As Warren Buffett rightly says, value and growth are joined at the hip. It seems like a perfectly sensible strategy to pay more for high-quality businesses than for low-quality deep value stocks. And it is… in theory. In practice, it is extraordinarily difficult to find the right trade-off. There has been done some systematic research trying to improve value strategies by including a quality component. Quality, here, means anything one should be willing to pay for (e.g., ROE, ROIC, growth, profitability, etc.) And some of these studies show very counter-intuitive results.
A prominent example of a strategy that supplements a pure value ranking by a quality measure is Joel Greenblatt’s Magic Formula. In their outstanding book “Quantitative Value”, Wesley Gray and Tobias Carlisle show that the quality component actually decreases the performance of a portfolio based on a value ranking alone. The likely reason for this is the mean reverting nature of return on capital, the used quality measure. Economic theory dictates increased competition if companies demonstrate high returns of capital, and exits of competitors if returns are poor. The new competition decreases returns for all suppliers. Exits of competitors increase returns for prevailing businesses. Betting on businesses with historically high returns seems like a bad idea on average, then.
As ambitious bargain hunters, we try to find high-quality businesses at low prices. Studies, however, show that (in competitive markets) valuation is far more important than quality. And in some cases, due to naïve extrapolation of noise traders, quality is actually associated with lower returns. Lakonishok, Shleifer and Vishny (1994) study exactly that. Their results fly in the face of many investors working hard to find the ‘best’ value stocks. Lakonishok et al. construct value portfolios not only based on current valuation ratios but also on past growth. They define the contrarian value portfolio as having a high Book-to-Market ratio (B/M) — the inverse of P/B — and low past sales growth (GS). The reasoning behind this is that by Lakonishok’s et al. definition, value strategies exploit other investor’s negligence to factor reversion to the mean into their forecasts. This is a form of base rate negligence, a tendency in intuitive decision-making found by Kahneman and Tversky (1982). Lakonishok et al. thus identify stocks with low expected future growth (valuation ratio) and low past growth (GS) that indicate naïve extrapolation of poor performance. They show that this definition of value performs better than a simple definition based only on a valuation ratio (e.g., B/M.) Another way of looking at this is by subdividing the high B/M further into high and low past growth. The low past growth stocks outperform the high past growth stocks by 4% p.a. (21.2% vs. 16.8% p.a.) while the B/M ratios of these sub-portfolios “are not very different.” 
In his excellent book “Deep Value”, Tobias Carlisle shows insightful statistics for these portfolios. The incredible insight is that even if valuation ratios are practically the same, stocks that rank low on quality (past sales growth) perform better than high-quality stocks. One likely reason is mean reversion in fundamentals.
Similar results are also showing in the deepest of value strategies: net-nets. Oppenheimer (1986) shows that loss-making net-nets outperformed profitable net-nets (36.2% p.a. vs. 33.1%), and non-dividend-paying net-nets outperformed dividend-paying net-nets (40.6% vs. 27.0%) from 1970 to 1983. Carlisle confirms these results out of sample from 1983 to 2010. My own backtests confirm these results from 1999 to 2015. My results at least are, however, mainly driven by the higher discount — profitable businesses don’t usually trade at large discounts to NCAV.
Whether you are a full quant or not, if you are trying to pick the ‘best’ stocks from a value screen you are likely making a systematic mistake — unless you are searching for businesses with moats (i.e., a sustainable competitive advantage that prevents a high return on capital to revert to the mean.) But good luck finding such a business in deep value territory consistently.
Regression to the mean is such a strong tendency and is systematically underestimated by market participants that just betting on historically poorly performing businesses outperforms the market. Bannister (2013) finds that betting on “unexcellent” companies (ranking low on growth, return on capital, profitability) outperformed the market from 1972 to 2013 (13.74% p.a. vs. 10.59%). A portfolio constructed of stocks of “excellent” businesses, in turn, underperformed the market (9.77%).
I still think good quality measures (i.e., measures that do not implicitly bet against regression to the mean in fundamentals) are a potent tool for improving a value ranking. It is, however, not as easy as layering a quality screen blindly over a value screen and thereby imply equal weights. A category of quality measures that is of special interest to me is distress/bankruptcy prediction. But even if the such a measure is very good at identifying value traps, there is still the very serious issue of false positives. That is, excluding stocks that actually perform well on average. A too sensitive measure will likely exclude all the ugliest stocks that perform the best. More research is needed to determine a sensible weighting mechanism. The merit of such a measure is dependent on the false negative error rate, false positive error rate, the cost of false negatives, and, importantly, on the cost of false positives. The cost of false positives may be very high for concentrated portfolios. Even if in studies the quality measure can improve performance, that doesn’t mean that it will improve a concentrated value portfolio (20-30 stocks). The reason is that these studies often hold a very diversified portfolio (e.g., a decile). This is quite a number of stocks. If the quality factor excludes 20 extremely cheap stocks, it’s not a big deal. If you were to hold the 30 cheapest stocks in the universe, however, and the quality factor excludes 20 of them and the next cheapest stocks have 2 times the valuation ratio, it is very likely that the performance will suffer. It will dilute the value factor too much. The important thing is to actually backtest your portfolio and not just rely on studies.
Another interesting area of research lies in identifying moats that prevent mean reversion of high return businesses. That, however, still leaves the question open if these businesses are systematically undervalued.
 Gray, Wesley and Carlisle, Tobias: Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, 2013, John Wiley & Sons, Inc., Hoboken, New Jersey, Table 11.1.
 Kahneman, Daniel and Tverky, Amos: Intuitive Prediction: Biases and Corrective Procedures, in D. Kahneman, P. Slovic, and A. Tversky, Eds.; Judgment Under Uncertainty: Heuristics and Biases, 1982, Cambridge University Press, Cambridge, England.
 Lakonishok, Josef, Shleifer, Andrei and Vishny, Robert: Contrarian Investment, Extrapolation, and Risk, The Journal of Finance 49, no. 5, 1994, p. 1555. http://www.jstor.org/stable/2329262 OR http://lsvasset.com/pdf/research-papers/Contrarian-Investment-Extrapolation-and-Risk.pdf
 Lakonishok, Josef, Shleifer, Andrei and Vishny, Robert: Contrarian Investment, Extrapolation, and Risk, The Journal of Finance 49, no. 5, 1994, p. 1549. http://www.jstor.org/stable/2329262 OR http://lsvasset.com/pdf/research-papers/Contrarian-Investment-Extrapolation-and-Risk.pdf
 Bannister, Barry, Stifel Financial Corp., and Eyquem Investment Management LLC from Carlisle, Tobias: Deep Value: Why Activist Investors and Other Contrarians Battle for Control of “Losing” Corporations, 2014, John Wiley & Sons, Inc., Hoboken, New Jersey, p. 137.