基础率手册——毛利润率：整合过去以更好预见未来
全球金融策略部 www.credit-suisse.com
GLOBAL FINANCIAL STRATEGIES www.credit-suisse.com
基础率手册——毛利润率：整合过去，更好预判未来
The Base Rate Book – Gross Profitability Integrating the Past to Better Anticipate the Future
2016 年 4 月 25 日 25 作者 迈克尔·莫布森 20
April 25, 2016 25 Authors Michael J. Mauboussin 20
价值（基年 = 1 美元）
Value (Base Year = $1)
全球范围 最低 丹·卡拉汉，CFA 15 [email protected]
Universe Lowest Dan Callahan, CFA 15 [email protected]
Darius Majd 10 5 0 1990 1995 2000 2005 2010 2015
Darius Majd 10 5 0 1990 1995 2000 2005 2010 2015
资料来源：瑞信 HOLT®。
Source: Credit Suisse HOLT®.
“…掌握个别案例信息的人，很少觉得有必要去了解该案例所属类别的统计数据。”
“. . . people who have information about an individual case rarely feel the need to know the statistics of the class to which the case belongs.”
Daniel Kahneman1
Daniel Kahneman1
毛利润率（毛利润除以资产）可能是估值分析中一个有用的额外输入指标。
Gross profitability, gross profits divided by assets, may be a useful additional input into valuation analysis.
这一盈利能力指标所提供的估值信号，可能不同于市盈率倍数——后者是分析师最常用的股票估值指标。
This measure of profitability can provide a different valuation signal than the price-earnings multiple, which is the most common metric analysts use to value stocks.
毛利润率在长期内高度稳定，研究表明，高毛利润率公司带来的股东总回报高于低毛利润率公司。
Gross profitability is highly persistent over time, and research shows that firms with high profitability deliver higher total shareholder returns than those with low profitability.
本报告展示了 1950 年至 2014 年间全球近 1000 家公司的毛利润率基础率，并按行业进行了分析。
This report shows the base rate for gross profitability for nearly 1,000 global companies from 1950-2014 and includes analysis by sector.
我们提供了一种方法，将公司特定观点与基础率相结合，以提升预测的准确性。
We provide a method to integrate company-specific views with the base rate to sharpen the quality of forecasts.
Introduction
Introduction
证券分析之父本杰明·格雷厄姆在 20 世纪 70 年代曾与一位名叫詹姆斯·雷的航空工程师共度时光。他们共同开发了一个筛选系统，用来寻找具备十项标准的吸引力股票。
Benjamin Graham, the father of security analysis, spent some time with an aeronautical engineer named James Rea in the 1970s. Together, they developed a screen to find attractive stocks that had ten criteria.
由于这发生在格雷厄姆生命的末期，有人将此列表称为格雷厄姆的“最后遗愿”。其中大约一半的指标基于估值，这与格雷厄姆的价值导向一致。但另一半指标则关乎质量。因此，通过筛选的公司既要统计上便宜，又要高质量。
Because it was toward the end of Graham’s life, some refer to the list as Graham’s “last will.”2 About one-half of the measures were based on valuation, consistent with Graham’s value orientation. But the other half of the criteria addressed quality. So a company that passed the screen would be both statistically cheap and of high quality.
本基础率系列报告探讨的是毛利润率——一项衡量公司赚钱能力的指标，近年来引起了学术界和从业者的关注。之前的报告包括两份针对利润表的（销售增长和净利润增长），以及一份关于现金回报率（CFROI®）——一项衡量经济盈利能力的指标。
This installment in our base rate series examines gross profitability, a measure of a company’s ability to make money that has attracted the attention of academics and practitioners in recent years. Prior reports include two dedicated to the income statement, sales growth and net income growth, and one to Cash Flow Return on Investment (CFROI®), a measure of economic profitability.3
罗切斯特大学西蒙商学院的金融学教授罗伯特·诺维-马克思将毛利润率定义为：收入减去销售成本，除以总资产账面价值。换句话说，毛利润率等于毛利润除以资产。投资者可以将毛利润率作为质量的代理指标，它与经典的价值衡量指标并无正相关关系。
Robert Novy-Marx, a professor of finance at the Simon Business School at the University of Rochester, defines gross profitability as revenues minus cost of goods sold, scaled by the book value of total assets. In other words, gross profitability is gross profit divided by assets. Investors can use gross profitability as a proxy for quality and it is not positively correlated with classic measures of value.4
研究表明，毛利润率在短期和长期内都具有高度持续性。这意味着你可以根据过去对未来盈利能力做出合理估计。学术研究还表明，高毛利润率公司带来的股东总回报优于低毛利润率公司，尽管前者起步时的市净率更高。
Research shows that gross profitability is highly persistent in the short and long run. This means that you can make a reasonable estimate of future profitability based on the past. Academic research also shows that firms with high gross profitability deliver better total shareholder returns than those with low profitability. This is despite the fact that they start with loftier price-to-book ratios.5
如今，许多学者和从业者将毛利润率纳入其资产定价模型。例如，芝加哥大学教授、诺贝尔奖得主尤金·法玛与达特茅斯学院塔克商学院金融学教授肯尼思·弗伦奇，就将盈利能力列为有助于解释资产价格变化的因素之一。其他因素包括贝塔系数（衡量资产回报对市场回报的敏感度）、规模、估值和投资。法玛和弗伦克对盈利能力的定义与诺维-马克思略有不同，但抓住了相同的本质。
Many academics and practitioners now incorporate gross profitability into their asset pricing models. For instance, Eugene Fama, a professor at the University of Chicago and a winner of the Nobel Prize, and Kenneth French, a professor of finance at the Tuck School of Business, Dartmouth College, include profitability as one of the factors that helps explain changes in asset prices. The others include beta (a measure of the sensitivity of an asset’s returns to market returns), size, valuation, and investment.6 The definition of profitability that Fama and French use differs somewhat from that of Novy-Marx but captures the same essence.
