What Drives Stock Returns of Apple, Google,
Microsoft, and Oracle? Industry Structure and
Stock Returns in the US Computer Industry,
1965-2012.
Eiichiro Kazumori
2014:11:08
ABSTRACT
We report the results of the …rst study of the structure of stock returns in the com-puter industry characterized by platform competition based on network e¤ects using the US market data from 1965 to 2012. The computer industry provides the highest returns among all industries from the industry market share momentum strategy that buys the stocks of …rms whose market shares are increasing and sells the stocks of …rms whose market shares are decreasing. These computer industry returns are robust to size, market beta, Fama French factors, and turnovers. Furthermore, these high re-turns may not be attributable to investor under-reaction to slow di¤usion of company speci…c information given analyst coverage and high public availability of information about the computer industry. We present a simple consumption-based asset pricing model with industry dynamics based on network e¤ects that explains these empiri-cal …ndings. Our …ndings suggest that risk and returns of stocks in the computer industry are driven, not just by behavioral mechanisms, but rather by its underlying "winner-takes-all" industry structure.
This draft: 2014:11:08:12:15:04. I am greateful for many people, especially Preston McAfee, for their help for the preparation of this draft. I am also thankful for Hengjie Ai, Susan Athey, Lanier Benkard, Ulrich Doraszelski, Igal Hendel, Ali Hortacsu, Leslie Marx, Ming Yang, and Shiying Yu for helpful conversations. I am also grateful for and Andrew Berkin (First Quadrant) for helpful comments. I gratefully acknowledge …nancial supports from the Frontier Project of the Japanese Ministry of Economics, Trade, and Industry (Grant No. 07131), Nomura Foundation, Grants-in-Aid for Scienti…c Research (Grant No. 208032, 2053226, and 228026) from the Japan Society for the Promotion of Science, and the National Science Foundation (Grant No. 1247988).
I.
Introduction.
On December 12, 1980, Apple went public with the (split-adjusted) price $2.75 per share. On January 25, 2013, an Apple stock is traded on $439.88. On March 13, 1986, Microsoft went public with the (split-adjusted) price of $0.073 per share. On January 25, 2013, a Microsoft stock is traded on $27.88. On March 12, 1986, Oracle went public with the (split-adjusted) price $0.019 per share. On January 2013, an Oracle stock is traded on $35.38. On August 19, 2004, Google went public with the price $85.00 per share. On 2013, a Google stock is traded on $753.67. These examples show that the stocks in the computer industry can have extraordinary returns. Indeed, Morgan (2012) documents that 4 out of 10 stocks that have highest returns for the period 1992-2012 are computer-related. Then the research question of this paper is to understand the structure of stock returns in the computer industry. In addition to interests from the asset managements, this question is important since the information technology in the computer industry plays a crucial role in today’s economy as a “general purpose technology" that drives the evolution of the production system1and economic growth2.
Nevertheless, there have been no previous academic studies on this topic. Indeed, in one of the …rst studies that estimate the volatility of computer industry stocks, Camp-bell, Lettau, Malkiel and Xu (2001) note that “there is surprisingly little empirical research on volatility at the level of the industry or …rm”(p.2.) The standard equilib-rium asset pricing models (e.g. Lucas (1978) andDu¢ e and Zame (1989)) derive the CCAPM formula for the expected returns in the exchange economy. But these models abstract the product market competition among …rms although it is the determinant of the dividend processes of securities. On the other hand, the industrial organization literature (e.g. Tirole (1988) and McAfee (2005)) consider competitive strategies of …rms with market powers in an industry and obtain regulatory policy insights. But these works do not consider their implications on the stock returns. This paper intends to …ll the gap by documenting the empirical regularities of stock returns in the com-puter industry and by developing theories to explain these empirical regularities based on its industrial structure.
Bresnahan (2000),Bresnahan and Greenstein (1999), and Bresnahan, Greenstein and Henderson (2011) describe the computer industry as being organized around the
1SeeMilgrom and Roberts (1990b). 2SeeBresnahan and Trajtenberg (1995).
platforms3 for each horizontal layer of network computing4. Within each layer, a
plat-form introduces network e¤ects associated with social scale economies and barriers to entry to new entrants.5 On the other hand, there are scope diseconomies across layers and antitrust concerns that prevent vertically integrated suppliers and limit dominant …rms to their layers. For competition within horizontal layers, Greenstein (1993) empirically shows the e¤ect of installed bases in Federal government computer pro-curements andGandal (1994) establishes network externalities for o¢ ce spreadsheet software. For competition across layers, Sinkinson (2012) studies exclusive contracts for smartphones between Apple and AT&T and Lee (2013) considers vertical inte-gration in the videogame industry. None of them consider the implications on stock returns.
With regard to the …nance literature, Campbell, Lettau, Malkiel and Xu (2001) …nd that the computer industry has the highest industry volatility, their stock volatili-ties are second largest just next to …nancial industries, and that stock volatilivolatili-ties have the highest standard deviation among the 10 industries with the largest market capital-izations. Agarwal, Bharath, and Viswanathan (2004) document signi…cant increases in idiosyncratic and total return volatilities associated with the adoption of eCom-merce. They also …nd that these increases are attributable to changes in …rm’s product market shares as a result of the adoption of new technology. Pastor, and Veronesi (2009) develop a general equilibrium asset pricing model that explain high volatili-ties and bubble-like patterns of innovative …rms based on the uncertainty concerning technological adoption. Garleanu, Panageas, and Yu (2012) consider asset pricing implications of technological growth. Recently, ? document that a few companies generate outstanding returns, many …rms fail, and returns are volatile in the personal computer industry. But none of these papers systematically study the stocks returns in the computer industry, nor provide theoretical explanations of their behavior.6
Based on these prior results, the research questions of this paper are as follows. The …rst question is to identify empirical regularities of the stock returns in the computer industry and compare them with other industries. Answering this question is a …rst
3Platforms are de…ned to be a shared, stable set of hardware, software, and networking technologies
on which users build and run applications.
4Examples of a horizontal layer and the dominant platform are Microsoft O¢ ce for client
applica-tions, Microsoft Windows for client operating system, Intel for client processors, etc.
5See Katz and Shapiro (1994)Katz and Shapiro (1994) for a standard presentation on network
e¤ects.
6? note “we do not address whether that compensation (to risks) is consistent with a model of
step to understand the properties of stock returns in the computer industry. But this question cannot be resolved with the previous results sinceCampbell, Lettau, Malkiel and Xu (2001) and Bustamante (2012) do not provide systematic studies of the computer industry.
The second question is to build a model of asset pricing with the industry structure that rationalizes the basic feature of the computer industry to explain the empirical regularities identi…ed in the …rst question. Stocks are claims to the ownership of the …rm whose dividend processes are determined through competition in the product markets. As such, the industry structure should have profound impacts on the structure of stock returns in the industry. But this question has not been answered since Pastor, and Veronesi (2009) and Garleanu, Panageas, and Yu (2012) do not incorporate the product market competition into their model of asset pricing.7
To understand these questions, we proceed as follows. First, we consider a trading strategy called the industry market share momentum strategy involving buying the stocks of …rms with the highest industry market share growth rates and selling the stocks of …rms with the lowest industry market share growth rates. In addition to practical interests as an investment strategy8, the industry market share momentum strategy focuses on the behavior of market shares that are basic objects of study in industrial organization. Furthermore, by buying winners and selling losers in the same industry, the calculation of the returns from the strategy cancels out the industry level shocks common to all stocks in the same industry and focuses on the e¤ect of competition within the industry. Using the US market data 1965-2012, we calculate the returns from this strategy and compare the returns from computer industries with other industries. Then, we consider a consumption-based asset pricing model that incorporates network e¤ects and platform competition to see whether the model can match these empirical regularities.
The main …ndings of the paper are as follows. First, the computer industry market share momentum strategy has high expected returns and high risks compared with
7There have been surprisingly few works that connect product market competition and stock
re-turns. In real options literature,Aguerrevere (2009) and? consider the relationship between concen-tration of the industry and expected returns considered inHou, and Robinson (2003). See alsoBerk, Green, and Naik (1999) and Sagi and Seasholes (2007). None of these papers discuss interactions with the industrial organization literature. On the other hand, to the best of our knowledge, there are no results in the literatures of industry dynamics in the product market for concerning stock returns (seeAckerberg, Benkard, Berry, and Pakes (2007) a survey),
8Curtat (1996) …nds that the mutual fund performance persistence can be explained by the JT
Figure 1. Risk and Returns from the Industry Momentum Strategy.
other industries.9 Figure 1 describes the expected returns and the standard deviation
of returns of the industry speci…c market share momentum strategies for 3 months holding period for the largest 15 industries. Figure 1 clearly shows that the computer industry has the highest expected returns and also the highest risk. Indeed, the ex-pected returns from this strategy are higher than the exex-pected returns from the original return momentum strategy of Jegadeesh, and Titman (1993), hereafter called as the JT momentum strategy). Moreover, the computer industry market share momentum strategy has signi…cantly high CAPM and Fama French alphas than the industry av-erage. But at the same time, the computer industry market share momentum strategy involve higher risks than other industries. We note that the behavioral model based on slow information di¤usion and investor under-reaction may not be able to explain the high return from the industry market share momentum strategy in the computer industry because of higher availability of information about computer industry.10
Second, in contrast to the behavioral explanations, the rational model of asset pricing with industry competition and network e¤ects can match the observed patterns
9In summary, our empirical regularities are that “buying stocks of companies in network industries
whose market shares are increasing and selling stocks of companies in these industries whose market shares are decreasing are pro…table trading strategies, although it involves signi…cant risks.”
