Multiple factors are explaining abnormal equity returns, which could be used to build a profitable equity long-short strategy. The most common are momentum, value, short-term reversal, size factors, etc..
We present an academic research paper that shows additional useful factor – earnings quality. Stocks with high quality (defined via multiple ratios) outperform stocks with low quality. Earnings quality factor portfolio has low to negative correlation to other factor portfolios and could be therefore used as a valuable diversifier in a multifactor long-short equity strategy.
The effect is explained mainly by investors’ behavioral defects. The majority of investors usually overly fixate on actual earnings, and they do not investigate the quality of earnings scrutinizingly. The in-depth analysis, therefore, allows exploiting this inefficiency.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, annualized (geometrically) monthly return of 0.6399%, data from table VIII for “Mix” portfolio in US
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
estimated from t-statistic 9.09, data from table VIII for “Mix” portfolio in US
Notes to Number of Traded Instruments
more or less, it depends on investor’s need for diversification
Notes to Maximum drawdown
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of all non-financial stocks from NYSE, Amex and Nasdaq.
Big stocks are defined as the largest stocks that make up 90% of the total market cap within the region, while small stocks make up the remaining 10% of the market cap. Investor defines breakpoints by the 30th and 70th percentiles of the multiple “Earnings Quality” ratios between large caps and small caps.
The first “Earnings Quality” ratio is defined by cash flow relative to reported earnings. The high-quality earnings firms are characterized by high cash flows (relative to reported earnings) while the low-quality firms are characterized by high reported earnings (relative to cash flow).
The second factor is based on return on equity (ROE) to exploit the well-documented “profitability anomaly” by going long high-ROE firms (top 30%) and short low-ROE firms (bottom 30%).
The third ratio – CF/A (cash flow to assets) factor goes long firms with high cash flow to total assets.
The fourth ratio – D/A (debt to assets) factor goes long firms with low leverage and short firms with high leverage.
Investor builds a scored composite quality metric by computing the percentile score of each stock on each of the four quality metrics (where “good” quality has a high score, so ideally a stock has low accruals, low leverage, high ROE, and high cash flow) and then add up the percentiles to get a score for each stock from 0 to 400. He then forms the composite factor by going long the top 30% of small-cap stocks and also large-cap stocks and short the bottom 30% of the small-cap stocks and also large-cap stocks and cap-weighting individual stocks within the portfolios.
The final factor portfolio is formed at the end of each June and is rebalanced yearly.
Hedge for stocks during bear markets
Yes - Based on the source and related research papers (see, for example, tables 4.2 and 3.5 from paper Jensen-Gaard: Equity Investment Styles – Recent evidence on the existence and cyclicality of investment styles), strategy has a positive return during recession months therefore probably can be used as a hedge/diversification to equity market risk factor during bear markets.
Kozlov, Petajisto: Global Return Premiums on Earnings Quality, Value, and Size
We investigate the return premium on stocks with high earnings quality using a broad and recent global dataset covering all developed markets from 7/1988 to 6/2012. We find that a simple strategy that is long stocks with high earnings quality and short stocks with low earnings quality produces a higher Sharpe ratio than the overall market or similar strategies betting on value or small stocks. This result holds both in the overall sample as well as in the more recent time period since 2005. Because the global earnings quality portfolio has a negative correlation with a value portfolio, an investor wishing to invest in both exposures can achieve significant diversification benefits.
Strategy's implementation in QuantConnect's framework
Assness, Frazzini, Pedersen: Quality Minus Junk
We define a quality security as one that has characteristics that, all-else-equal, an investor should be willing to pay a higher price for: stocks that are safe, profitable, growing, and well managed. High-quality stocks do have higher prices on average, but not by a very large margin. Perhaps because of this puzzlingly modest impact of quality on price, high-quality stocks have high risk-adjusted returns. Indeed, a quality-minus-junk (QMJ) factor that goes long high-quality stocks and shorts low-quality stocks earns significant risk-adjusted returns in the U.S. and globally across 24 countries. The price of quality – i.e., how much investors pay extra for higher quality stocks – varies over time, reaching a low during the internet bubble. Further, a low price of quality predicts a high future return of QMJ.