盈利能力对股东总回报的解释力似乎是一种全球现象。利用 Compustat 数据（1963 年 7 月至 2010 年 12 月）和 Compustat Global 数据（1990 年 7 月至 2009 年 10 月），诺维-马克思发现，在美国以及美国以外的发达市场，盈利能力较强的公司股票的表现优于盈利能力较弱的公司股票。两个样本均排除了金融服务业公司的股票。这些结果与一项针对 1980 年至 2010 年 41 个国家中毛利润率对股东总回报影响的研究结果一致。
The power of profitability to explain total shareholder returns appears to be a global phenomenon.7 Using Compustat data (July 1963 to December 2010) and Compustat Global data (July 1990 to October 2009), Novy-Marx found that the stocks of more profitable firms outperformed the stocks of less profitable firms in the United States as well as in developed markets outside the U.S. Both samples exclude stocks of companies in the financial services sector. These results are consistent with a study that examined the effect of gross profitability on total shareholder returns in 41 countries from 1980 to 2010.8
毛利润率也可能是在寻找有吸引力股票时一个有用的筛选因子。盈利能力所提供的信号可能与市盈率倍数截然不同——市盈率倍数是分析师最常用的股票估值指标。一只用市盈率倍数看来缺乏吸引力的股票，用毛利润率来看可能很有吸引力；反之亦然。
Gross profitability may also be a useful factor to screen for in a search for attractive stocks. Profitability can provide a very different signal than a price-earnings (P/E) multiple, which is the most common metric analysts use to value stocks. A stock that appears unattractive using a P/E multiple may look attractive using gross profitability, and a stock that appears unattractive using gross profitability may look attractive using a P/E multiple.
以亚马逊为例。截至 2015 年底，基于 12 月 31 日的股价 676 美元和全年报告每股收益 1.25 美元，该股的往绩市盈率倍数约为 540 倍。作为对比，同期标普 500 指数的市盈率倍数为 20 倍。纯粹从市盈率倍数来看，亚马逊的估值似乎很高。
Take Amazon.com as a case. The stock had a trailing P/E multiple of roughly 540 at year-end 2015 based on a price of $676 on December 31 and full-year reported earnings per share of $1.25. For context, the P/E multiple was 20 for the S&P 500 at the same time. Based purely on its P/E multiple, the valuation of Amazon.com appeared high.
但该公司的毛利润率则讲述了另一个故事。2015 年，亚马逊的毛利润率为 0.54（毛利润 350 亿美元，总资产 650 亿美元）。根据诺维-马克思的说法，毛利润率达到 0.33 或更高通常具有吸引力。亚马逊近期的毛利润率不仅轻松超过了这个水平，而且在公司历史的大部分时间里，它都远高于这一阈值（见图表 1）。
The company’s gross profitability told a different story. For 2015, Amazon.com’s gross profitability was 0.54 (gross profit of $35 billion and total assets of $65 billion). According to Novy-Marx, gross profitability of 0.33 or higher is generally attractive.9 Not only did Amazon.com’s recent gross profitability surpass that level easily, it has been well above that threshold for most of the company’s history (see Exhibit 1).
图表 1：亚马逊毛利润率，1997-2015 年 0.60
Exhibit 1: Amazon.com’s Gross Profitability, 1997-2015 0.60
0.50
0.50
原件此处是表格，PDF 抽取时列结构已丢失，下面只剩按列读出的数字，行列对应关系无法还原。核对数据请打开来源正文。
Gross Profitability 0.40 0.30 0.20 0.10 0.00 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Gross Profitability 0.40 0.30 0.20 0.10 0.00 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Source: FactSet.
Source: FactSet.