10Indeed, the JT momentum strategy performs poorly in the computer industry, in contrast to the
of positive correlations between increases in the market shares and expected stock returns. The mechanisms are as follows: in the computer industry where …rms engage in platform competitions, innovations based on new technologies, which drive gains in the market share, are often risky because of network e¤ects. In other words, if innovations are signi…cant enough to overturn the incumbent, the …rm will obtain a dominant position in the industry and will earn high pro…t. But otherwise, the network e¤ects of an incumbent …rm will drive the …rm out of the market.11 Second,
switching consumption from one product to another can be costly because of switching costs (Klemperer (1982)), learning by doing, and network e¤ects. Third, trading and rebalancing of the portfolios in …nancial markets are relatively cheap. The combinations of these factors imply that an innovations in new technology involves risks: consumers may be locked in products that may not be able to survive against incumbents market share that they cannot get out without paying the switching costs. Thus there are gains from hedging these risks by rebalancing portfolios in the …nancial markets by putting more weights on the stocks of competitors.12
The contributions of the paper are as follows. First, this paper documents the empirical regularities of stock returns in the computer industry in the form of industry market share strategies. In contrast to previous results by Campbell, Lettau, Malkiel and Xu (2001) and?, we focus on the computer industry, control various factors, and make comparisons with other industries.
Second, we develop a …rst model of asset pricing that incorporates the feature of network e¤ects and imperfect industry competition. In contrast to previous asset pricing model ofPastor, and Veronesi (2009) andGarleanu, Panageas, and Yu (2012), we incorporate the industry structure into the asset pricing model to match empirical
11That is, network e¤ects creates a growth risk in the sense ofJohnson (2002).
12As an example, consider Apple in the 1980s and 90s. Apple’s Macintosh is an innovation that
features a mouse and a graphical user interface. Suppose a …rm had decided to adopt Macintosh. There are 3 implications of this decision. First, when the …rm switches to the Macintosh from IBM PCs that the …rm had used before, it would take some learning costs till the …rm can e¢ ciently deploy Mac, and there would be further switching costs if the …rm decides to move to IBM PC. Second, network e¤ects make it costly to have diversi…ed portfolios of Mac and IBM PCs in the same …rm. Third, although Mac is innovative, Mac may not be able to survive the competition with the IBM PC platform that is used by majority of business users. If Apple fails in the competition, the …rm would have to switch to other softwares, but the move would be costly for the reasons pointed above. Then, the …rm would very well be a bit cautious in committing to Mac. One way to hedge these risks is to invest in stocks of competitors such as Microsoft that would bene…t when Mac is not able to catch up with the competition. In other words, network e¤ects and platform competitions make Apple stocks risky and lead to high required returns of Apple stocks. Tookes (2008)Tookes (2008) …nds exampels of information-based trading of competitor’s stocks related to earnings announcements.
regularities of stock returns in the computer industry.
Third, our …ndings on the industry market share momentum strategy can provide a microfoundation on the pro…tability of momentum phenomena. In contrast to the JT momentum strategy that are very hard to provide rational explanation13, the
in-dustry market share momentum is quantitatively stronger and has a simple risk-based explanation.14
The rest of the paper is organized as follows. Section 2 studies the performance of industry market share momentum strategies in the US market 1965-2012 and examine their pro…tability and characteristics. Section 3 focuses on the computer industry and examines its performance. Section 4 develops a model of asset pricing where …rms compete for platforms with network e¤ects. Section 5 concludes.
II.
Industry Market Share Momentum Strategy.
In this section we begin by de…ning the industry market share momentum strategy and compare its performance with the JT momentum strategy. Then we examine whether the expected returns from the industry market share momentum strategy can be attributed to size, CAPM beta, and/or Fama-French factors.
A.
Trading Strategy and Implementation.
A.1. De…nition 1: The Industry Market Share Momentum Strategy.
De…nition 1: The industry market share momentum strategy is a trading strategy that selects stocks based on the past industry market share growth rates and holds the stocks for some period of time.
An economic interpretation of De…nition 1 is that the industry market share mo-mentum strategy is to buy stocks of …rms whose market shares are increasing and to
13See Fama and French (1996). Vayanos and Woolley (2012) provide rational explanation of
momentum based on funds ‡ows.
14This approach is indeed consistent with the recent results on the return momentum that point
out connections with the …rm growth. Moskowitz and Grinblatt (1999) consider industry momen-tum investment strategies which buy stocks from past winning industries and sell stocks from past losing industries. Liu, Warner, and Zhang (2004)point out that recent winners in past returns have temporarily higher, and recent losers have temporarily lower, future growth rates on average, and the duration of the average future growth rate dispersion roughly matches that of momentum pro…ts. See alsoAsness, Porter,and Stevens (2000) andGrundy and Martin (2000).
sell stocks of …rms whose market shares are decreasing. This trading strategy is related to theJT momentum strategy studied by Jegadeesh, and Titman (1993) in the sense that this strategy chooses the stocks based on some sorting variable. The di¤erence is that we use the industry market share growth rate as a sorting variable, in contrast to the past returns inJegadeesh, and Titman (1993).
The strategies are characterized by three parameters, sorting variables, sorting pe-riods, and holding periods. Speci…cally, for each month, we sort stocks according to the compounded value of the sorting variables calculated over the sorting period. When we sort all the available stocks in the market, stocks are sorted into 10 subsamples. When we restrict attentions to stocks in an industry, stocks are sorted into 5 subsam-ples. Based on these rankings, the top decile portfolio is called the winners portfolio and the bottom decile portfolio is called the losers portfolio. Then we consider the strategy that buys the winner portfolio that assigns equal weights to the securities in the top decile and sells the loser portfolio that assigns an equal weight to the stocks in the bottom decile. Then we compute the di¤erence between the performances of the winner portfolio and the loser portfolio over the holding period. These calculations give us a sequence of the returns over the holding period. We then test whether they are signi…cantly di¤erent from zero.15
A.2. Implementation.
We consider stocks who are included in the CRSP monthly stock security …les and the expanded CRSP/COMPUSTAT combined quarterly industrial …le from 1965-2012. We exclude …rms that do not have the CRSP permanent number (PERMNO). From the CRSP/COMPUSTAT …le, we exclude …rms which do not have the Standard and Poor’s Identi…er (GVKEY), SIC codes, and date information.
For the price, we use the PRC data from CRSP monthly security …les, which is the closing price or its bid/ask average for the last trading day of the month. For the return, we use RET data from the CRSP which is the holding period return including dividends. For the industry market share, we obtain each …rm’s sales from COMPUS-TAT and calculate each …rm’s weight over the total sales of the industry that the …rm
15We have assumed zero returns for delisted …rms. AsShumway (1997) documents, the …rm delisted
for negative reasons have large negative returns. Thus, our formulation of assuming zero return for every delisted …rms implies likely higher returns than they really are for losers. This induces a negative bias for our hypothesis. This implies that the results reported here are robust to delisting e¤ects.
belongs to. We use the 48 industry classi…cation ofFama and French (1997).16
B.
Returns from the Industry Market Share Momentum Strategy.
We now report returns from the industry market share momentum strategy and comparisons with the returns from the JT momentum strategy. Then we conduct subperiod analysis to test robustness of the results.
B.1. Empirical Regularity 1: Average Monthly Returns.
Table 1 reports the average returns from the industry momentum strategy for 32 di¤erent combinations of sorting periods and holding periods.
Empirical Regularity 1: The industry market share growth momentum strategy provide statistically signi…cant positive returns for every speci…cation.
An economic interpretation of Empirical Regularity 1 is that the industry market share growth strategy provides consistently positive returns and can be a viable method to describe the risk and returns of the stocks in an industry. We note that the most successful zero-cost strategy selects stocks based on their industry market shares over the previous 12 months and then holds the portfolio for 3 months. This strategy yields 2.33% per month (shown in Panel A) when there is no time lag between the portfolio formation period.
B.2. Empirical Regularity 2: Comparison with the JT Momentum Strat-egy.
The next step is to compare the industry market share momentum strategy with the JT momentum strategy. Table2 reports returns from the JT momentum strategy using the same sample and same criteria.
Empirical Regularity 2: For each speci…cation, returns from the industry market share momentum strategy are strictly higher than returns from the JT momentum strategy.
