and was a finance professor at Drexel University. Dr. Gray’s interest in entrepreneurship and behavioral finance led him to found Alpha Architect. Dr. Gray has published three books: EMBEDDED: A Marine Corps Adviser Inside the Iraqi Army, QUANTITATIVE VALUE: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, and DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His numerous published works has been highlighted on CBNC, CNN, NPR, Motley Fool, WSJ Market Watch, CFA Institute, Institutional Investor, and CBS News. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.


  • Chris

    Would love to see the results of the research with a monthly rebalance instead of annual. The AAII screens are designed for monthly rebalance, and my experience using them is that performance degrades when extended to annual rebalance.

  • Jack Vogel, PhD

    Good idea, we will do this in the future. More frequent re-balancing should help all the strategies, so it would be interesting to see if it changes the results.

  • Chris Scott

    Any reason for the switch in preference of EBITDA/TEV over EBIT/TEV that was previously preferred in the book?

  • just staying in line with the research paper we were referring to. EBIT/TEV is slightly better when doing a head-to-head horse race

  • Steve

    As long as the cost factor is looked at as well 🙂

  • Jack Vogel, PhD

    When we run the tests we will do that.

  • Steve

    You guys are awesome!

  • Michael Milburn

    I use the the AAII SIPro database and appreciate this study. It’s actually a pretty good database to build out ideas from your Quantitative Value book also. Outside of the F-Score model (which often has very few stock to choose from currently), the upward earnings revision models have been top performers among others (including a few w/ price momentum).

    You may already have this, but If the link works, here’s a link w/ rankings from 1999 to present (and i think they use monthly rebalancing).
    http://www.aaii.com/stock-screens/performance?sort=total&order=desc&adv=yes

  • Doug01

    The comparison of EBITDA/TEV to Piotroski is a bit of an apples and oranges comparison. Piotroski is a value screen, along with a fundamental screen, whereas EBITDA/TEV is just a value screen. If you used EBITDA/TEV, and then screened for stocks with a Piotroski score of 8 or 9, I wonder if EBITDA/TEV would come out ahead. I have heard the case made that the Piotroski score may be relevant to multiple stock screens.

    http://www.stockopedia.com/content/one-indicator-to-rule-them-all-the-piotroski-f-score-66530/

    Also, I have to bring up momentum. Mixing value and momentum is anathema to a devout value investor. Nevertheless, when I’ve seen backtests of value and momentum together, return and/or volatility are decreased. Some shops (DFA, Bridgewater) don’t seem to mix the two directly, but do screen out negative momentum stocks as an entry criteria. I believe that they also retain positive momentum stocks, when it comes to exit decisions.

    There was request for redoing the study with monthly rebalancing. For investors, it might be more relevant to redoing the study with rebalancing over time periods greater than 1 year. What data I’ve seen suggests that the value premium lasts up to 5 years. Especially in a taxable account, monthly rebalancing and even annual rebalancing has a significant headwind.

  • Jack Vogel, PhD

    Thanks Michael. For this paper we focused on “Value” strategies.

  • Jack Vogel, PhD

    I agree there may be an apples to oranges comparison, but the goal of the study was to compare a simple model (EBITDA/TEV) to more complicated models. Also, while longer holding periods may work for value screens, the implementation can add complexity (Do I re-balance every 5 years? Do I create overlapping portfolios? If I create overlapping portfolios, how do I fund these new purchases?).

  • Guillaume Kaminer

    What is the result of using the best decile of ebitda/tev and screening for 8 and 9 F-score ? Any improvement with that combo ?

  • We tried to stick to the website and the simple EBITDA/TEV screen.
    The techniques you are mentioning are what we describe in our book Quantitative Value. And as the book shows, these sort of combinations have historically been the best performining

  • Bruno Facca

    Great article! Please, Have you tried to combining FSCORE and EBITDA/TEV in a single screen?

  • Jack Vogel, PhD

    Thanks! Yes, we have tested this, and using F-score after screening on EBIT/TEV makes sense.

  • Nrg359

    This is a great article, I would also be curious if you tried combining momentum or accrual factors with the F score model?

  • We’ve tested just about everything.

    We have some posts on the value and momentum published and we’ll have some more on the subject in the coming months. Stay tuned.