毛利润率的基础率
Base Rate of Gross Profitability
进行预测的两种常见方式包括自下而上的研究（称为“内部视角”），这是最自然的方法；以及应用基础率（“外部视角”），看看在适当的参考类别中已有怎样的结果。决策研究表明，自下而上的方法容易受到偏见的影响，而结合基础率通常能提高预测的准确性。你需要明智地将两种方法结合起来。
Two common ways of making a forecast include bottom-up research (known as the “inside view”), which is the most natural approach, and application of a base rate (“outside view”) to see what the results have been for an appropriate reference class. The research in decision making shows that the bottom-up approach is subject to biases and that incorporating the base rate generally improves the accuracy of the forecast. 10 You want to combine the two approaches intelligently.
有一种技术可以将这两种方法整合起来，我们将其应用于毛利润率。基本思路是：如果当前的结果与过去一致，你可以对内部视角赋予更多权重。另一方面，如果当前结果与过去差别很大，你就应该更强调外部视角。
There is a technique to blend the approaches that we apply to gross profitability.11 The basic idea is if an outcome in the present is consistent with the past, you can place more weight on the inside view. On the other hand, if the present outcome is very different than the past, you should place greater emphasis on the outside view.
该方法的关键是相关性，它衡量两个分布中变量之间线性关系的程度。相关系数的值可以在 -1.0（一个变量上升与另一个变量下降完全相关）到 1.0（两个变量同步变动）之间。零相关表示随机性。毛利润率的相关性高且为正。
The key to the method is correlation, which measures the degree of the linear relationship between variables in a pair of distributions. The value of a correlation coefficient can fall between -1.0 (the rise in one variable perfectly correlates with the fall of the other) to 1.0 (both variables move in tandem). A zero correlation indicates randomness. Correlations for gross profitability are high and positive.
如果两个分布之间的相关性很高，那么之前发生的事情就能很好地预示未来。例如，美国股票共同基金当前的费用率与三年前的费用率之间的相关系数约为 0.98。只要这种相关性持续存在，如果你知道一家共同基金当前的费用率，你就可以非常准确地预测其未来的费用率。自下而上的工作高度相关。
If the correlation between two distributions is high, then what happened before gives you a really good sense of what will follow. For example, the correlation between the expense ratio for U.S. equity mutual funds today and three years ago is about 0.98. Provided that correlation persists, you can forecast a mutual fund’s future expense ratio with a great deal of accuracy if you know the ratio today.12 The bottom-up work is highly relevant.
如果相关性低，之前发生的事情对接下来会发生什么几乎没有什么提示。阿尔法（衡量基金风险调整后超额回报的指标）就是一个很好的例子。对于投资于大市值公司股票的美国共同基金而言，三年期阿尔法之间的相关系数为 0.05。这意味着知道一只共同基金 2010-2012 年的阿尔法，对于其 2013-2015 年的阿尔法几乎没有什么提示作用。因此，对未来阿尔法的最佳预测接近参考类别的平均值。
If the correlation is low, what happened before provides little sense of what will happen next. Alpha, a measure of a fund’s risk-adjusted excess return, is a good example. For U.S. mutual funds that invest in stocks of large capitalization companies, the correlation between the three-year periods of alpha is 0.05. That means knowing a mutual find’s alpha from 2010-2012 would tell you little about the fund’s alpha from 2013-2015. As a consequence, your best forecast for future alpha is close to the average of the reference class.
图表 2 显示，诺维-马克思定义的毛利润率在一年、三年和五年期都非常稳定。例如，当前年份的毛利润率与三年后毛利润率之间的相关系数 r 为 0.89（图表 2 中间面板）。即使是五年期的相关性也很高，为 0.82（右侧面板）。
Exhibit 2 shows that the Novy-Marx definition of gross profitability is very persistent over one-, three-, and five-year periods. For example, the correlation between profitability in the current year and three years in the future has a coefficient, r, of 0.89 (middle panel of Exhibit 2). But even the five-year correlation is high at 0.82 (right panel).
这个范围包括 1950 年至 2014 年间全球市值排名前 1000 的公司。样本包括已倒闭公司，但排除了金融服务业和公用事业部门的公司。数据涵盖了近 40000 个公司年，且由于盈利能力以比率形式表示，无需考虑通货膨胀因素。
This universe includes the top 1,000 firms in the world from 1950 to 2014 as measured by market capitalization. The sample includes dead companies but excludes firms in the financial services and utilities sectors. The data include nearly 40,000 company years, and there is no need to take into account inflation because profitability is expressed as a ratio.
图表 2：毛利润率的持续性
Exhibit 2: Persistence of Gross Profitability
3.5 r = 0.95 3.5 r = 0.89 3.5 r = 0.82 3.0 3.0 3.0
3.5 r = 0.95 3.5 r = 0.89 3.5 r = 0.82 3.0 3.0 3.0
原件此处是表格，PDF 抽取时列结构已丢失，下面只剩按列读出的数字，行列对应关系无法还原。核对数据请打开来源正文。
下一年毛利润率 三年后毛利润率 五年后毛利润率 2.5 2.5 2.5 2.0 2.0 2.0 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -0.5 -0.5 -0.5 -1.0 -1.0 -1.0 毛利润率 毛利润率 毛利润率
Gross Profitability Next Year Gross Profitability in 3 Years Gross Profitability in 5 Years 2.5 2.5 2.5 2.0 2.0 2.0 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -0.5 -0.5 -0.5 -1.0 -1.0 -1.0 Gross Profitability Gross Profitability Gross Profitability
资料来源：瑞信 HOLT®。
Source: Credit Suisse HOLT®.