Jensen-Gaard: Equity Investment Styles – Recent evidence on the existence and cyclicality of investment styles
Over the past two decades, financial academics and investment professionals have documented several anomalies on the global financial markets. A subset of these anomalies, known as equity style strategies, has been shown to yield substantial excess returns, which cannot be explained by traditional finance theory. However, in the light of the financial turmoil during the 2000s, several studies have shown considerable changes in the magnitude of the style-based strategy premiums. The purpose of this thesis is to investigate whether recent data support the continued existence of these premiums and evaluate how these premiums fluctuate in relation to the economic cycle. Furthermore, this thesis provides practical advice on how investors can apply the findings.
Landier, Simon, Thesmar: The Capacity of Trading Strategies
Due to non-linear transaction costs, the financial performance of a trading strategy decreases with portfolio size. Using a dynamic trading model a la Garleanu and Pedersen (2013), we derive closed-form formulas for the performance-to-scale frontier reached by a trader endowed with a signal predicting stock returns. The decay with scale of the realized Sharpe ratio is slower for strategies that (1) trade more liquid stocks (2) are based on signals that do not fade away quickly and (3) have strong frictionless performance. For an investor ready to accept a Sharpe reduction by 30%, portfolio scale (measured in dollar volatility) is given by a simple formula that is a function of the frictionless Sharpe, a measure of price impact, and a measure of the speed at which the signal fades away. We apply the framework to four well-known strategies. Because stocks have become more liquid, the capacity of strategies has increased in the 2000s compared to the 1990s. Due to high signal persistence, the capacity of a “quality” strategy is an order of magnitude larger than the others and is the only one highly scalable in the mid-cap range.
Bouchaud, Stefano, Landier, Simon, Thesmar: The Excess Returns of ‘Quality’ Stocks: A Behavioral Anomaly
This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The “risk view”, whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation for risk exposure. This view is consistent with the Efficient Market Hypothesis. The other view is the “behavioral view”, which states that some investors persistently underestimate the true value of high quality firms. We find no evidence in favor of the “risk view”: The returns from investing in quality firms are abnormally high on a risk-adjusted basis, and are not prone to crashes. We provide novel evidence in favor of the “behavioral view”: In their forecasts of future prices, and while being overall overoptimistic, analysts systematically underestimate the future return of high quality firms, compared to low quality firms.
de Carvalho, Xiao, Soupe, Dugnolle: Diversify and Purify Factor Premiums in Equity Markets
In this paper we consider the question of how to improve the efficacy of strategies designed to capture factor premiums in equity markets and, in particular, from the value, quality, low risk and momentum factors. We consider a number of portfolio construction approaches designed to capture factor premiums with the appropriate levels of risk controls aiming at increasing information ratios. We show that information ratios can be increased by targeting constant volatility over time, hedging market beta and hedging exposures to the size factor, i.e. neutralizing biases in the market capitalization of stocks used in factor strategies. With regards to the neutralization of sector exposures, we find this to be of importance in particular for the value and low risk factors. Finally, we look at the added value of shorting stocks in factor strategies. We find that with few exceptions the contributions to performance from the short leg are inferior to those from the long leg. Thus, long-only strategies can be efficient alternatives to capture these factor premiums. Finally, we find that factor premiums tend to have fatter tails than what could be expected from a Gaussian distribution of returns, but that skewness is not significantly negative in most cases.
Cook, Hoyle, Sargaison, Taylor, Hemert: The Best Strategies for the Worst Crises
Hedging equity portfolios against the risk of large drawdowns is notoriously difficult and expensive. Holding, and continuously rolling, at-the-money put options on the S&P 500 is a very costly, if reliable, strategy to protect against market sell-offs. Holding ‘safe-haven’ US Treasury bonds, while providing a positive and predictable long-term yield, is generally an unreliable crisis-hedge strategy, since the post-2000 negative bond-equity correlation is a historical rarity. Long gold and long credit protection portfolios appear to sit between puts and bonds in terms of both cost and reliability. In contrast to these passive investments, we investigate two dynamic strategies that appear to have generated positive performance in both the long-run but also particularly during historical crises: futures time-series momentum and quality stock factors. Futures momentum has parallels with long option straddle strategies, allowing it to benefit during extended equity sell-offs. The quality stock strategy takes long positions in highest-quality and short positions in lowest-quality company stocks, benefitting from a ‘flight-to-quality’ effect during crises. These two dynamic strategies historically have uncorrelated return profiles, making them complementary crisis risk hedges. We examine both strategies and discuss how different variations may have performed in crises, as well as normal times, over the years 1985 to 2016.