An economic interpretation of Empirical Regularity 2 is that the industry market share momentum strategy provides higher expected returns compared with the JT
Panel A Panel B J K= 3 6 9 12 K= 3 6 9 12 3 Buy 0.0174 0.0157 0.014 0.0135 0.0173 0.0146 0.0134 0.0127 -8.86 -10.98 -12.21 -13.91 -8.82 -10.26 -11.77 -13.91 3 Sell -0.0023 -0.0005 0.0014 0.0031 -0.0016 0.0006 0.0024 0.004 ( -1.19) ( -0.41) -1.36 -3.43 ( -0.83) -0.48 -2.25 -3.43 3 Buy-Sell 0.0196 0.0165 0.0131 0.0111 0.0188 0.0143 0.0115 0.0092 -32.54 -37.62 -35.91 -32.75 -33.36 -35.37 -32.88 -32.75 6 Buy 0.0193 0.0166 0.0151 0.0139 0.0177 0.0151 0.014 0.0126 -9.79 -11.65 -13.22 -14.47 -9.04 -10.67 -12.37 -14.47 6 Sell -0.0029 -0.0009 0.0012 0.0031 -0.001 0.0007 0.0025 0.0044 ( -1.52) ( -0.69) -1.12 -3.34 ( -0.53) -0.53 -2.35 -3.34 6 Buy-Sell 0.0221 0.0177 0.0144 0.0113 0.0187 0.0146 0.0118 0.0085 -37.63 -42.99 -39.29 -34.46 -34.17 -37.05 -33.12 -34.46 9 Buy 0.0202 0.0178 0.0157 0.014 0.0187 0.0161 0.0142 0.0126 -10.07 -12.34 -13.73 -14.53 -9.39 -11.34 -12.6 -14.53 9 Sell -0.0027 -0.0011 0.0014 0.0035 -0.0013 0.0005 0.0028 0.0048 ( -1.38) ( -0.81) -1.24 -3.74 ( -0.68) -0.34 -2.47 -3.74 9 Buy-Sell 0.0227 0.019 0.0146 0.0109 0.0199 0.0157 0.0116 0.008 -38.73 -44.99 -39.78 -33.46 -34.02 -36.97 -31.61 -33.46 12 Buy 0.0216 0.018 0.0153 0.0137 0.0197 0.0158 0.0136 0.0121 -10.41 -12.13 -12.94 -13.7 -9.5 -10.73 -11.6 -13.7 12 Sell -0.0029 -0.0008 0.002 0.004 -0.0013 0.0012 0.0036 0.0054 ( -1.45) ( -0.54) -1.67 -4.06 ( -0.62) -0.81 -3 -4.06 12 Buy-Sell 0.0243 0.0187 0.0135 0.01 0.0208 0.0145 0.01 0.0068 -36.83 -40.65 -33.7 -27.2 -31.93 -30.91 -24.43 -27.2 Table 1 Returns from the Industry Market Share Momentum Strategy.
momentum strategy. For example, the highest monthly expected return from the JT momentum strategy is 0.87% per month based on past returns over the previous 9 months that holds the portfolio for 3 months (Part B) which is signi…cantly less than 2.33% from the industry market share momentum strategy.
As another illustration of the point, in the original calculation of Jegadeesh, and Titman (1993) with the time period of 1965-1989, the highest expected return was 1.49%. In Jegadeesh, and Titman (2001) with the period of 1965-1998, the highest expected return was 1.23%. These returns are signi…cantly smaller than the ones from the industry market share momentum strategy.
Panel A Panel B J K= 3 6 9 12 K= 3 6 9 12 3 Buy 0.0113 0.0114 0.0118 0.0116 0.0122 0.0121 0.0122 0.0114 -5.67 -7.92 -10.09 -11.75 -6.06 -8.34 -10.48 -11.75 3 Sell 0.0096 0.0068 0.0068 0.0071 0.0051 0.0048 0.0053 0.0066 -3.91 -3.9 -4.95 -6.2 -2.1 -2.8 -3.85 -6.2 3 Buy-Sell -0.0009 0.0027 0.0034 0.0032 0.0047 0.0059 0.0058 0.0034 ( -0.56) -2.33 -3.64 -3.49 -2.88 -5.29 -6.56 -3.49 6 Buy 0.015 0.0144 0.0133 0.0119 0.0152 0.0143 0.0128 0.0112 -7.62 -10.16 -11.59 -12.31 -7.73 -10.03 -11.17 -12.31 6 Sell 0.0085 0.0061 0.0063 0.0077 0.0056 0.0045 0.0057 0.0076 -3.28 -3.32 -4.36 -6.33 -2.16 -2.51 -4 -6.33 6 Buy-Sell 0.0036 0.0071 0.0061 0.003 0.0072 0.0082 0.0053 0.0028 -1.89 -6.02 -5.79 -2.62 -4.15 -6.5 -4.15 -2.62 9 Buy 0.0156 0.0143 0.0127 0.0113 0.0157 0.0137 0.0119 0.0105 -7.93 -10.06 -11.19 -11.71 -7.9 -9.59 -10.54 -11.71 9 Sell 0.0085 0.0062 0.0069 0.0085 0.0055 0.0052 0.0066 0.0085 -3.21 -3.34 -4.76 -6.86 -2.11 -2.85 -4.58 -6.86 9 Buy-Sell 0.0038 0.0063 0.0056 0.0016 0.0075 0.006 0.0046 0.001 -1.8 -4.54 -5.63 -1.42 -4 -3.9 -3.99 -1.42 12 Buy 0.0151 0.0129 0.0115 0.0104 0.0143 0.0121 0.0107 0.0096 -7.67 -9.2 -10.25 -10.78 -7.28 -8.68 -9.59 -10.78 12 Sell 0.0081 0.0072 0.0081 0.0098 0.0065 0.0068 0.0082 0.0099 -3.06 -3.88 -5.52 -7.93 -2.5 -3.69 -5.64 -7.93 12 Buy-Sell 0.0039 0.0041 0.0032 0.0008 0.0048 0.0028 0.001 -0.0011 -1.89 -3.03 -2.99 -0.9 -2.42 -1.72 -0.72 -0.9 Table 2 Returns from the JT Momentum Strategy.
B.3. Empirical Regularity 3: Subperiod Analysis.
The next step is to check whether these returns are robust even if we restrict the sample period. Table3 reports results of the subperiod analysis.
Empirical Regularity 3: For each subperiod, average returns from the industry mar-ket share momentum strategies are positive and statistically signi…cant. The average returns do not have monotonic relationships over the time periods.
An economic interpretation of Empirical Regularity 3 is that the industry market share momentum strategy is e¤ective for all sample periods. For the second point, the average returns are 1.86% for the period 1965-81, 1.97% for the period 1982-1995, and 1.49% for the period 1996-2012. This result suggests that returns from the industry
1965-1981 1982-1995 1996-2012 P1 (Past Winners) 2.05 1.84 1.5 (-8.43) (-7.92) (-5.18) P2 1.56 1.93 1.63 (-7.39) (-9.51) (-7.42) P3 1.25 1.73 1.42 (-6.69) (-9.62) (-7.26) P4 1.13 1.55 1.21 (-6.4) (-9.23) (-6.58) P5 0.97 1.39 1.11 (-5.47) (-8.72) (-5.94) P6 0.78 1.26 1.06 (-4.35) (-8.01) (-5.51) P7 0.63 0.98 0.86 (-3.35) (-6.07) (-4.35) P8 0.47 0.74 0.63 (-2.44) (-4.3) (-2.78) P9 0.26 0.29 0.17 (-1.24) (-1.59) (-0.66) P10 (Past Losers) 0.2 -0.21 -0.25 (-0.89) (-0.98) (-0.82) P1-P10 1.89 2.05 1.69 (-24.61) (-23.41) (-22.86)
Table 3 Subperiod Analysis of the Industry Market Share Momentum Strategy.
market share momentum strategy are driven not just by the di¤usion of information within the economy since it is reasonable to assume that di¤usion of information has accelerated over the years.
C.
Relationship to the Size and the Market Beta.
We have seen that the returns from the industry market share momentum strategy are signi…cant and robust over the time periods. We now consider whether the strategy systematically picks high-risk stocks in terms of size17 and market beta so that these
returns can be explained by the size and the market risk.
Beta Average Market Capitalization P1 (Past Winner) 1.29 653,432 P2 1.25 1,032,191 P3 1.19 1,333,140 P4 1.15 1,628,740 P5 1.11 1,881,019 P6 1.09 1,871,699 P7 1.08 1,613,759 P8 1.13 1,280,678 P9 1.17 954,425 P10 (Past Loser) 1.2 498,666 P1-P10 0.09 154,766
Table 4 Betas and Market Caps of the Industry Market Share Momentum Straetgy Portfolios.
C.1. Empirical Regularity4: Betas and Sizes of the Winner and the Loser Portfolios.
Table 4 reports the post-ranking betas of the ten 12-month/3-month industry market share momentum portfolios and also their average market capitalizations. Empirical Regularity 4: The winners’ portfolio (in terms of industry market share growth rate) has higher beta than the losers’ portfolio. The winners’ portfolio has larger market capitalizations than the losers’portfolio.
The …rst economic interpretation of Empirical Regularity4is that the di¤erence in the beta of the zero-cost winners minus losers portfolio is positive (0.08). In contrast, the di¤erence is negative (-0.08) in the JT momentum strategy. It suggests that the in-dustry market share momentum strategy and the JT momentum strategy are driven by di¤erent mechanisms. In Empirical Regularity5, we will check whether this di¤erence in beta explains the returns from the industry market share momentum strategy
The second interpretation is that since the stocks in the winners portfolios have larger market capitalizations than the stocks in the losers portfolio, it is not that the size e¤ect is driving the returns from the industry market share momentum strategy. It is in contrast to the JT momentum strategy where case of the pro…t momentum of Jegadeesh, and Titman (1993) where the stocks in the losers portfolios are larger than winners portfolios.
C.2. Empirical Regularity 5: Returns from Size and Beta Based Subsam-ples.
As a next step of our study of the relationship with the size and the market risk, we now conduct subsamples analysis on the samples strati…ed on the basis of …rm size and ex ante estimates of betas. Speci…cally, we implement this strategy on three size-based subsamples (small, medium, and large), and three beta-based subsamples (low-beta, medium-beta, and high-beta stocks). Measuring relative strength pro…ts on size- and beta-based subsamples allows us to examine whether the pro…tability of the strategy is con…ned to any particular subsample of stocks. Table5 presents the average returns of the 12 month/3 month strategies for each subsample.