图表 3 显示了毛利润率的稳定性。我们首先根据年初的毛利润率将公司分为五组。然后追踪这五个组别各自的毛利润率。均值回归现象非常微弱。从最高组到最低组的差距仅从 0.55 略微缩小至 0.50。考虑到这种稳定性，一个合理的预测是从去年的毛利润率出发，并寻找偏离它的理由。
Exhibit 3 shows the stability of gross profitability. We start by sorting companies into quintiles based on gross profitability at the beginning of a year. We then follow the gross profitability for each of the five cohorts. There is very little regression toward the mean. The spread from the highest to the lowest quintile shrinks only slightly, from 0.55 to 0.50. Given this stability, a sensible forecast is to start with last year’s profitability and seek reasons to move away from it.
图表 3：毛利润率的均值回归 0.4
Exhibit 3: Regression Toward the Mean for Gross Profitability 0.4
相对毛利润率（中位数）
Relative Gross Profitability (Medians)
原件此处是表格，PDF 抽取时列结构已丢失，下面只剩按列读出的数字，行列对应关系无法还原。核对数据请打开来源正文。
0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 1 2 3 4 5 Year
0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 1 2 3 4 5 Year
资料来源：瑞信 HOLT®。
Source: Credit Suisse HOLT®.
毛利润率与股东总回报
Gross Profitability and Total Shareholder Returns
图表 4 显示，毛利润率与股东总回报之间的相关系数，一年期为 0.01，三年期为 0.13，五年期为 0.17。然而，无论是诺维-马克思还是法玛和弗伦奇，都不建议简单地将毛利润率与股东总回报进行直接相关分析。
Exhibit 4 shows that the correlation between gross profitability and total shareholder return (TSR) is 0.01 for one year, 0.13 for three years, and 0.17 for five years. However, neither Novy-Marx nor Fama and French recommend a simple correlation between gross profitability and TSR.
图表 4：毛利润率的预测价值 r = 0.01 r = 0.13 r = 0.17 200 200 200
Exhibit 4: Predictive Value of Gross Profitability r = 0.01 r = 0.13 r = 0.17 200 200 200
原件此处是表格，PDF 抽取时列结构已丢失，下面只剩按列读出的数字，行列对应关系无法还原。核对数据请打开来源正文。
1 年股东总回报（%） 3 年股东总回报（%） 5 年股东总回报（%） 150 150 150 100 100 100 50 50 50 0 0 -0.5 0.0 0.5 1.0 1.5 2.0 -0.5 0.0 0.5 1.0 1.5 2.0 0 -50 -50 -0.5 0.0 0.5 1.0 1.5 2.0 -100 -100 -50 -150 -150 -100 1 年毛利润率 3 年平均毛利润率 5 年平均毛利润率
Total Shareholder Return 1 Year (Percent) Total Shareholder Return 3 Years (Percent) Total Shareholder Return 5 Years (Percent) 150 150 150 100 100 100 50 50 50 0 0 -0.5 0.0 0.5 1.0 1.5 2.0 -0.5 0.0 0.5 1.0 1.5 2.0 0 -50 -50 -0.5 0.0 0.5 1.0 1.5 2.0 -100 -100 -50 -150 -150 -100 Gross Profitability 1 Year Gross Profitability 3-Year Average Gross Profitability 5-Year Average
资料来源：瑞信 HOLT®。
Source: Credit Suisse HOLT®.
使用毛利润率更有效的方法是，按毛利润率将股票分成五组，并为每组构建投资组合。图表 5 展示了毛利润率最高和最低组别的 1 美元累计增长价值，以及整个全球范围的累计增长价值。样本包括 1990 年至 2016 年 1 月间美国最大的 1000 家工业和服务业公司。投资组合每月进行再平衡。
A more effective way to use gross profitability is to rank stocks in quintiles by gross profitability and to build portfolios for each. Exhibit 5 shows the cumulative growth in value of $1 for the quintiles with the highest and lowest ratios of gross profitability, as well as that for the whole universe. The sample includes the largest 1,000 U.S. industrial and service companies from 1990 through January 2016. The portfolios are rebalanced monthly.
图表 5：最高和最低盈利能力组别的总回报（1990 年至 2016 年 1 月）
Exhibit 5: Total Return for the Highest and Lowest Quintiles of Profitability (1990-January 2016)
25
25
20 Highest Universe
20 Highest Universe
价值（基年 = 1 美元）
Value (Base Year = $1)
原件此处是表格，PDF 抽取时列结构已丢失，下面只剩按列读出的数字，行列对应关系无法还原。核对数据请打开来源正文。
Lowest 15 10 5 0 1990 1995 2000 2005 2010 2015
Lowest 15 10 5 0 1990 1995 2000 2005 2010 2015
资料来源：瑞信 HOLT®。
Source: Credit Suisse HOLT®.
