Stocks can be classified into four factors: value, size, investment, and quality. It is well-known that value stocks perform well, and small caps tend to outperform large caps. However, what about the quality factor trading strategy?
The quality factor can be described as investing in stocks of safe, profitable, growing, and well-managed companies. This sounds pretty intuitive, but how has it performed?
This article will examine the quality factor, backtest its performance, and analyze the results to determine its profitability.
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What is the quality factor?
The “quality factor” in stocks refers to a specific investment strategy or approach that focuses on selecting stocks of companies that exhibit strong fundamental characteristics, financial stability, and reliability. It is one of the common factors used in quantitative investing and stock market analysis. Key characteristics and metrics associated with the quality factor in stocks include:
- Profitability: High-quality companies tend to be consistently profitable. Metrics such as return on equity (ROE), return on assets (ROA), and profit margins are often used to assess profitability.
- Stability of Earnings: Quality companies typically have a history of stable and reliable earnings growth.
- Financial Strength: Quality stocks are often associated with strong balance sheets and low debt levels. When assessing financial strength, metrics like debt-to-equity ratios and interest coverage ratios are considered.
- Dividend History: Many quality stocks have a track record of paying dividends and even increasing them over time. Dividend yield and dividend growth are factors of interest. For example, Dividend Kings and Dividend Aristocrats.
Every firm builds their quality factor differently, but ultimately, the goal is to invest in companies that are more likely to weather economic downturns, generate sustainable profits, and deliver long-term value to shareholders.
However, it’s important to note that, like any investment strategy, the quality factor is not foolproof, and there are no guarantees of positive returns. However, as we will show you, it has done pretty well over the years.
The Quality Factor – backtest
For the backtest, we are going to use the quality factor calculated by French and Fama. The portfolio we used was formed by the 30% highest stocks ranked by profitability (Operating Profits – OP) at the end of each June using NYSE breakpoints.
Trading rules (investing rules)
OP for June of year t is annual revenues minus cost of goods sold, interest expense, and selling, general, and administrative expenses divided by book equity for the last fiscal year end in t-1.
The data is not adjusted for dividends. Here is the equity curve:
As you can see, the quality stocks performed much better than S&P 500! And because of the snowball effect, the difference grows bigger as time goes by.
Here are some performance metrics and statistics about the factor (reinvested dividends not considered):
- CAGR is 9.08% (S&P 500 buy and hold 7.24% (dividends not included)
- The standard deviation is 15.33 (14.95)
- Maximum drawdown is -50.41% (-52.56%)
- $100 dollars become $18.414,91 ($6628,42)
The quality factor performs exceptionally well. The difference might not sound much, but over time the difference snowballs.
It outperformed the S&P 500 while maintaining the same level of volatility and drawdown.
Quality factor ETFs
How can you copy the quality factor in real-life investing and trading?
Although replicating this strategy may be challenging for investors, many ETFs track it and perform exceptionally. Here are the largest ones and their respective CAGR since inception (not adjusted for dividends):
- QUAL: iShares MSCI USA Quality Factor ETF (CAGR 10.81%)
- IQLT: iShares MSCI Intl Quality Factor ETF (3.87%)
- SPHQ: Invesco S&P 500 Quality ETF (7.24%)
- JQUA: JP Morgan US Quality Factor ETF (11.23%)
Quality factor – conclusion
In summary, today, you learned the quality factor and how it performs. Over the long run, it has outperformed the S&P 500 while exhibiting the same level of volatility and drawdown. The factor could be further improved by adding additional value and size filters.