Empirical Regularity 5: The returns from the industry market share momentum strate-gies are statistically signi…cantly positive for all size-based and beta-based subsamples except for the largest stocks. The returns are not monotonic in the size nor in beta.
An economic interpretation of Empirical Regularity 5is that, although the returns from the industry momentum strategies come from relatively small stocks, but there are no clear monotone relationships in terms of size and market beta. It suggests that the size and the market beta are not related to the returns from the industry market share momentum strategy.
In addition, Table6 presents the risk-adjusted returns implemented within the size and the best-sized subsamples. The results are quantitatively and qualitatively close to the result in Table5 .
D.
Relationship to the Fama French Factors.
The previous subsection shows that returns from the industry market share mo-mentum growth strategy cannot be explained by the size e¤ect and market beta. Then the next question is to consider its relationship with the Fama-French factors which is shown to have an explanatory power of the cross section of expected stock returns (Fama and French (1992) andFama and French (1993)).
Panel A: Average Monthly Returns All S1 S2 S3 B1 B2 B3 P1 0.0192 0.0252 0.0153 0.012 0.0183 0.0178 0.0176 (Past Winner) (-12.15) (-14.42) (-10.18) (-9.18) (-14.55) (-13.69) (-9.44) P2 0.0157 0.025 0.0167 0.0111 0.0166 0.0168 0.0173 (-10.14) (-16.02) (-12.75) (-10.71) (-15.59) (-15.22) (-10.72) P3 0.0165 0.0204 0.0148 0.0105 0.0141 0.0146 0.0147 (-10.44) (-14.66) (-12.51) (-11.22) (-14.67) (-14.46) (-10.07) P4 0.012 0.0169 0.013 0.0102 0.0121 0.0128 0.0133 (-6.73) (-12.41) (-11.55) (-11.52) (-13.23) (-13.13) (-9.68) P5 0.007 0.0143 0.0111 0.0096 0.011 0.0117 0.0115 (-4.37) (-10.54) (-9.89) (-11.08) (-11.95) (-12.31) (-8.53) P6 0.0091 0.0114 0.0092 0.0091 0.0098 0.0103 0.0093 (-6.63) (-8.17) (-8.39) (-10.35) (-10.47) (-10.52) (-6.94) P7 0.0065 0.008 0.0069 0.0079 0.0075 0.0086 0.0073 (-3.65) (-5.61) (-6.15) (-8.88) (-7.78) (-8.51) (-5.24) P8 0.0011 0.0043 0.0044 0.0068 0.0054 0.0069 0.004 (-0.64) (-2.94) (-3.79) (-7.45) (-5.16) (-6.39) (-2.71) P9 0.0015 0.0006 0.0002 0.0052 0.0019 0.0041 0.0004 (-1.06) (-0.41) (-0.13) (-5.23) (-1.71) (-3.57) (-0.26) P10 0 -0.0007 -0.0041 0.0029 0.0007 0.0007 -0.0038 (Past Loser) (-0.03) ( -0.39 ) ( -2.87 ) (-2.68) (-0.55) (-0.51) ( -2.18 ) P1-P10 0.0196 0.0259 0.0194 0.009 0.0176 0.0171 0.0214 (-24.92) (-41.26) (-36.41) (-11.76) (-32.73) (-36.35) (-31.48) Table 5 Returns of Size-Based and Beta-Based Industry Market Share Momentum Portfolios.
D.1. Empirical Regularity 6: Sensitivity of Returns to the Fama French Factors.
Table 7 presents the sensitivities of these portfolios to the three Fama-French factors. The size decile ranks in this table are computed using NYSE size decile cuto¤s with the size rank of one being the smallest and the size rank of ten being the largest. Our …ndings are as follows.
Empirical Regularity 6: The di¤erence in sensitivities of the winners portfolios and the losers portfolios are smaller for any of beta (-0.01), SMB (0.03), and HML (-0.04) in comparison with those in the JT momentum strategy.
An economic interpretation of Empirical Regularity6is that returns from the indus-try market share momentum strategy are not sensitive to market risk and the Fama
Panel B: Excess Returns All S1 S2 S3 B1 B2 B3 P1 0.0106 0.0164 0.0063 0.0034 0.0143 0.0098 0.0043 (Past Winner) -10.1 -13.13 -7.22 -5.93 -10.51 -12.99 -3.46 P2 0.0074 0.0169 0.0079 0.0027 0.0121 0.0087 0.005 -6.98 -15.84 -10.41 -7.09 -11.05 -15.32 -4.55 P3 0.0072 0.0123 0.0063 0.002 0.0094 0.0066 0.0027 -6.62 -12.7 -8.78 -6.02 -9.81 -12.88 -2.58 P4 0.0037 0.0091 0.0046 0.0019 0.0073 0.0048 0.0011 -3.02 -9.52 -7.31 -5.8 -8.27 -9.69 -1.15 P5 0.0033 0.0065 0.0026 0.0012 0.0061 0.0037 -0.0004 -2.66 -6.9 -4.25 -3.86 -7.04 -7.75 ( -0.42 ) P6 0.0016 0.0037 0.001 0.0009 0.0049 0.0023 -0.0026 -1.78 -3.85 -1.6 -2.95 -5.43 -4.85 ( -2.65 ) P7 -0.002 0.0002 -0.0014 -0.0002 0.0026 0.0005 -0.0047 ( -2.03 ) -0.2 ( -2.21 ) ( -0.74 ) -2.83 -1.1 ( -4.75 ) P8 -0.0031 -0.0036 -0.0043 -0.0015 0.0007 -0.0011 -0.0082 ( -2.73 ) ( -3.41 ) ( -6.58 ) ( -4.60 ) -0.67 ( -1.97 ) ( -8.14 ) P9 -0.0064 -0.0078 -0.0085 -0.0032 -0.0025 -0.0039 -0.0124 ( -7.50 ) ( -6.79 ) ( -12.51 ) ( -8.51 ) ( -2.13 ) ( -6.34 ) ( -11.25 ) P10 -0.009 -0.0095 -0.0131 -0.0057 -0.0029 -0.0074 -0.0176 (Past Loser) ( -9.15 ) ( -7.31 ) ( -15.51 ) ( -12.84 ) ( -1.95 ) ( -9.55 ) ( -13.48 ) P1-P10 0.0198 0.0259 0.0194 0.009 0.0172 0.0172 0.0219 -24.49 -38.3 -36.23 -14.41 -30.73 -36.45 -33.44
Table 6 Excess Returns of Industry Market Share Momentum Portfolios
French factors in comparison with the JT momentum strategy with the coe¢ cients -0.14, -0.13, and -0.22.
D.2. Empirical Regularity 7: Can the CAPM and the Fama French Fac-tors Model Explain the Returns from the Industry Market Share Momentum Strategy?
We now examine whether returns from the industry market share momentum strat-egy are explained by these factors. Table8 reports the alphas that are residuals of the regression of the monthly momentum returns (less the risk-free rate) on the monthly returns of both the value-weighted index less the risk-free rate and the three Fama-French factors.
Empirical Regularity 7: The di¤erences of CAPM alphas between the winner portfolios and the loser portfolios are about the same magnitude in comparison with the raw return di¤erence. The same is true for the Fama-French alpha.
in-FF Factor Sensitivities Size Decile Market SMB HML P1 (Past Winners) 3.54 0.06 0.46 0.22 P2 4 0.06 0.43 0.22 P3 4.28 0.06 0.39 0.23 P4 4.51 0.05 0.37 0.22 P5 4.6 0.05 0.37 0.23 P6 4.52 0.05 0.38 0.23 P7 4.31 0.05 0.38 0.23 P8 4 0.06 0.39 0.25 P9 3.62 0.06 0.41 0.25 P10 (Past Losers) 3.07 0.05 0.46 0.27 P1-P10 0.48 0.01 0.01 -0.05 Table 7 Industry Market Share Portfolio Characteristics.
dustry market share momentum strategy cannot be explained by the CAPM or the Fama-French three factor model.
The result is similar to the JT momentum that cannot be reduced to the CAPM model and/or the Fama French Three Factors Model. Nevertheless, the underlying mechanisms are di¤erent: the JT momentum is driven by slow di¤usion of company speci…c characteristics in the economy, but the industry market share momentum strat-egy cannot be reduced to the information di¤usion.
III.
Computer Industry Market Share Momentum
Strategy.
The previous section shows that the industry market share momentum strategy is an e¤ective strategy that produces expected returns higher than the JT momentum strategy. In this section, we consider thecomputer industry momentum strategy that restricts attention to stocks in the computer industry and examine its performances and characteristics.
CAPM Alpha FF Alpha P1 (Past Winners) 2.07 1.89 (-6.72) (-6.13) P2 1.62 1.45 (-6.22) (-5.57) P3 1.27 1.1 (-5.39) (-4.68) P4 1.03 0.86 (-4.64) (-3.91) P5 0.84 0.67 (-3.88) (-3.13) P6 0.69 0.52 (-3.15) (-2.39) P7 0.5 0.33 (-2.23) (-1.48) P8 0.28 0.1 (-1.17) (-0.41) P9 -0.04 -0.23 ( -0.15 ) ( -0.88 ) P10 (Past Losers) -0.36 -0.56 ( -1.17 ) ( -1.83 ) P1-P10 2.01 2.03 (-22.69) (-22.54) Table 8 CAPM and Fama-French Alphas
A.
De…nition and Summary Statistics.
