Does Complexity Imply Value? AAII Value Strategies from 1963 to 2013
Alpha Architect; Drexel University – LeBow College of Business
At this year's Sohn Investment Conference, Dan Sundheim, the founder and CIO of D1 Capital Partners, spoke with John Collison, the co-founder of Stripe. Q1 2021 hedge fund letters, conferences and more D1 manages $20 billion. Of this, $10 billion is invested in fast-growing private businesses such as Stripe. Stripe is currently valued at around Read More
Alpha Architect; Drexel University
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.
Does Complexity Imply Value? AAII Value Strategies from 1963 to 2013 – Introduction
In the academic literature, value investing is characterized as buying stocks that have a low price relative to a measure of earnings or asset value. For example, Fama and French (1992) utilize low price to book ratios as a way to identify “value” stocks. Nonetheless, practitioners invest in multiple “value” strategies that go beyond simply buying low price to book stocks. For example, Warren Buffett, the most famous value investor of our time, advocates that the “buy cheap stocks” investing mantra originally preached by his Benjamin Graham has evolved over time. Buffett has an evolutionary view on value investing: “It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.2” Buffett’s implication is that investors can’t solely focus on price, but also need to think about the quality of a firm (See Frazini, Kabiller, and Pedersen (2013) for a discussion).
To assess alternative views on value investing that go beyond simply buying low price to book stocks, we analyze and compare 13 stock screens labeled as “Value” on the American Association for Individual Investors (AAII) website.3 We compare the performance of the value strategies followed by AAII investors to a simple “low-price” value strategy between 1963 and 2013. We use EBITDA/TEV as our simple low-price value strategy, which has been shown in Gray and Vogel (2012) and Loughran and Wellman (2011) to be the top-performing valuation metric. 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.
The remainder of the paper is organized as follows: Section 1 describes our data; Section 2 presents the results; Section 3 concludes.
Our data sample includes all firms on the New York Stock Exchange (NYSE), American Stock Exchange (AMEX), and NASDAQ with the required data on CRSP and Compustat. The data extend over the time period from July 1, 1963 until December 31, 2013. The sample includes firms with ordinary common equity on CRSP and eliminates all REITS, ADRS, and closed-end funds. AAA historical yields are from Goyal and Welch (2008). Thomson Reuters institutional (13f) holdings database is used for institutional holdings data. CRSP delisting return data is incorporated into the sample using the technique of Beaver, McNichols, and Price (2007). As per the evidence in Beaver, McNichols, and Price (2007), the choice of delisting algorithm might be marginal when assessing market returns, but using their more comprehensive delisting algorithm is very important in the context of assessing extreme value stocks.
To be included in the sample, all firms must have a non-zero market value of equity as of June 30th of year t. Firm fundamentals are based on December 31st of year t-1 (for firms with fiscal year ends between January 1st and March 31st we use year t fundamentals; for firms with fiscal year ends after March 31st we use year t-1 fundamentals). Book to Market is computed on June 30th each year using the book value methodology from Fama and French (2000) and the market capitalization on June 30th. All firms with negative book values are eliminated from the sample.
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