注：毛利润率使用财政年度期初和期末资产的平均值计算。
Note: Gross profitability is calculated using the average of the assets at the beginning and the end of the fiscal year.
行业毛利润率基础率
Base Rate of Gross Profitability for Sectors
我们可以通过考察行业层面的毛利润率来细化这一分析。这会减少样本量，但提高了相关性。我们为八个行业提供了计算均值回归速率以及应使用的适当均值的指南。我们排除了金融服务业和公用事业行业。
We can refine this analysis by examining gross profitability at the sector level. This reduces the sample size but improves its relevance. We present a guide for calculating the rate of regression toward the mean, as well as the proper mean to use, for eight sectors. We exclude the financial services and utilities sectors.
图表 6 考察了两个行业的毛利润率：非必需消费品和能源。顶部的面板显示了非必需消费品行业毛利润率的持续性。在右侧，我们看到基年毛利润率与五年后毛利润率之间的相关系数为 0.77。
Exhibit 6 examines gross profitability for two sectors, consumer discretionary and energy. The panels at the top show the persistence of gross profitability for the consumer discretionary sector. On the right, we see that the correlation between gross profitability in the base year and five years in the future is 0.77.
附录 6 底部表格展示的是能源行业的相同关系。右侧我们看到，基年总利润率与未来五年总利润率之间的相关系数为 0.62。这意味着，你应当预期非必需消费品行业向均值回归的速度慢于能源行业。
The panels at the bottom of exhibit 6 show the same relationships for the energy sector. On the right, we see that the correlation between gross profitability in the base year and five years in the future is 0.62. This suggests you should expect a slower rate of regression toward the mean in the consumer discretionary sector than in the energy sector.
表 6：非必需消费品与能源行业毛利润率的相关系数
Exhibit 6: Correlation Coefficients for Gross Profitability in Consumer Discretionary and Energy
Consumer Discretionary 2.5 r = 0.95 2.5 r = 0.88 2.5 r = 0.77 2.0 2.0 2.0
Consumer Discretionary 2.5 r = 0.95 2.5 r = 0.88 2.5 r = 0.77 2.0 2.0 2.0
| 下一年毛利率 | 三年后毛利率 | 五年后毛利率 | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.5 | 1.5 | 1.5 | ||||||||||||||||||
| 1.0 | 1.0 | 1.0 | ||||||||||||||||||
| 0.5 | 0.5 | 0.5 | ||||||||||||||||||
| 0.0 | 0.0 | 0.0 | ||||||||||||||||||
| -0.5 | 0.0 | 0.5 | 1.0 | 1.5 | 2.0 | 2.5 | -0.5 | 0.0 | 0.5 | 1.0 | 1.5 | 2.0 | 2.5 | -0.5 | 0.0 | 0.5 | 1.0 | 1.5 | 2.0 | 2.5 |
| -0.5 | -0.5 | -0.5 | ||||||||||||||||||
| 毛利率 | 毛利率 | 毛利率 |
Gross Profitability Next Year Gross Profitability in 3 Years Gross Profitability in 5 Years 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -0.5 -0.5 -0.5 Gross Profitability Gross Profitability Gross Profitability
Energy 2.5 r = 0.89 2.5 r = 0.75 2.5 r = 0.62 2.0 2.0 2.0
Energy 2.5 r = 0.89 2.5 r = 0.75 2.5 r = 0.62 2.0 2.0 2.0
| 明年毛利率 | 三年后毛利率 | 五年后毛利率 | |
|---|---|---|---|
| 1.5 | 1.5 | 1.5 | |
| 1.0 | 1.0 | 1.0 | |
| 0.5 | 0.5 | 0.5 | |
| 0.0 | 0.0 | 0.0 | |
| -0.5 | 0.0 0.5 1.0 1.5 2.0 | -0.5 0.0 0.5 1.0 1.5 2.0 | -0.5 0.0 0.5 1.0 1.5 2.0 |
| -0.5 | -0.5 | -0.5 | |
| 毛利率 | 毛利率 | 毛利率 |
Gross Profitability Next Year Gross Profitability in 3 Years Gross Profitability in 5 Years 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 -0.5 0.0 0.5 1.0 1.5 2.0 -0.5 0.0 0.5 1.0 1.5 2.0 -0.5 0.0 0.5 1.0 1.5 2.0 -0.5 -0.5 -0.5 Gross Profitability Gross Profitability Gross Profitability
来源：瑞信 HOLT® 研究。
Source: Credit Suisse HOLT®.