A.1. De…nition2: Computer Industry Market Share Momentum Strategy.
De…nition 2: The computer industry market share momentum strategy is the imple-mentation of the industry market share strategy restricted to the computer industry.
An economic interpretation of De…nition 2 is that the computer industry market share momentum strategy is to buy stocks in the computer industry whose market shares are increasing and sell stocks in the computer industry whose markets shares are decreasing. This strategy is related to the industry market share momentum strat-egy studied in the previous section. The di¤erence is that the computer industry market share momentum strategy restricts attention to stocks in the computer indus-try. Although the computer industry market share strategy limits the set of stocks that can be potentially chosen, it can have a higher return by excluding stocks in other
Industry # of Firms Stock Price Ret MV Sales Petroleum and natural gas 218.05 $28.09 1.31% $2,658,058 $441 Telecommunications 132.5 $26.31 1.40% $3,299,533 $326 Banking 336.63 $23.52 1.18% $1,127,282 $148 Pharmaceutical products 176.53 $26.55 1.54% $1,931,376 $77 Business services 439.33 $18.28 1.46% $762,368 $61 Retail 219.61 $21.35 1.24% $1,494,312 $257 Utilities 180.73 $25.80 0.98% $1,623,003 $169 Computers 128.91 $28.88 1.36% $2,266,473 $133 Electronic equipment 218.42 $18.53 1.42% $1,097,797 $92 Trading 273.74 $24.76 1.23% $861,369 $72 Insurance 109.44 $223.38 1.28% $1,980,522 $308 Consumer goods 80.84 $24.20 1.17% $1,885,748 $144 Machinery 151.43 $21.95 1.31% $852,845 $80 Chemicals 86.96 $27.79 1.16% $1,451,465 $157 Automobiles and trucks 73.86 $23.25 1.18% $1,431,312 $646
Table 9 Descriptive Statistics of 15 Largest Industries.
industries with lower return from the portfolio.
The de…nition of the computer industry follows the classi…cation of Fama and French (1997) that includes the …rms in the SIC code 3570-3579, 3680-3689, 3695-3695, and 7373-7375.
A.2. Empirical Regularity 8: Summary Statistics.
Table9 provides the descriptive statistics of the computer industry.
Empirical Regularity 8: The number of …rms in the computer industry is 129, the 14th highest out of 48 industries. The computer industry is the 9th largest in the market size and its average market value is the 13th largest one.
An economic interpretation of Empirical Regularity8is that the computer industry is su¢ ciently large and its average market value is also signi…cantly large. It implies that the results about the computer industry are not driven by the size e¤ect.
Panel A Panel B J K= 3 6 9 12 K= 3 6 9 12 3 Buy 0.0284 0.0242 0.0192 0.0175 0.0285 0.021 0.0177 0.0161 (-8.46) (-10.02) (-10.12) (-10.63) (-8.65) (-9.02) (-9.5) (-10.63) 3 Sell -0.0181 -0.0137 -0.0093 -0.0073 -0.0161 -0.0097 -0.0076 -0.0056 ( -6.14) ( -6.46) ( -5.41) ( -4.98) ( -5.51) ( -4.52) ( -4.45) ( -4.98) 3 Buy-Sell 0.0436 0.0343 0.0253 0.0215 0.0418 0.0266 0.0217 0.0192 (-18.78) (-20.29) (-18.07) (-16.22) (-18.15) (-14.55) (-14.48) (-16.22) 6 Buy 0.0349 0.026 0.0221 0.0188 0.0314 0.0226 0.0199 0.0159 (-10.35) (-10.94) (-11.44) (-11.15) (-9.32) (-9.48) (-10.2) (-11.15) 6 Sell -0.0186 -0.0136 -0.0106 -0.0079 -0.0141 -0.0101 -0.0081 -0.0054 ( -6.35) ( -6.33) ( -6.10) ( -5.30) ( -4.59) ( -4.62) ( -4.61) ( -5.30) 6 Buy-Sell 0.0504 0.0357 0.0292 0.0236 0.0425 0.029 0.0242 0.0183 (-21.52) (-21.48) (-20.75) (-19.06) (-17.87) (-16.5) (-15.99) (-19.06) 9 Buy 0.0324 0.0258 0.0216 0.018 0.0307 0.0229 0.0191 0.0157 (-9.17) (-10.4) (-10.74) (-10.43) (-8.77) (-9.39) (-9.65) (-10.43) 9 Sell -0.0172 -0.0135 -0.0093 -0.0062 -0.0144 -0.0095 -0.0067 -0.0035 ( -5.64) ( -6.15) ( -5.17) ( -3.97) ( -4.69) ( -4.26) ( -3.75) ( -3.97) 9 Buy-Sell 0.0465 0.0348 0.0268 0.0218 0.0421 0.0279 0.022 0.017 (-18.37) (-17.99) (-16.99) (-17.53) (-16.56) (-14.09) (-13.69) (-17.53) 12 Buy 0.0344 0.0262 0.021 0.0176 0.0304 0.0219 0.0179 0.0147 (-9.69) (-10.61) (-10.64) (-10.39) (-8.76) (-9.01) (-9.26) (-10.39) 12 Sell -0.0155 -0.0117 -0.0072 -0.0031 -0.0127 -0.0078 -0.0042 -0.0009 ( -4.99) ( -5.29) ( -4.01) ( -1.90) ( -4.01) ( -3.46) ( -2.30) ( -1.90) 12 Buy-Sell 0.0468 0.0345 0.0254 0.0184 0.0397 0.0261 0.0191 0.0137 (-18.58) (-20.13) (-18.43) (-13.47) (-15.25) (-14.46) (-12.72) (-13.47) Table 10 Returns of the Computer Industry Market Share Portfolios
B.
Returns from the Computer Industry.
B.1. Empirical Regularity 9: Average Monthly Returns.
Table 10 reports the average returns from the computer industry market share strategy for the 32 possible combinations of sorting periods and holding periods. Empirical Regularity 9: The returns from the computer industry market share momen-tum strategy provide statistically signi…cant positive returns for all 32 combinations. Moreover, the returns from the computer industry market share momentum strategy have signi…cantly higher returns than the industry market share momentum strategy studied in the previous section..
An economic interpretation of Empirical Regularity9is that the computer industry market share momentum strategy provides higher returns than the industry market share momentum strategy does.
We note that the returns from the computer industry market share momentum strategy have the same quantitative characteristics of the industry market share mo-mentum strategy studied in the previous section: the most successful zero-cost strategy selects stocks based on their industry market share growth rates over the previous 12 months and then holds the portfolio for 3 months.
B.2. Empirical Regularity 10: JT Momentum Strategy Applied to the Computer Industry.
As a next step, we then compare its performance with the JT momentum strategy applied to the computer industry. Both the JT momentum strategy applied to the computer industry and the computer industry market share momentum strategy select stocks from the computer industry. The di¤erence is that the JT momentum strategy chooses stocks according to the past returns and the computer industry market share momentum strategy chooses stocks according to the past industry market share growth rates. Table 11 reports the returns from the JT momentum strategy applied to the computer industry.
Empirical Regularity 10: For every possible speci…cation, average returns from the JT momentum strategy applied to the computer industry are less than the returns from the computer industry market share momentum strategy.
An economic interpretation of Empirical Regularity 10 is that the JT momentum strategy does not work very well in the computer industry. An implication is that the JT momentum strategy and the industry market share momentum strategies are based on di¤erent mechanisms.
In other words, there can be many causes the can cause increases of returns such as under-reaction to temporary shocks. But with quick information di¤usion, these under-reaction will disappear and there will be less continuation of returns. But on the other hand, the return continuation based on innovations and increases in market share can continue since an increase in the market share will increase the innovation risk in the network industry that will lead to further continuation of returns.
Panel A Panel B J K= 3 6 9 12 K= 3 6 9 12 3 Buy 0.01 0.0099 0.0101 0.0091 0.0118 0.0111 0.0103 0.0092 (-3.38) (-4.73) (-5.77) (-5.94) (-4.1) (-5.15) (-5.86) (-5.94) 3 Sell 0.0124 0.0068 0.006 0.0058 0.0095 0.0058 0.0047 0.0057 (-3.48) (-2.82) (-3.24) (-3.59) (-2.71) (-2.44) (-2.59) (-3.59) 3 Buy-Sell -0.0072 -0.001 -0.0003 0.0008 -0.0027 0 0.0005 -0.0021 ( -2.50) ( -0.48) ( -0.16) (-0.45) ( -0.92) (-0.02) (-0.24) (-0.45) 6 Buy 0.0162 0.0139 0.0115 0.0106 0.0148 0.0121 0.0105 0.0094 (-5.58) (-6.73) (-6.77) (-6.92) (-5.18) (-5.71) (-6.03) (-6.92) 6 Sell 0.014 0.0089 0.0068 0.0073 0.0106 0.007 0.0061 0.0073 (-3.88) (-3.54) (-3.5) (-4.23) (-2.91) (-2.83) (-3.05) (-4.23) 6 Buy-Sell -0.0067 -0.0029 0.002 -0.0011 -0.0021 -0.0016 0.0017 -0.0016 ( -1.88) ( -1.11) (-1.13) ( -0.55) ( -0.68) ( -0.62) (-0.96) ( -0.55) 9 Buy 0.0146 0.0128 0.0113 0.0104 0.013 0.0114 0.0104 0.0096 (-5) (-6.14) (-6.56) (-6.79) (-4.48) (-5.4) (-5.98) (-6.79) 9 Sell 0.0133 0.0082 0.0065 0.0076 0.0099 0.0063 0.0063 0.0071 (-3.55) (-3.23) (-3.27) (-4.27) (-2.71) (-2.48) (-3.15) (-4.27) 9 Buy-Sell -0.0068 -0.0032 0.0011 -0.0004 -0.0049 -0.0024 0.0012 -0.0004 ( -1.95) ( -1.19) (-0.56) ( -0.23) ( -1.41) ( -0.89) (-0.67) ( -0.23) 12 Buy 0.0138 0.011 0.01 0.0095 0.0122 0.0102 0.0094 0.009 (-4.83) (-5.39) (-5.96) (-6.49) (-4.34) (-4.89) (-5.6) (-6.49) 12 Sell 0.015 0.0098 0.0084 0.0087 0.0118 0.0084 0.0076 0.0084 (-4.01) (-3.72) (-4.15) (-4.93) (-3.13) (-3.22) (-3.74) (-4.93) 12 Buy-Sell -0.0089 -0.0058 -0.0026 -0.002 -0.0088 -0.0042 -0.004 -0.0041 ( -2.49) ( -2.09) ( -1.25) ( -1.11) ( -2.34) ( -1.53) ( -1.88) ( -1.11) Table 11Return from the JT Momentum Strategy Applied to the Computer Industry.
