Popularity
Page 15
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Table 7.4. Performance of Size Quartile Portfolio Returns, 1972–2016
Portfolio Sorting Metric
Statistic
Q4
(least popular, smallest)
Q3
Q2
Q1
(most popular, largest)
Market cap
Geometric mean return (%)
13.27
12.32
12.45
11.48
Arithmetic mean return (%)
16.43
14.74
14.45
13.01
Standard dev. (%)
26.50
23.15
20.89
17.83
Sharpe ratio
0.44
0.43
0.46
0.46
Total assets
Geometric mean return (%)
10.48
12.72
13.90
12.60
Arithmetic mean return (%)
13.84
14.98
15.69
14.30
Standard dev. (%)
27.27
22.14
19.84
18.92
Sharpe ratio
0.33
0.46
0.55
0.50
Revenue
Geometric mean return (%)
10.20
13.07
13.19
13.42
Arithmetic mean return (%)
13.25
15.36
15.23
15.11
Standard dev. (%)
26.07
22.37
21.07
19.06
Sharpe ratio
0.32
0.47
0.49
0.54
Net income
Geometric mean return (%)
9.92
13.64
13.28
12.57
Arithmetic mean return (%)
13.79
16.00
15.05
14.04
Standard dev. (%)
29.19
22.67
19.73
17.69
Sharpe ratio
0.31
0.49
0.52
0.52
Source: Ibbotson and Kim (2017) .
In addition to market cap, Table 7.4 presents statistics on quartile portfolios based on three alternative measures of company size: total assets, revenue, and net income. When these measures are used, the largest companies outperform the smallest. This result is consistent with previous empirical research, such as that of Berk (1997) , who explored the relationship between accounting-based measures of company size and returns, although Berk did not attribute the cause of this result to popularity as we do.
Regardless of the metric used, the standard deviation of returns is much higher for the small companies than for the large companies. As Table 7.4 shows, high volatility is associated with small size, however defined. For small-cap companies , high risk is associated with high returns, whereas for companies that were small based on other metrics , high risk is associated with low returns. Figure 7.2 shows this graphically.
Why do small-cap companies tend to outperform when larger companies measured by total assets, revenue, and total net income outperform? We believe small-cap companies are unpopular because they are riskier, less liquid, and so on, but we also believe that larger companies based on high assets, high revenue, or high net income can be relatively unpopular or overlooked on a relative basis.
Most importantly, we believe that the popularity effect is most pronounced when investors seem to nearly uniformly agree about whether an attribute or characteristic is desirable or undesirable. Market cap seems to be something that matters to most investors, to the degree that it can drive decision making; or to put it differently, perhaps it is a direct barometer of the aggregate decisions made by investors. In contrast, few investors make decisions exclusively on the basis of such metrics as assets, revenue, or total net income, and to interpret these factors as a reflection of investor preference would be challenging. When it comes to decision making, these metrics are almost always combined with some other data point(s) to arrive at a metric that influences decision making.
Value
The value effect is one of the best known violations of the CAPM, being first documented by Basu (1977) . (See also Stattman 1980 and Basu 1983 ). Value tends to outperform growth over long periods for various measures of value. The results in Table 7.5 confirm these results for our quartile portfolios formed by ranking stocks based on book/market (B/M) and earnings/price (E/P). 50 In each case, the Quartile 4 (least popular) portfolio outperformed the Quartile 1 (most popular) portfolio.
Figure 7.2. Risk and Return of Size Quartile Portfolios, 1972–2016
Note: Squares indicate Q4 (smallest), and diamonds indicate Q1 (largest). Small dots indicate Q2 and Q3.
Source: Ibbotson and Kim (2017) .
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Figure 7.3 plots the geometric mean return and standard deviation of return for the four value quartile portfolios for the two measures of value. Note that, although the Q4 value (least popular) portfolio clearly outperforms in each case, the Q1 growth (most popular) portfolio is usually the riskiest, even though it has the lowest returns. Thus, value premiums appear to be positive but are not necessarily risk premiums, as they are sometimes referred to (see Fama and French 1992 ).
