Mispricing Drives the Value Premium
There’s extensive literature documenting that value stocks (the stocks of companies with low prices relative to a valuation metric, such as earnings, book value, cash flow or sales) possess a strong, persistent and pervasive tendency to outperform growth stocks.
While there’s no debate about the existence of the value premium, there’s a major debate about the source of the return differential. Some argue that returns reflect compensation for risk; others argue for mispricing.
The mispricing explanation for the value premium is that investors are systematically too optimistic in their expectations for the performance of growth companies, and too pessimistic in their expectations of value companies. Ultimately, prices correct when these expectations aren’t met.
Value And Mispricing
Joseph Piotroski and Eric So, authors of the study “Identifying Expectation Errors in Value/Glamour Strategies: A Fundamental Analysis Approach,” which was published in the September 2012 issue of The Review of Financial Studies, tested the mispricing hypothesis by identifying potential ex-ante biases and comparing the expectations implied by pricing multiples against the strength of firms’ fundamentals. Value strategies would be successful if prices don’t accurately reflect the future cash flow implications of historical information in a timely manner, resulting in equity prices that temporarily drift away from their fundamental value.
Piotroski and So classified and allocated firm-year observations into value and glamour (growth) portfolios on the basis of each firm’s book-to-market ratio. Because a firm’s book-to-market ratio reflects the market’s expectations about future performance, sorting by this metric will group firms on the basis of future performance expectations embedded in prices. Thus, book-to-market ratios serve as an empirical proxy for the relative strength of the market’s expectations about future firm performance.
The authors classified the strength of a firm’s recent financial performance trends using the aggregate statistic F-Score, which is based on nine financial signals designed to measure three different dimensions of a company’s financial condition: profitability, change in financial leverage/liquidity (capital structure) and change in operational efficiency.
Firms with the poorest signals have the strongest deterioration in fundamentals and are classified as low F-Score firms. Firms that receive the highest score have the strongest improvement in fundamentals and are classified as high F-Score firms.
Prior research shows that F-Score is positively correlated with future earnings growth and future profitability levels. Low F-Score firms experience continued deterioration in future profitability, and high F-Score firms experience overall improvement in profitability.
Following is a summary of the authors’ findings, which cover the period 1972 through 2010:
Among firms where the expectations implied by their current value/glamour classification were consistent with the strength of their fundamentals, the value/glamour effect in realized returns is statistically and economically indistinguishable from zero.
The returns to traditional value/glamour strategies are concentrated among firms where the expectations implied by their current value/glamour classification are ex-ante incongruent with the strength of their fundamentals.
Returns to this “incongruent value/glamour strategy” are robust and significantly larger than the average return generated by a traditional value/glamour strategy.
In the academic literature, the explanation for the mispricing is that behavioral errors—such as optimism, anchoring and confirmation biases—cause investors to underweight or ignore contrarian information.
The authors write: “For example, investors in glamour stocks are likely to under-react to information that contradict their beliefs about firms’ growth prospects or reflect the effects of mean reversion in performance. Similarly, value stocks, being inherently more distressed than glamour stocks, tend to be neglected by investors; as a result, performance expectations for value firms may be too pessimistic and reflect improvements in fundamentals too slowly.”
Piotroski and So’s findings were consistent with the mispricing explanation. They concluded that firms with low book-to-market ratios and low F-Scores (weak fundamentals) were persistently overvalued, and firms with high book-to-market ratios and high F-Scores (strong fundamentals) were persistently undervalued. It was in these subsets that the pricing errors were strongest.
The authors also observed that while both the traditional value/glamour strategy (which relies solely on book-to-market rankings) and the incongruent value/glamour strategy produce consistently positive annual returns, the frequency of these positive returns was higher for the incongruent value/glamour strategy. It generated positive returns in 35 out of 39 years over the sample period (versus 27 out of 39 years for the traditional value/glamour strategy).
They also found that annual returns to the incongruent value/glamour strategy were larger than returns to the traditional value/glamour strategy in all but six years, with an average annual portfolio return of 20.8% versus 10.5% for the traditional value/glamour strategy.
The evidence from this study contributed to the growing body of literature demonstrating that, at the very least, the value premium has been too large to be explained solely by a risk story. In other words, it’s an anomaly.
Digging Into The F-Score Anomaly
The success of the Piotroski high F-Score led to its popularity. F-Score data is readily available on several websites (the American Association of Individual Investors, MeetInvest and ValueSignals, for instance). In addition, since May 2002, the American Association of Individual Investors, using its Stock Investor Pro software to run a live version of Piotroski’s screen on paper with regular updates every month, has shown raw returns of 23.7% from January 2005 to April 2015 for the U.S. stock market. However, the impact of market frictions, such as trading costs, liquidity constraints and microstructure effects, weren’t considered. The results are, therefore, only theoretical.