B.3. Empirical Regularity 11: Intra-Industry Comparison.
We have seen that returns from the computer industry momentum strategy are higher than returns from the industry market share momentum strategy and also than the returns from the JT momentum strategy applied to the computer industry. The next question is to compare the returns from the computer industry market share momentum strategy with returns from other industries. Table 12 reports the results from the comparisons with returns from the 15 industries that have the largest market capitalization.
Empirical Regularity 11: The computer industry has the highest returns from the in-dustry market share among all 48 industries for all possible combinations.
Holding Period in Months
3 Month 6 Months 9 Months 12 Months
Industry CumRet AvgRet CumRet AvgRet CumRet AvgRet CumRet AvgRet Computers 0.1298 0.0371 0.1897 0.0244 0.2108 0.0158 0.1943 0.0083 (-9.48) (-9.08) (-9.4) (-9.03) (-8.07) (-6.62) (-6.6) (-3.5) Consumer goods 0.1041 0.0312 0.1732 0.0239 0.2035 0.017 0.1698 0.0092 (-11.38) (-10.84) (-11.8) (-11.45) (-9.95) (-9.37) (-7.66) (-5.82) Electrical 0.058 0.0138 0.0733 0.0039 0.0497 -0.0027 0.0078 -0.0068 equipment (-4.89) (-3.14) (-4.11) (-1.07) (-2.44) ( -0.87 ) (-0.37) ( -2.67 ) Pharmaceutical 0.0761 0.0222 0.1022 0.0138 0.1323 0.0113 0.1344 0.0079 products (-7.18) (-6.65) (-7.31) (-6.62) (-7.62) (-6.89) (-6.55) (-5.5) Machinery 0.0699 0.0212 0.1096 0.0156 0.1139 0.0105 0.1101 0.0071 (-8.83) (-8.46) (-9.8) (-8.97) (-9.34) (-8.25) (-7.85) (-6.41) Automobiles 0.0808 0.023 0.1188 0.015 0.1184 0.0084 0.101 0.0039 and trucks (-8.08) (-7.51) (-8.2) (-7.12) (-6.57) (-4.97) (-5.04) (-2.63) Banking 0.0681 0.0199 0.103 0.0131 0.1164 0.0087 0.1223 0.0055 (-6.77) (-6.34) (-6.34) (-5.52) (-5.66) (-4.55) (-4.76) (-3.18) Trading 0.0571 0.0165 0.1016 0.0139 0.1143 0.0098 0.1174 0.0066 (-5.65) (-5.15) (-7.05) (-6.73) (-6.57) (-6.12) (-5.67) (-4.5) Retail 0.0481 0.0148 0.0622 0.0089 0.0802 0.0075 0.0875 0.006 (-6.74) (-6.51) (-6.18) (-5.51) (-6.88) (-6.24) (-6.67) (-6.2) Telecommunications 0.0557 0.0156 0.0888 0.0109 0.1065 0.008 0.1138 0.0062 (-5.65) (-5.07) (-5.89) (-4.91) (-5.74) (-4.67) (-5.8) (-4.68) Chemicals 0.0459 0.0129 0.0707 0.0087 0.0513 0.0028 0.0234 -0.0009 (-6.03) (-5.25) (-6.32) (-4.93) (-3.79) (-1.95) (-1.6) ( -0.71 ) Petroleum 0.0432 0.0124 0.072 0.0084 0.0654 0.0049 0.0718 0.0038 and natural gas (-4.58) (-4.02) (-3.85) (-3.35) (-4.32) (-2.83) (-4.41) (-2.62) Utilities 0.0195 0.006 0.0342 0.0051 0.0491 0.0048 0.0539 0.0038 (-5.72) (-5.39) (-6.98) (-6.42) (-8.1) (-7.7) (-7.48) (-7.03) Insurance 0.0157 0.0026 0.0274 0.001 0.0195 -0.0016 0.0117 -0.0023 (-1.91) (-0.95) (-2.38) (-0.48) (-1.45) ( -0.92 ) (-0.81) ( -1.71 )
Table 12Comparison of Returns from the Industry Market Share Momentum Porfolios in 15 Industries.
An economic interpretation is that the computer industry has the highest returns among all industries. For example, the computer industry has cumulative returns of 19.54% for 12 months. We also note that, among the 15 industries with the high-est industry size, the second highhigh-est returns are provided by consumer goods in-dustry (17.15%), the third highest returns are from the electronic equipment indus-try (15.53%), and the fourth highest returns are from the Pharmaceutical indusindus-try (12.24%).
B.4. Empirical Regularity 12: Subperiod Analysis.
Having con…rmed that the computer industry market share momentum strategy outperforms the JT momentum strategy, the next step is to check whether these returns are con…ned to come just some subperiod of our analysis. Table 13 reports average returns from the computer industry market share momentum strategy for 3 subperiods. Empirical Regularity 12: For all subperiods, average returns from the computer indus-try market share momentum strategy exceed average returns from the indusindus-try market share momentum strategy.
An economic interpretation of Empirical Regularity 12 is that the returns from computer industry market share momentum strategy have signi…cantly higher returns than the industry market share momentum strategies for all periods. For stocks in the computer industry, average return of the computer industry during the period 1996-2012 are 3.74%, far exceeding the average return of 1.49% from the industry market share momentum strategy.
We also note that there are no monotonic relationship over the years. Indeed, returns from the recent years 1996-2012 are higher than the returns from the previous years. The result provides some indication that returns in the computer industry are not driven by the di¤usion of information that could very well have accelerated over the years.
C.
Relationship to the Size and Market Beta.
We have seen that the computer industry market share momentum strategy has ro-bust returns over all the subperiods. Table15considers whether the computer industry market share momentum strategy systematically pick high-risk stocks.
1965-1981 1982-1995 1996-2012 P1 (Past Winners) 3.16 2.24 2.41 (-6.74) (-6.69) (-5.35) P2 1.77 2.21 1.84 (-4.72) (-6.8) (-5.15) P3 1.58 1.82 1.81 (-4.71) (-5.91) (-5.58) P4 1.67 1.44 1.37 (-5.65) (-5.12) (-4.81) P5 1.27 1.11 1.15 (-4.04) (-4.4) (-4) P6 0.66 0.73 0.75 (-2.42) (-3.03) (-2.6) P7 0.24 0.25 0.23 (-0.88) (-0.97) (-0.75) P8 -0.03 -0.23 0.04 ( -0.08) ( -0.83 ) (-0.11) P9 -0.17 -0.76 -0.49 (-0.52) ( -2.51 ) ( -1.23 ) P10 (Past Losers) -0.55 -1.49 -1.53 (-1.45) ( -3.77 ) ( -4.06 ) P1-P10 3.49 3.11 3.73 (-10.06) -12.17 -13.97
Table 13 Subperiod Analysis of the Computer Industry Market Share Momentum Strategy.
C.1. Empirical Regularity 13: Size and Market Beta of Portfolios.
Empirical Regularity 13: Winners and losers in the computer industry have smaller beta than the average. Winners have larger market capitalizations than the losers.
An economic interpretation of Empirical Regularity 13 is that the winners in the computer industry have a higher beta and the larger capitalizations than losers. The next step is to consider whether these di¤erences drive the returns from the computer industry market share momentum strategy.
C.2. Empirical Regularity 14: Size and Beta Based Subsamples.
Table15 presents the average returns and betas of the computer industry market share momentum strategy for each subsample.
Beta Average Market Capitalization P1 (Past Winner) 1.78 1,881,335 P2 1.73 1,737,886 P3 1.59 2,580,796 P4 1.54 2,826,187 P5 1.57 3,024,212 P6 1.46 3,290,438 P7 1.53 3,109,703 P8 1.55 2,440,236 P9 1.6 1,623,538 P10 (Past Winner) 1.63 311,043 P1-P10 0.15 1,570,292
Table 14 Beta and Market Cap of the Computer Industry Market Share Momentum Portfolios.
Empirical Regularity 14: For all size-based and beta-based subsamples, the computer industry market share momentum strategy has returns signi…cantly higher than returns from the industry market share momentum strategy.
An economic interpretation of Empirical Regularity14is that the computer indus-try market share momentum strategy has higher returns even after conditioning on sizes and betas.