表格 7 展示了 1950 年至 2014 年间八个行业毛利润率的五年变动的相关系数，以及记录到的相关系数范围的标准差。该表格有两个方面值得强调。第一是 r 值从高到低的排序。这能让你了解各行业均值回归的速度。r 值高意味着回归缓慢，而 r 值低则代表回归更快。消费类行业通常拥有更高的 r 值，而技术或大宗商品敞口更大的行业 r 值则较低。
Exhibit 7 shows the correlation coefficient for five-year changes in gross profitability for eight sectors from 1950 to 2014, as well as the standard deviation for the ranges of recorded correlations. Two aspects of the exhibit are worth highlighting. The first is the ordering of r from high to low. This gives you a sense of the rate of regression toward the mean by sector. A high r suggests slow regression, and a low r means more rapid regression. Consumer-oriented sectors generally have higher r’s, and sectors with more exposure to technology or commodities have lower r’s.
第二个方面是这些相关性在年度之间的变化。非必需消费品板块的标准差为 0.10，相关系数为 0.77，这意味着 68% 的观测值落在 0.67 到 0.87 的区间内。能源板块的标准差则为 0.18。
The second aspect is how the correlations change from year to year. The standard deviation for the consumer discretionary sector was 0.10. With a correlation coefficient of 0.77, that means 68 percent of the observations fell within a range of 0.67 and 0.87. The standard deviation for the energy sector was 0.18.
相关系数为 0.62，意味着 68% 的观察值落在 0.44 至 0.80 的范围内。
With a correlation coefficient of 0.62, that means 68 percent of the observations fell within a range of 0.44 and 0.80.
附录 7：非必需消费与能源行业毛利润率的相关系数
Exhibit 7: Correlation Coefficients for Gross Profitability in Consumer Discretionary and Energy
| 行业 | 5 年期相关系数 | 标准差 |
|---|---|---|
| 必需消费品 | 0.86 | 0.07 |
| 工业 | 0.78 | 0.12 |
| 医疗保健 | 0.77 | 0.12 |
| 可选消费品 | 0.77 | 0.10 |
| 原材料 | 0.76 | 0.14 |
| 信息技术 | 0.64 | 0.13 |
| 能源 | 0.62 | 0.18 |
| 电信服务 | 0.61 | 0.44 |
5-Year Correlation Standard Sector Coefficient Deviation Consumer Staples 0.86 0.07 Industrials 0.78 0.12 Health Care 0.77 0.12 Consumer Discretionary 0.77 0.10 Materials 0.76 0.14 Information Technology 0.64 0.13 Energy 0.62 0.18 Telecommunication Services 0.61 0.44
来源：瑞信 HOLT®。
Source: Credit Suisse HOLT®.
表 8 基于六十多年的数据，为八个行业提供了向均值回归的速率指南，以及应使用的正确均值。请记住，向均值回归适用于整体群体，但未必适用于每一家具体公司。
Exhibit 8 presents guidelines on the rate of regression toward the mean, as well as the proper mean to use, for eight sectors based on more than sixty years of data. Keep in mind that regression toward the mean works on a population but not necessarily on every individual company.
第三列和第四列展示了每个板块的毛利润率中位数和平均数。之所以纳入中位数，是因为许多板块的毛利润率并不服从正态分布。（当平均数高于中位数时，分布呈右偏态。）尽管如此，平均数仅比中位数高出 5% 到 10%。
The third and fourth columns show the median and mean, or average, gross profitability for each sector. We include medians because the gross profitability in many sectors does not follow a normal distribution. (When the average is higher than the median, the distribution is skewed to the right.) Still, the means are only 5-10 percent higher than the medians.
右栏的两列展示了波动性指标。变异系数是一种标准化度量，用于捕捉离散程度。变异系数等于总利润率的标准差除以平均总利润率。消费可选板块的总利润率高于能源板块且波动性更小，这并不令人意外。
The two columns at the right show measures of variability. The coefficient of variation, a normalized measure, captures dispersion. The coefficient of variation equals the standard deviation of gross profitability divided by average gross profitability. It is not surprising that gross profitability is higher and less volatile in consumer discretionary than it is in energy.
附录 8：八个行业的毛利率回归速度与回归水平 回归幅度有多大？ 回归到什么水平？
Exhibit 8: Rate of Reversion and to What Mean Gross Profitability Reverts for Eight Sectors How Much Reversion? To What Level?
5 年相关系数 标准差 相关系数
5-Year Correlation Standard Coefficient of
| 行业 | 系数 | 中位数 | 均值 | 平均偏差 | 变异 |
|---|---|---|---|---|---|
| 必需消费品 | 0.86 | 0.50 | 0.54 | 0.08 | 0.14 |
| 工业 | 0.78 | 0.28 | 0.30 | 0.07 | 0.23 |
| 医疗保健 | 0.77 | 0.48 | 0.50 | 0.09 | 0.17 |
| 非必需消费品 | 0.77 | 0.36 | 0.40 | 0.05 | 0.12 |
| 材料 | 0.76 | 0.26 | 0.28 | 0.04 | 0.14 |
| 信息技术 | 0.64 | 0.40 | 0.42 | 0.06 | 0.13 |
| 能源 | 0.62 | 0.22 | 0.24 | 0.04 | 0.17 |
| 电信服务 | 0.61 | 0.24 | 0.27 | 0.07 | 0.26 |
Sector Coefficient Median Average Deviation Variation Consumer Staples 0.86 0.50 0.54 0.08 0.14 Industrials 0.78 0.28 0.30 0.07 0.23 Health Care 0.77 0.48 0.50 0.09 0.17 Consumer Discretionary 0.77 0.36 0.40 0.05 0.12 Materials 0.76 0.26 0.28 0.04 0.14 Information Technology 0.64 0.40 0.42 0.06 0.13 Energy 0.62 0.22 0.24 0.04 0.17 Telecommunication Services 0.61 0.24 0.27 0.07 0.26
资料来源：瑞士信贷 HOLT®。
Source: Credit Suisse HOLT®.