Table 7.5. Value Quartile Portfolio Returns, 1972–2016
Portfolio Sorting Metric
Statistic
Q4
(least popular, high)
Q3
Q2
Q1
(most popular, low)
B/M
Geometric mean return (%)
15.77
13.91
11.48
8.23
Arithmetic mean return (%)
18.43
15.85
13.47
11.00
Standard dev. (%)
24.50
20.98
20.55
24.32
Sharpe ratio
0.55
0.52
0.42
0.25
E/P
Geometric mean return (%)
16.10
13.86
10.89
8.22
Arithmetic mean return (%)
18.42
15.55
12.85
11.83
Standard dev. (%)
22.77
19.52
20.33
27.93
Sharpe ratio
0.60
0.55
0.39
0.25
Source: Ibbotson and Kim (2017) .
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Figure 7.3. Risk and Return of Value Quartile Portfolios, 1972–2016
Notes: Squares indicate Q4 (least popular, high), and diamonds indicate Q1 (most popular, low). Small dots indicate Q2 and Q3.
Source: Ibbotson and Kim (2017) .
High-growth companies, whether characterized by low B/M or low E/P, tend to be the newsworthy, up-and-coming, “hot” companies. But again, the most popular stocks have the worst performance.
Liquidity
To measure liquidity, IK used the Amihud (2002) illiquidity metric, defined as the absolute value of the daily return divided by the daily dollar value of shares traded, averaged over the course of the selection year. IK ranked stocks during the selection year with this metric and placed them into the four quartile portfolios for each performance year.
Table 7.6 shows the returns for the illiquidity quartile portfolios. Q4 is the low-liquidity (highest illiquidity, least popular) portfolio and Q1 is the high-l
iquidity (lowest illiquidity, most popular) portfolio. Q4 outperformed Q1 by a wide margin. This result makes sense because liquidity is always desired by some segments of the market, and those investors are willing to pay for it.
Momentum
Table 7.7 presents results for the returns of quartile portfolios formed from ranking the returns on the last 12 months and on the last 11 months (2–12) as of calendar year-end. The 11-month measure is often used because the near-in month is usually considered a reversal month (Jegadeesh 1990 ). The results show that when either measure is used, a momentum effect occurred in the period.
Figure 7.4 shows the geometric mean return plotted against standard deviation of returns for the quartile portfolios formed on the two measures of momentum. As shown, the low-momentum portfolio (Q4) not only has the worst performance, but it also has the highest risk. As far as our analysis goes, momentum is a special case. Although it fits well with the popularity framework, it does not fit well with our cross-sectional testing method. We view high-momentum stocks as stocks that are increasing in popularity —that is, becoming higher priced. But why do high-momentum stocks in one year continue to outperform the next year? Empirically, the increase in popularity is sustained over a relatively long period of time, such as 6–18 months. Ultimately, we believe that these stocks become overly popular, resulting in a reversal, but the reversal is not immediate and appears to be a market inefficiency. Stocks do not appear to immediately react to new information; their price changes continue over long periods in the same general direction.
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Table 7.6. Illiquidity Quartile Portfolio Returns, 1972–2016
Portfolio Sorting Metric
Statistic
Q4
(least popular, least liquid)
Q3
Q2
Q1
(most popular, most liquid)
Amihud illiquidity
Geometric mean return (%)
14.48
11.95
11.97
11.22
Arithmetic mean return (%)
17.16
14.46
14.16
12.87
Standard dev. (%)
24.55
23.50
21.83
18.53
Sharpe ratio
0.50
0.41
0.43
0.43
Source: Ibbotson and Kim (2017) .
Table 7.7. Momentum Quartile Portfolio Returns, 1972–2016
Portfolio Sorting Metric
Statistic
Q4
(low)
Q3
Q2
Q1
(high)
12-month momentum
Geometric mean return (%)
8.20
13.40
14.38
13.07
Arithmetic mean return (%)
11.90
15.27
16.03
15.45
Standard dev. (%)
28.61
20.38
19.11
22.87
Sharpe ratio
0.25
0.51
0.58
0.46
2–12 month momentum
Geometric mean return (%)
8.39
13.05
14.46
13.25
Arithmetic mean return (%)
11.98
14.86
16.23
15.58
Standard dev. (%)
28.17
19.97
19.75
22.65
Sharpe ratio
0.25
0.50
0.58
0.47
Source: Ibbotson and Kim (2017) .
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Figure 7.4. Risk and Return of Momentum Quartile Portfolios, 1972–2016
Notes: Squares indicate Q4 (low), and diamonds indicate Q1 (high). Small dots indicate Q2 and Q3.