We note that the return di¤erences among small-, medium-, and large size-based subsamples are smaller in the computer industry market share momentum strategy than in the industry market share momentum strategy. The same is true for the case of beta-based subsamples. That is, the returns from the computer industry are more stable with respect to size and betas.
Table 16 presents the risk-adjusted returns from the computer industry market share momentum strategy within the size and the best-sized subsamples. The results are quantitatively and qualitatively close to the results in Table 15 . First, returns from the computer industry market share momentum are consistently and signi…cantly higher than the industry market share momentum strategy. In addition, returns are more stable with the computer industry market share momentum strategy.
Panel A: Average Monthly Returns All S1 S2 S3 B1 B2 B3 P1 0.0262 0.0356 0.0258 0.016 0.028 0.0225 0.0243 (Past Winner) (-10.61) (-10.88) (-8.31) (-5.82) (-9.21) (-7.93) (-7.76) P2 0.0193 0.0302 0.0195 0.0115 0.0193 0.0185 0.0161 (-9.42) (-10.28) (-7.64) (-5.34) (-8.34) (-8.1) (-5.76) P3 0.0173 0.0235 0.0121 0.0096 0.0186 0.0155 0.0123 (-9.25) (-8.46) (-5.15) (-5.32) (-9.05) (-7.07) (-4.8) P4 0.0147 0.0172 0.0089 0.009 0.013 0.0114 0.0115 (-8.63) (-6.65) (-3.85) (-5.82) (-6.42) (-6.17) (-4.81) P5 0.0113 0.01 0.0089 0.0082 0.0107 0.0088 0.0077 (-6.42) (-4.32) (-4.14) (-5.48) (-6.43) (-4.88) (-3.3) P6 0.0071 0.0045 0.0035 0.005 0.0068 0.0049 0.0043 (-4.56) (-1.74) (-1.74) (-3.38) (-3.64) (-2.64) (-1.85) P7 0.002 -0.0006 -0.0016 0.0032 0.0021 0.0024 -0.0013 (-1.24) ( -0.22 ) ( -0.76 ) (-1.9) (-0.99) (-1.25) ( -0.57 ) P8 -0.0007 -0.0033 -0.0075 0.0034 -0.0008 -0.0029 -0.0047 ( -0.40 ) ( -1.26 ) ( -3.50 ) (-2.28) ( -0.39 ) ( -1.47 ) ( -1.89 ) P9 -0.0045 -0.0137 -0.0128 -0.0032 -0.0086 -0.005 -0.0181 ( -2.28 ) ( -4.95 ) ( -5.52 ) ( -1.87 ) ( -3.83 ) ( -2.26 ) ( -7.00 ) P10 -0.0117 -0.0098 -0.0177 -0.0116 -0.012 -0.009 -0.0183 (Past Loser) ( -5.28 ) ( -2.75 ) ( -6.78 ) ( -5.69 ) ( -4.47 ) ( -3.25 ) ( -5.70 ) P1-P10 0.0379 0.0363 0.0355 0.023 0.0328 0.0271 0.034 (-22.58) (-12.91) (-15.46) (-11.61) (-13.47) (-12.05) (-12.87) Table 15 Returns of Size-Based and Beta-Based Computer Industry Market Share Momentum Portfolios
D.
Relationship to the Fama French Factors.
D.1. Empirical Regularity 15: Sensitivity to Fama French factors
We now consider the relationship between the returns from the computer industry market share strategy and the market beta and the Fama-French factors. Table 17 presents sensitivities of the portfolios chosen by the computer industry market share momentum strategy to the Fama-French factor.
Empirical Regularity 15: The computer industry market share momentum strategy has higher loadings for beta (0.04), smaller loading for SMB (0.02), and higher loading for HML (0.06) than the industry market share momentum strategy. But these loadings are still smaller than the ones in the JT momentum strategies (i.e. -0.14, -0.13, and
Panel B: Excess Returns All S1 S2 S3 B1 B2 B3 P1 (Past Winner) 0.0169 0.0272 0.0141 0.0065 0.0236 0.0133 0.0081 (-8.43) (-8.98) (-5.31) (-2.99) (-7.62) (-5.98) (-2.85) P2 0.0108 0.0208 0.0099 0.0034 0.0141 0.0095 0.0031 (-7.02) (-7.96) (-4.43) (-1.95) (-6.04) (-5.1) (-1.28) P3 0.0086 0.0156 0.002 0.0007 0.0146 0.0067 -0.0003 (-6.09) (-6.58) (-1.04) (-0.49) (-7.31) (-3.95) ( -0.11 ) P4 0.006 0.0077 0.0001 -0.0001 0.0071 0.0024 -0.0008 (-4.76) (-3.46) (-0.05) ( -0.04 ) (-3.65) (-1.6) ( -0.38 ) P5 0.0027 0.0013 0.0003 -0.0012 0.0052 -0.0002 -0.0055 (-2.19) (-0.64) (-0.18) ( -1.07 ) (-3.31) ( -0.12 ) ( -2.62 ) P6 -0.0022 -0.0028 -0.0069 -0.0053 0.001 -0.0041 -0.0094 ( -1.90 ) ( -1.24 ) ( -4.09 ) ( -4.77 ) (-0.57) ( -2.73 ) ( -4.39 ) P7 -0.0073 -0.0102 -0.0105 -0.0062 -0.0028 -0.0069 -0.016 ( -6.49 ) ( -4.52 ) ( -5.86 ) ( -4.70 ) ( -1.38 ) ( -4.54 ) ( -7.66 ) P8 -0.0099 -0.0126 -0.0159 -0.006 -0.0054 -0.0112 -0.0191 ( -7.21 ) ( -5.64 ) ( -9.01 ) ( -4.99 ) ( -2.81 ) ( -7.36 ) ( -8.71 ) P9 -0.0136 -0.0211 -0.0243 -0.014 -0.0115 -0.0139 -0.0329 ( -8.95 ) ( -8.11 ) ( -13.17 ) ( -10.29 ) ( -5.01 ) ( -7.94 ) ( -14.23 ) P10 (Past Loser) -0.0203 -0.0217 -0.0281 -0.02 -0.0159 -0.0191 -0.0364 ( -11.03 ) ( -6.58 ) ( -11.95 ) ( -12.73 ) ( -5.42 ) ( -8.38 ) ( -11.28 ) P1-P10 0.0371 0.0387 0.0344 0.022 0.0323 0.0273 0.0349 (-21.21) (-13.28) (-14.65) (-10.58) (-12.98) (-12.47) (-13.11) Table 16Excess Returns of the Computer Industry Market Share Momentum Strategy
-0.22).
An economic interpretation of Empirical Regularity 15 is that returns from the computer industry market share momentum strategy are more stable and do not depend on the market beta and the Fama French factors compared with the returns from the JT momentum strategy.
D.2. Empirical Regularity 16: CAPM and Fama and French Alpha of Portfolios.
Table18 reports CAPM and Fama and French alphas from the computer industry market share momentum strategies.
Empirical Regularity 16: The CAPM alpha and the Fama and French alpha in the computer industry market share momentum strategy are much larger than the ones in
FF Factor Sensitivities Size Decile Market SMB HML P1 (Past Winners) 3.83 0.06 0.67 0.24 P2 4.34 0.03 0.68 0.15 P3 4.56 0.05 0.64 0.21 P4 4.64 0.05 0.6 0.24 P5 4.65 0.07 0.61 0.26 P6 4.68 0.07 0.54 0.27 P7 4.55 0.06 0.56 0.29 P8 3.93 0.03 0.59 0.22 P9 3.36 0.08 0.61 0.3 P10 (Past Losers) 2.88 0.02 0.65 0.17 P1-P10 0.97 0.05 0.02 0.07
Table 17 Characteristics of the Portfolios Formed by the Computer Industry Market Share Strategy
industry market share momentum strategy.
An economic interpretation of Empirical Regularity16is that the returns from the computer industry are higher than the industry market share momentum strategy even after controlling the market risk and the Fama French factors.
E.
Additional Considerations.
E.1. Empirical Regularity 17: Portfolio Turnover.
Empirical Regularity 17: Portfolio turnovers of the winners and losers portfolios from the computer industry momentum strategy are less than those from the industry average and also less than those in the JT momentum strategy.
The computer industry has a monthly turnover of 16.61% that is the 33rd high-est among 48 industries. It implies the turnover of semiannual 63%, lower than the turnover from the JT momentum strategy 84.8%18. Another interpretation of
Empir-ical Regularity 17 are that returns from the computer industry exceed returns from other industries even after taking into account of the transaction costs associated with turnovers.
CAPM Alpha FF Alpha P1 (Past Winners) 3.31 3.09 (-6.92) (-6.43) P2 2.44 2.26 (-6.17) (-5.73) P3 1.87 1.67 (-5.19) (-4.65) P4 1.44 1.23 (-4.3) (-3.69) P5 1.02 0.79 (-3.1) (-2.44) P6 0.57 0.36 (-1.73) (-1.08) P7 0.06 -0.17 (-0.17) ( -0.52 ) P8 -0.16 -0.36 ( -0.42 ) ( -0.96 ) P9 -0.54 -0.78 ( -1.32 ) ( -1.91 ) P10 (Past Losers) -0.8 -0.99 ( -1.68 ) ( -2.07 ) P1-P10 3.69 3.66 (-12.3) (-11.96) Table 18 CAPM and Fama-French Alphas
E.2. Empirical Regularity 18: Does the Information Di¤usion Hypothesis Explain the Returns in the Computer Industry?