Summary
Summary
本杰明·格雷厄姆多年前就明白，以有吸引力的价格买入一家好企业，很可能会带来令人满意的结果。本报告考察总盈利能力（gross profitability）这一企业业绩衡量指标，该指标已受到学术界和实务界的关注。例如，盈利能力如今已成为尤金·法玛和肯尼思·弗伦奇五因子资产定价模型中的因子之一。
Benjamin Graham understood years ago that buying a good business at an attractive price would likely yield satisfactory results. This report examines gross profitability, a measure of corporate performance that has received attention from both academics and practitioners. For example, profitability is now one of the factors in Eugene Fama and Kenneth French’s five-factor asset pricing model.
此外，总盈利能力传递的估值信号可能与市盈率等传统估值方法不同。我们证明总盈利能力具有持续性，并确认了总盈利能力与股东总回报之间的正相关关系。
Further, gross profitability can send a different valuation signal than does a more traditional valuation approach such as the price-earnings multiple. We show that gross profitability is persistent and confirm the positive relationship between gross profitability and total shareholder returns.
决策研究表明，使用基础概率可以提高预测的质量。本报告提供了 1950-2014 年间近 1000 家全球公司的毛利率基础概率，并按行业展示了毛利率。投资者可能会发现，毛利率是一个有用的筛选指标，也是估值分析中的一个额外输入项。
Decision-making research shows that using base rates can improve the quality of forecasts. This report provides the base rate for gross profitability for nearly 1,000 global companies from 1950-2014, and it shows gross profitability by sector. Investors may find gross profitability to be a useful metric to screen for and an additional input into valuation analysis.
注释
1 丹尼尔·卡尼曼，《思考，快与慢》（纽约：法尔・斯特劳斯・吉鲁出版社，2011 年），第 249 页。
Endnotes 1 Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux, 2011), 249.
2 詹姆斯·B·雷亚，“追忆本杰明·格雷厄姆——老师与朋友”，《投资组合管理期刊》1977 年夏季刊第 3 卷第 4 期，第 66-72 页。另见 P·布卢斯坦，“本·格雷厄姆的最后遗嘱”，《福布斯》1977 年 8 月 1 日刊，第 43-45 页。另见查尔斯·M·C·李与埃里克·C·苏，“Alpha 经济学：市场效率的信息基础”，《会计学基础与趋势》2014 年 12 月刊第 9 卷第 2-3 期，第 59-258 页。
2 James B. Rea, “Remembering Benjamin Graham – Teacher and Friend,” Journal of Portfolio Management, Vol. 3, No. 4, Summer 1977, 66-72. Also, see P. Blustein, “Ben Graham’s Last Will and Testament,” Forbes, August 1, 1977, 43-45. Also, Charles M. C. Lee and Eric C. So, “Alphanomics: The Informational Underpinnings of Market Efficiency,” Foundations and Trends in Accounting, Vol. 9, Nos. 2-3, December 2014, 59-258.
3 Michael J. Mauboussin、Dan Callahan 和 Darius Majd 合著的《基准率手册——销售增长：融合历史以更好地预见未来》，瑞信全球金融战略，2016 年 2 月 23 日；Michael J. Mauboussin、Dan Callahan 和 Darius Majd 合著的《基准率手册——盈利增长：融合历史以更好地预见未来》，瑞信全球金融战略，2015 年 12 月 16 日；Michael J. Mauboussin、Dan Callahan、Bryant Matthews 和 David A. Holland 合著的《如何对均值回归建模：确定结果回归的速度及目标均值》，瑞信全球金融战略，2013 年 9 月 17 日。
3 Michael J. Mauboussin, Dan Callahan, and Darius Majd, “The Base Rate Book – Sales Growth: Integrating the Past to Better Anticipate the Future,” Credit Suisse Global Financial Strategies, February 23, 2016; Michael J. Mauboussin, Dan Callahan, and Darius Majd, “The Base Rate Book – Earnings Growth: Integrating the Past to Better Anticipate the Future,” Credit Suisse Global Financial Strategies, December 16, 2015; Michael J. Mauboussin, Dan Callahan, Bryant Matthews, and David A. Holland, “How to Model Reversion to the Mean: Determining How Fast, and to What Mean, Results Revert,” Credit Suisse Global Financial Strategies, September 17, 2013.