Source: Ibbotson and Kim (2017) .
Table 7.8 consolidates the previous analyses and presents our assessment of how the results are or are not consistent with the popularity framework and/or the more-risk/more-return paradigm. We found that 7 out of 10 of the analyses are consistent with the popularity framework, whereas only 2 out of 10 are consistent with the more-risk/more-return paradigm.
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Table 7.8. Summary Results for Factor Portfolios and Consistency with Asset Pricing Frameworks, 1972–2016
Notes: All quartile portfolios are equally weighted. “Geometric” and “Arithmetic” refer to mean returns and are given in percentages; “Std. dev.” is standard deviation and is given in percentage.
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We deem three measured characteristics to be inconsistent with the popularity framework—high total assets, high revenue, and high net income. Although these three are characteristics that investors should like (thus making them popular), we believe they are almost never viewed in isolation and they are almost never the sole reason that an investment decision is made. Therefore, we do not interpret these results as definitive evidence against the popularity framework.
Conclusion
In this chapter, we analyzed the applicable quartile portfolios from IK through the popularity lens. IK ranked stocks and formed quartile portfolios on the basis of beta, volatility, size of companies, value measures, liquidity, and momentum, which we interpreted to be indicators of or characteristics associated with popularity. Rankings were all done in a selection year, and the ranked quartile portfolio returns were measured in the following (out of sample) performance year for the period 1972–2016.
Classical finance tells us that, in general, greater reward comes with greater systematic risk. IK found, however, that low-beta and low-volatility portfolios outperform high-beta and high-volatility portfolios. Also, small-cap stocks outperform but not small companies; large companies, as measured by assets, revenue, and income, outperform. The less liquid stocks outperform with more risk. High-momentum portfolios outperform, as anticipated, but the low-momentum portfolios are riskier.
Overall, the characteristics of the best performing portfolios are high E/P and high B/M. On a risk-adjusted basis, the best performers are low-beta and low-volatility portfolios.
When considered individually, the results presented here mainly confirm previously reported results. By presenting these results together in a common framework, however, we have shown a clear negative relationship between risk and return in the US stock market during the period studied. A common theme has emerged. Although risk is often unpopular, it can be popular in certain circumstances; often, characteristics other than risk dominate returns in the stock market.
Of the 10 factors analyzed in this chapter, we found that 7 are consistent with the popularity framework but only 2 of 10 are consistent with the classical view that more risk means more return. For the three factors that were not consistent with the popularity framework—larger companies based on high assets, high revenue, or high net income—we believe these are attributes that are almost always coupled with market capitalization to form valuation ratios. As standalone measures, investors do not uniformly agree about whether these characteristics are desirable or undesirable.
8. Summary and Conclusions
To summarize the concepts and findings that we have presented in this book, we classify them into popularity as a concept, popularity as a bridge between theories, popularity as a theory, and empirical evidence for popularity.
Popularity as a Concept
Popularity is how much anything is liked, preferred, or desired. We applied the concept to assets and securities. In this way, we were able to give explanations for all the premiums in the markets, especially the stock market.
Most assets and securities are in relatively fixed supply over the short or intermediate t
erm. Popularity represents the demand or perhaps the excess demand for a security and is thus an important determinant of prices for a set of expected cash flows.
The “risk” premiums in the market are payoffs for the riskiness of securities. In classical finance, investors are risk averse and market frictions are usually assumed away. Risk is unpopular. The largest risk premium is the equity risk premium—that is, the extra expected return for investing in equities rather than bonds or risk-free assets. Other risk premiums include the interest rate risk and default risk in bond markets.
The market encompasses many premiums that may or may not be related to risk, but all are related to investing in something that is unpopular in some way. We analyzed premiums on security characteristics that are systematically unpopular, although they can change dynamically over time. Such premiums include the following:
the size premium,
the value premium,
the liquidity premium,
the premium for severe downside risk,
low-volatility and low-beta premiums,
premiums and discounts for environmental, social, and governance investing,
premiums for lack of competitive advantage, poor brand awareness, and poor reputation, and
the premium for any type of security that is being overlooked.
Assets and securities that are only temporarily popular or unpopular are considered to be mispriced. We did not focus on mispricing in this book.