Hong, Lim, and Stein (2000) develop the information di¤usion hypothesis of the JT momentum: they …nd that the JT momentum strategies work better among stocks with low analyst coverage and they suggest that slow di¤usion of the …rm speci…c information across the economy is a cause of returns from the JT momentum strategy. We now consider applicability of the information di¤usion hypothesis to the computer industry momentum strategy.
Empirical Regularity 18: The information di¤usion hypothesis does not apply to explain average returns from computer industry market share momentum strategy.
As a …rst support of the statement, consider the analyst coverage that is considered as a proxy of the rate of information ‡ow in the economy in Hong, Lim, and Stein
Industry Staying % Turnover % Telecommunications 85.95% 14.05%
Computers 83.83% 16.17%
Automobiles and trucks 83.98% 16.02%
Retail 82.80% 17.20%
Consumer goods 83.51% 16.49%
Chemicals 82.73% 17.27%
Utilities 80.89% 19.11%
Petroleum and natural gas 80.94% 19.06% Consumer goods 83.51% 16.49% Apparel 89.10% 10.90% Healthcare 84.18% 15.82% Medical equipments 82.87% 17.13% Pharmaceutical products 79.37% 20.63% Chemicals 82.73% 17.27% Utilities 80.89% 19.11%
Petroleum and natural gas 80.94% 19.06% Electronic equipment 80.77% 19.23% Banking 81.26% 18.74% Machinery 80.16% 19.84% Pharmaceutical products 79.37% 20.63% Insurance 82.16% 17.84% Business services 79.04% 20.96% Trading 80.16% 19.84%
Table 19 Turnover of the winner portfolios of the 15 Largest Industries.
(2000). In their paper, they consider the determinants of the number of analysts covering the …rm. Their …nding is that the …rm size is the most important determinant of the analyst coverage and that industry dummies have a small e¤ect in explaining the number of analysts in the …rm, increasing the R2 from 0.61 only to 0.63.19 This result
suggests that the analyst coverage in the computer industry would not signi…cantly di¤erent from other industries. Therefore, the analyst coverage will not explain the di¤erences in returns from the computer industry market share momentum strategy.
Second, we can consider the hits from the Google Search as another proxy of avail-ability of information about industries. Table XX reports the count from Google search from the industry name. The computer industry has the 13th highest Google search count out of 48 industries. As such, the high returns in the computer industry are not
driven by relative lack information di¤usion across industries.20
IV.
The Model of Asset Pricing with Industry
Dynamics
In the previous section we identi…ed the empirical regularities about stock returns in the computer industry. The key …nding is that the computer industry market share momentum strategy delivers statistically signi…cant returns higher than the industry market share momentum strategy and the JT momentum strategy. We have also seen that this empirical regularity cannot be explained by the behavioral mechanism of slow di¤usion of company speci…c information. We now develop an economic theory to explain the empirical regularities based on the characteristics of the computer industry. As we discussed in Introduction,Bresnahan (2000) andGreenstein (1999) identify the platform competition based on network e¤ects as the key characteristics of the computer industry. The network e¤ects imply that a …rm’s strategy and pro…ts are a¤ected by not only a …rm’s cost structure but also the distribution of market shares in the industry, and that a consumer’s choice is also a¤ected by his consideration of the market share. In other words, the state of the industry is described by the distribution of market shares among …rms.
There are 3 implications of network e¤ects. First, a large market share will help the …rm’s sales because consumers prefer products of the …rms with a large market share. Mathematically, it implies that the …rm’s pro…t function satis…es the increasing di¤erences in the …rm’s choice variable (that is assumed to be a price) and the market share of the …rm.
Second, network e¤ects imply that an innovative …rm has to confront with large market shares of the incumbents. Mathematically, it implies that a small …rm’s future on the market involves signi…cant uncertainty. Even if a …rm increases its market share a bit, this uncertainty will not disappear.
Third, network e¤ects imply that it is di¢ cult for a consumer to hedge this risk by having a diversi…ed portfolio of consumption of …rms from the same industry. Mathe-matically, it implies that a consumer can buy only from one …rm for an industry and
20The information di¤usion theory can explain the lack of returns from the return momentum
strategy of Empirical Regularity10: since the computer industry attracts high public attention. But itcannot explain the high returns from the computer industry market share momentum strategy.
cannot hedge the risk in the real consumption.
Then, there are 2 consequences of this network e¤ects. First, the network e¤ects imply that the …rm with a high market share will prefer to lower the price further to increase the market share. That is, the industry equilibrium will be characterized by the pattern of market dominance.
Second, when consumers purchase more from an innovative …rm, the consumer faces a larger risk from the product in case an innovative …rm will lose to incumbents with larger market shares. With the adjustment costs, the consumer will hedge this risk by selling the stocks of an innovative …rm and buying the stocks of competitors. That is, an innovative …rm will have a higher required rate of returns.
A.
Model.
We now formulate these ideas in a simple model of asset pricing with industry dynamics based on network e¤ects. We consider an economy with time[0; T].
A.1. Firms, Goods, and Securities.
Suppose there are K 2 N+ …rms in the economy and that consider 2 …rms in an
industry. We can consider these 2 …rms as small innovative …rms entering into the industry where other …rms behavior (assumed to be incumbents with a large market share) are …xed in the short term.21 Firm 1 and 2 produce identical products (but …rm
1’s product can be superior by its innovativeness that will be de…ned below.)
We assume that the market is complete. Each …rm produces a good and also issues a stock. Let D(t) = (D1(t); :::; DK(t)) be the dividend process and suppose
dD(t) = D(t)dt+ D(t)dBt where D(t) = ( 1;D(t); :::; K;D(t))be the di¤usion of the dividend process. Let S = (S1; :::; SK) be the security price process. Let G= S+D be the gain process.
Letm(t)2[0;1] be the market share of …rm 1 in the industry at time t. With the assumption of2 …rms in the industry, the market share of …rm 2 is 1 m(t).
21A possible justi…cation is that most of the demand for the incumbent comes from large customers
A.2. Consumers.
Suppose that there is a continuum of consumers. Each consumer is small so that they behave as a price taker and also ignore the impact of his purchase on the future market share of the …rm.
Letxi(t) = (xi
1(t); :::; xiK(t))be the consumption process of consumer i. Let Ui(xi) be the utility function of playerifrom the consumption processxi(t). We assume that
Ui(x) =
Z T
0
te
Ui(xi(t))dt
where is the discount rate and Ui(x
i) is the per period utility function from the consumptionxi(t) at period t.
Let i = ( i1; :::; iK) be the process of portfolio of securities owned by consumer i. Then the budget feasibility condition is
t St= Z t 0 sdGs+ Z t 0 ps(eis xs)ds
That is, the current wealth from the portfolio is equal to the trading gains and net consumption purchases.
Assumption 19: Consumer Utility with Network E¤ects
Assumption 19: We assume that
e
Ui(xi(t)) = (Vi(xi(t)) + fxi1(t)(m(t) +i+"i(t)) +xi2(t)(1 m(t))g:
whereV(xi(t))is the utility from the consumption that does not depend on the network externality in the industry. xi
1(t)(m(t) + i(t) +"i(t)) is the term that depends on
the network e¤ects. 2 R+ denotes the importance of the network e¤ects in the
consumer’s utility. m(t) +i+"i(t) represents the marginal utility involving the term of
network externality: m(t) is its market share, i is the term that represents the relative advantage of innovation of the product of …rm 1 (over …rm 2), and"i is consumer i’s
idiosyncratic utility from good of …rm 1.
An economic interpretation of Assumption 19 is as follows. Consumer i compares the network e¤ects utilities from products of …rm 1 and 2 by looking at their market shares, the innovativeness of the product of …rm 1, and consumer i’s personal
prefer-ences. Firm 1, presumably with a small market share, may su¤er from the lack of the market sharem(t)compares with the large market share of …rm 2 (1 m(t)). Firm 1 can compensate this lack of the market share by the innovativeness of the productior through consumeri’s idiosyncratic preferences for products of …rm 1.
Assumption 20: Purchase from Only One Firm.
Assumption 20: Each consumer can purchase only one unit, and only from one …rm in the industry.
An economic interpretation of Assumption20is that, because of the network e¤ect, it is costly for a consumer to have a diversi…ed portfolio of products of two …rms. In an example of PC industry, a user can have all IBM PCs or all Macs in his house, but not a mix of the two.
Assumption21: Small Firm Risk. We now introduce a shock to the consumption at each periodt.
Assumption 21: Let exi
n(t) = (1 + n;X(t)dBt)xin(t)be the actual consumption of player
i for the nth good at period t.
An economic interpretation is that an agent faces risks for consumptions of products from small …rms with incumbents that have large market shares. The key assumption is that the size of the shock for theith good X;i(t)is independent of its consumption level
xn;i(t)since a small change in the market share will not signi…cantly a¤ect uncertainty facing the large incumbents.22 The multiplicative formulation implies that the volatility
of consumption of a product is increasing in proportion to the amount of consumption of the product.
We assume that the shocks are negatively correlated among goods (…rms). An economic interpretation is that a positive shock to …rm 1 is likely to cause a negative shock to …rm2.
A.3. Industry Dynamics.
We now describe the industry dynamics between …rm 1 and 2. Firms choose the prices of their productsp(t) = (p1(t); p2(t)) at each periodt.
22This assumption is reasonable from the point of view of the empirical regularity that winning …rm
