It’s far better to buy a wonderful business at a fair price than to buy a fair business at a wonderful price

   Warren Buffett



Quality unlike value has no universally accepted definition, though these two strategies are tightly linked. Novy-Marx (2014) [1] notes: "Quality can even be viewed as an alternative implementation of value — buying high quality assets without paying premium prices is just as much value investing as buying average quality assets at a discount". Despite being similar in spirit, the strategies pick very different assets: high quality securities are expensive, while high value assets may be in general  of low quality. Asness et al. (2013) [2]  define quality as "characteristics that investors should be willing to pay a higher price for, everything else equal" and suggest an intuitive way to identify determinants of higher prices for stocks. Recall the Gordon growth model which equates price of a stock to discounted value of its future payoffs:


$ P_{t} = \dfrac{D_{t+1}}{r-g} = \dfrac{D_{t} (1+g)}{r-g} \tag{1} $


Divide both sides of the equation by the book value, then multiply and divide the right hand side by profit to obtain:


$\dfrac{P_{t}}{B_t} = \dfrac{(Profit_t/B_t)\times(D_{t}(1+g)/Profit_t)}{r-g} \propto \dfrac{Profitability \times Payout Ratio}{Required Return - Growth}\tag{2} $


So, prices are higher for: i.) profitable firms; ii.) firms with growing profits; iii.) safe stocks — i.e. stocks with lower required return; iv.) payout ratio — the fraction of firm’s profits redistributed to its shareholders. Note that keeping everything else equal is important, since, for instance, a high current payout may signal low future profitability or growth and consequently low current price (consider the management that targets dividend level and by doing so, misses good investment opportunities); thus high payout signals a higher price only when the other determinants are kept constant.




Factors such as momentum or value are rather straightforward to construct, quality, on the other hand, is a multidimensional phenomenon, thus the measurement issue naturally arises. Piotroski (2000) [3] suggests an accounting-based measure of financial strength which encompasses profitability, liquidity and operating efficiency. Piotroski’s F-score is defined as a sum of nine binary variables:


\begin{align*}  F_{score} = F_{ROA} + F_{\Delta ROA} + F_{CFO} + F_{ACCRUAL} + F_{\Delta MARGIN} &+ F_{\Delta TURN} + F_{\Delta LEVER} + \\ &+ F_{\Delta LIQUID} + F_{EQ-OFFER} \tag{3} \end{align*}


Each variable on the right hand side of  equation 3 equals one if it signals high quality, and zero otherwise, so the highest quality stocks have the F-score of 9. The first four variables control for profitability and capture firms’ ability to generate funds internally: ROA and DROA are the net income before extraordinary items and its growth, CFO is the cash flow from operations. All three signals are scaled by the total assets and equal one, if positive comparing to the previous year; FACCRUAL equals one if CFO > ROA. Operating efficiency is measured by changes in gross profits scaled by total sales (FDMARGIN) and changes in turnover ratio (FDTURN), these indicators also equal one if changes are positive. Finally, the last three variables grasp the capital structure and ability to service debt: so FDLEVER, FDLIQUID, FEQOFFER equal 1 whenever the long-term debt to total asset decreases, current ratio increases, and firm issued no equity in the previous year. Note that in terms of equation 2 first four signals account not only for profitability, but also for growth, and the last three variables reflect firm’s resilience to potential distress, therefore capturing safety. The F-score of 8-9 indicate high quality stocks. [Asness et al. , 2013] [2]  take the next step and construct the quality-minus-junk (QMJ) factor by evaluating each stocks’ characteristics relative to the whole cross-section. For example, the low exposure to market risk (BAB factor from Frazzini and Pedersen  (2014) [4] ), low idiosyncratic volatility and low leverage are among characteristics that measure safety.  In order to put each variable on equal footing, the authors suggest: i.) rank them with respect to the cross-section, so for market beta the vector of ranks is $r_{BAB}=rank(\beta)$ and the lowest-beta stock has the highest rank; ii.) standardize the rank vector, obtaining the z-score: $z_{BAB}=(r-\mu_r)/\sigma_r$, where $\mu_r$, $\sigma_r$ are the mean and standard deviation of the ranks respectively; iii.) perform the previous steps for the other determinants and get the aggregated z-score:


$Safety = z(z_{BAB} + z_{IVol} + z_{LEV}+z_{O}+z_{Z}+z_{EVol}) \tag{4} $


Where individual z-scores represent low exposure to the market (BAB), low idiosyncratic volatility (IVol), low leverage (LEV), low probability of default (Ohlson O-Score and Altman Z-score) and low ROE volatility (EVol). Note that the z-score of a sum of $N$ zero-mean unit-variance variables is equivalent to the average of these variables scaled by $\sqrt{N}$. Finally, the quality is measured by  averaging its contributing aspects:


$ Quality = z(Profitability + Growth + Safety + Payout)\tag{5} $


The approaches of Piotroski (2000) [3] and Asness et al.  (2013) [2]  capture the multidimensional nature of quality, however, they require a lot of data. Fortunately, there are also simple measures, for instance, Novy-Marx  (2013) [5]  identifies the gross profitability premium, showing that profitable firms earn higher returns despite having lower book-to-market ratios, this factor is subsequently employed by Fama and French  (2014) [6]  in their new five-factor model. Finally, Novy-Marx (2014) [1]  compares the performance of seven different measures including Piotroski’s F-score and finds the gross profitability to be superior to all of them. The reported correlations are insightful for selecting the right criteria,  as correlations between a classical value strategy and different quality strategies vary between .2  and -.58 according to Novy-Marx (2014) [1]. Overall, the exact approach to quantify quality should depend on the final goals set by researcher — a simple one-dimensional measure like gross profitability may be sufficient from an academic perspective, but in practice, we are interested also in the joint behavior of different aspects of  quality and their individual contributions to rewards and risks associated with this factor.




Quality is widely employed by practitioners, for example, since 2012 Société Générale computes the Global Quality Income Index by selecting stocks with the highest Piotroski’s F-score; MSCI Quality Index includes stocks conditional on their  leverage, ROE and earnings volatility. Interestingly, until now quality has not been drawing a lot of attention from academics, thus the research exploring its joint dynamics with other factors, sources of risk, etc. is in preliminary stage. As for now, the empirical evidence on quality may be summarized as a number of stylized facts (for further reference see Asness et al.  (2013) [2], Novy-Marx (2014)  [1]):


1. Quality is persistent, i.e. a stock that is profitable, safe, well managed and has high growth is likely to possess these attributes in the future.

2. High quality stocks tend to have higher price-to-book ratios relative to cross-section. Asness et al.  (2013) [2], report that one standard deviation increase in the quality score (measured by equation 5) leads to 0.32 standard deviation change in price-to-book ratio. Explanatory power of quality is, however, low — it captures only up to 31% of cross-sectional variation in prices.

3. Profitability and Growth command higher prices, in contrast to Safety and Payout. The low price of Safety is likely to occur due to the leverage constraints, consistent with the theory of Frazzini and Pedersen  (2014) [4], where risky assets have higher prices and, consequently, lower expected returns.  Lower price of Payout could be driven by reverse causality between stock price and  share issues (repurchase) decisions, i.e. management decides to repurchase shares when prices are low.

4. Quality-minus-junk portfolio earns positive excess return and has positive and significant alpha in the standard 4-factor model. Furthermore, the QMJ factor has negative market, size, and value exposures. In other words, QMJ takes a long position in low-beta large cap stocks.

5. Price of quality varies over time — so the QMJ factor risk premium is high when the price is low and vice versa.

6. A long-only quality strategy shows significant three-factor alphas, due to the negative loading on the value factor. However, the CAPM alphas are not significant for most quality measures, except for gross profitability and, marginally, for Piotroski’s F-score.

From risk management perspective, quality appears to be a potent augmentation of value investing— indeed the returns of these two strategies are negatively correlated. Novy-Marx (2014)  [1]  shows that combination of quality and value allows to reduce maximum drawdown from -43% for pure HML portfolio to -19% for joint value-gross profitability strategy in the universe of large-cap stocks. Asness et al.  (2013) [2]  show that during the extreme market conditions QMJ performs even better, probably capturing the flight to quality effect. Nevertheless, very little is still known about each of the quality’s aspects individual contribution to risks of the strategy and their evolution over time.




Although quality is a relatively new phenomenon in academic research, it is already being used by practitioners. Quality may be viewed as another dimension of value-investing, while the latter looks for cheap firms with average productivity, the former seeks highly productive assets albeit at premium prices. QMJ factor earns positive return and significant and positive alpha, controlling for traditional factors, thus given the negative exposures to market, value, size, and small positive exposure to momentum, the very existence of the quality premium, appears to be rather puzzling from the risk-based perspective. Overall, there is still much to explore about quality and especially about its risks.


  1. Quality Investing,
    Novy-Marx, Robert
    , Working Paper, (2014)
  2. Quality Minus Junk,
    Asness, Clifford S., Frazzini Andrea, and Pedersen Lasse H.
    , Available at SSRN 2312432, (2013)
  3. Value investing: The use of historical financial statement information to separate winners from losers,
    Piotroski, Joseph D.
    , Journal of Accounting Research, p.1–41, (2000)
  4. Betting against beta,
    Frazzini, Andrea, and Pedersen Lasse Heje
    , Journal of Financial Economics, Volume 111, Number 1, p.1–25, (2014)
  5. The other side of value: The gross profitability premium,
    Novy-Marx, Robert
    , Journal of Financial Economics, Volume 108, p.1–28, (2013)
  6. A five-factor asset pricing model,
    Fama, Eugene F., and French Kenneth R.
    , Journal of Financial Economics, (2014)