Christopher Krauss, Tom Krüger and Daniel Beerstecher, who authored an October 2015 paper titled “The Piotroski F-Score: A Fundamental Value Strategy Revisited from an Investor’s Perspective,” sought to determine whether the F-score anomaly could be exploited when real-world market frictions and exposure to common factors were considered. They ask: Does the Piotroski high F-Score actually achieve statistically significant, risk-adjusted returns?
The authors tested weekly and monthly rebalancing strategies and both long-only and long-short strategies. Piotroski had suggested shorting low F-Score firms. But the feasibility of strategies addressing capital market anomalies is often more aggressively challenged on the short side due to high costs. To address this issue, the authors chose to create a dollar-neutral portfolio by going short the same dollar amount in the S&P 500 as they were invested in high-F-Score firms.
This strategy is not necessarily market neutral, since the high F-Score firms in the high book-to-market universe may well exhibit an aggregate beta unequal to one. Also, the exposure to small companies and the high book-to-market universe is not hedged. On the other hand, this long-short strategy can easily be implemented at a low cost and is suitable from a practitioner’s perspective.
In terms of market frictions, the authors assumed commissions of 0.1% and round-trip trading costs of 0.5%. They also incorporated liquidity constraints, excluding stocks with a daily trading volume below a certain liquidity threshold. And finally, they incorporated a one-day waiting rule to consider potential microstructure effects, such as the bid/ask bounce. The result is that they delay transactions by one day after the signal.
Following is a summary of their findings:
The value-weighted (equal-weighted) monthly long-only Piotroski screen showed a raw return of 2.42 (2.71)% per month prior to trading costs, with a t-stat of 2.5. Thus, it was statistically significant at the 5% confidence level.
The value-weighted (equal-weighted) long-short monthly strategy produced a raw return of 1.84 (2.01)% per month, with a t-stat of 2.4.
Relative to benchmarks of the S&P 500 and four other indexes—the S&P MidCap 400, the S&P MidCap 400/Citigroup Value, the S&P SmallCap 600 and the S&P SmallCap 600/Citigroup Value—the F-Score strategies also produced superior risk-adjusted returns in measures such as their Sharpe ratios, value and risk, and drawdowns.
Relative to the Fama-French three-factor (beta, size and value), four-factor (adding momentum) and five-factor (adding profitability and investment) models, only a small portion of the returns can be attributed to the different risk factors.
Introducing liquidity constraints (minimum daily volume of $1 million) and introducing the one-day trading lag causes returns to decline from 2.71% per month (the original long-only strategy) to 1.1% per month.
Subtract the estimated transaction costs of 0.007 (0.7%) per month and the strategy no longer seems attractive. In addition, the average number of positions per month decreases from approximately nine holdings to less than three, which certainly is not possible for institutions. Adding a minimum liquidity requirement of $100,000 on the day of the signal leads to monthly raw returns of merely 0.15%, close to the average risk-free rate of 0.11% over the period of study.
When testing the strategy on a weekly rebalancing basis, the authors found that the strategy’s results were even more impressive in raw terms, as returns were greater than 4% per month and t-stats were also higher (larger than 5).
However, once again, liquidity constraints and trading costs make these strategies impractical in the real world, at least for any institutional investor. For example, weekly transaction costs of 0.70% consume the lion’s share of the 1.09% weekly return. In addition, constraining the minimum volume to $30,000 per day renders the strategy virtually unprofitable. What’s more, it’s important to note, the average number of holdings declines from 9.4 to 2.6.
The authors noted that “acting faster upon the new arrival of information leads to much higher annualized returns. However, the higher rebalancing frequency is detrimental to the returns due to elevated transaction costs. Liquidity constraints that render the strategy feasible also render it unprofitable.”
Friction Eats Anomaly Benefits
The authors concluded their results indicate that while the monthly (although not the weekly) strategy might be implementable for a very small individual investor, “any larger scale investor stands no chance of capturing the returns reported by the AAII. Overall, the value of the investment strategy seems to be mainly theoretical.”
Krauss, Krüger, and Beerstecher’s study shows how high frictions can allow anomalies to persist. The study also demonstrates how difficult it is to exploit some anomalies once real-world trading costs and liquidity constraints are imposed.
A fitting conclusion is the following from financial economist and money manager Richard Roll, who, in 2000, was responding to criticisms of the efficient market hypothesis: “I have personally tried to invest money, my client’s and my own, in every single anomaly and predictive result that academics have dreamed up. And I have yet to make a nickel on any of these supposed market inefficiencies. An inefficiency ought to be an exploitable opportunity. If there’s nothing investors can exploit in a systematic way, time in and time out, then it’s very hard to say that information is not being properly incorporated into stock prices. Real money investment strategies don’t produce the results that academic papers say they should.”
This commentary originally appeared July 6 on ETF.com
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