4 罗伯特·诺维-马克思，《价值的另一面：总利润率溢价》，《金融经济学杂志》，第 108 卷，第 1 期，2013 年 4 月，第 1-28 页。瑞信 HOLT 团队也分析了这一主题。参见布莱恩特·马修斯、戴维·A·霍兰德和理查德·库里，《质量的衡量标准》，瑞信 HOLT 财富创造原则，2016 年 2 月。
4 Robert Novy-Marx, “The Other Side of Value: The Gross Profitability Premium,” Journal of Financial Economics, Vol. 108, No. 1, April 2013, 1-28. Credit Suisse’s HOLT team also analyzed this topic. See Bryant Matthews, David A. Holland, and Richard Curry, “The Measure of Quality,” Credit Suisse HOLT Wealth Creation Principles, February 2016.
5 一些研究者对“毛利润”（毛利润率中的分子）是比净利润或营业利润等其他常用指标更好的盈利衡量指标这一说法持批评态度。他们认为，当毛利润率和净利润以相同方式折算时，两者具有相近的预测能力。参见 Ray Ball、Joseph Gerakos、Juhani T. Linnainmaa 和 Valeri V. Nikolaev 合著的《盈利指标折算》（*Journal of Financial Economics*，第 117 卷第 2 期，2015 年 8 月，第 225-248 页）。另一项研究则指出，经营杠杆可以解释毛利润率带来的超额收益。参见 Michael Kisser 的《什么解释了毛利润率溢价？》（工作论文，2014 年 11 月）。
5 Some researchers are critical of the claim that gross profit, the numerator of gross profitability, is a better measure of earnings than other popular measures such as net income or operating income. They argue that gross profitability and net income have similar predictive power when they are deflated the same way. See Ray Ball, Joseph Gerakos, Juhani T. Linnainmaa, and Valeri V. Nikolaev, “Deflating Profitability,” Journal of Financial Economics, Vol. 117, No. 2, August 2015, 225-248. Another study suggests operating leverage explains the excess returns to gross profitability. See Michael Kisser, “What Explains the Gross Profitability Premium?” Working Paper, November 2014.
6 Eugene F. Fama 和 Kenneth R. French，《五因子资产定价模型》，《金融经济学杂志》第 116 卷第 1 期，2015 年 4 月，第 1-22 页。
6 Eugene F. Fama and Kenneth R. French, “A Five-Factor Asset Pricing Model,” Journal of Financial Economics, Vol. 116, No. 1, April 2015, 1-22.
菲尔·德穆斯，《神秘因素“P”：查理·芒格、罗伯特·诺维-马克思与盈利因子》，
7 Phil DeMuth, “The Mysterious Factor ‘P’: Charlie Munger, Robert Novy-Marx And The Profitability Factor,”
Forbes, June 27, 2013.
Forbes, June 27, 2013.
孙磊、魏国强和谢飞雪，《关于毛利润率效应的解释：来自国际股票市场的洞见》，2014 年亚洲金融协会会议论文，2014 年 12 月 23 日。
8 Lei Sun, Kuo-Chiang (John) Wei, and Feixue Xie, “On the Explanations for the Gross Profitability Effect: Insights from International Equity Markets,” Asian Finance Association 2014 Conference Paper, December 23, 2014.
9 杰森·茨威格，“投资者终于破解了选股密码吗？”《华尔街日报》，2013 年 3 月 1 日。 10 丹·洛瓦洛与丹尼尔·卡尼曼，“成功的错觉：乐观如何削弱高管的决策”
9 Jason Zweig, “Have Investors Finally Cracked the Stock-Picking Code?” Wall Street Journal, March 1, 2013. 10 Dan Lovallo and Daniel Kahneman, “Delusions of Success: How Optimism Undermines Executives’
决策，《哈佛商业评论》，2003 年 7 月，第 56-63 页。
Decisions,” Harvard Business Review, July 2003, 56-63.
William M. K. 特罗基姆与詹姆斯·P. 唐纳利合著《研究方法知识库》第三版（俄亥俄州梅森：Atomic Dog 出版社，2008 年），第 166 页。参见 http://www.socialresearchmethods.net/kb/regrmean.php。迈克尔·J. 莫布森、丹·卡拉汉与达里乌斯·马杰德合著《何为有用统计：并非所有数字都生而平等》，瑞士信贷全球金融策略报告，2016 年 4 月 5 日。
11 William M. K. Trochim and James P. Donnelly, The Research Methods Knowledge Base, Third Edition (Mason, OH: Atomic Dog, 2008), 166. See http://www.socialresearchmethods.net/kb/regrmean.php. 12 Michael J. Mauboussin, Dan Callahan, and Darius Majd, “What Makes for a Useful Statistic: Not All Numbers Are Created Equal,” Credit Suisse Global Financial Strategies, April 5, 2016.