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In this paper, we find that individual stock momentum varies almost monotonically with industry growth. Firms in the highest industry growth quintile have significantly higher momentum compared to those in the lowest growth quintile. We find that the above-average growth group within each quintile has significantly higher momentum profits than the below-average group. Further, momentum profits of the highest industry growth quintile are always higher than those for the universe of firms, suggesting an economic benefit to stratifying firms based on industry growth and relative company growth intra-industry, while following a momentum investment strategy.

There has been a substantial body of recent literature documenting that the cross-section of stock returns is predictable based on past returns. DeBondt & Thaler, 1986 and DeBondt & Thaler, 1987 find that long-term past losers outperform long-term past winners over the subsequent 3 to 5 years. Jegadeesh and Titman (1993) show that, over intermediate horizons of 3 to 12 months, a portfolio that purchases past winners and sells past losers has a positive abnormal return. This evidence suggesting that simple trading strategies based on past returns can be used to achieve abnormal returns has received a great deal of attention. Such “momentum” strategies have been found to yield abnormal returns not only in U.S. markets, but also internationally. Rouwenhorst (1998), using a sample of stocks from twelve European countries, finds that a portfolio that is long in medium-term winners and short in medium-term losers earns approximately 1%/month. In addition to academia, practitioners such as stock analysts and portfolio managers have come to subscribe to the view that momentum strategies are one way to “beat the market”, to such an extent that today, momentum investing constitutes a distinct style of investment in both the United States and elsewhere. While the existence of momentum per se has been well documented, there is little agreement on the sources of profits of such strategies. Several explanations have been proposed for this apparently anomalous behavior in stock returns. The proposed explanations typically fall into one of the following three categories. 1. Those who argue that these results provide strong evidence against market efficiency: The original conjecture of Jegadeesh and Titman (1993) was that the market systematically underreacts to firm-specific information regarding its short-term prospects. Alternatively, it is possible that transactions by investors who buy past winners and sell past losers temporarily move prices away from their long-run values, thereby causing prices to overreact. This is consistent with arguments put forth by DeLong, Shleifer, Summers, and Waldmann (1990), that such overreaction is caused by rational speculators who indulge in positive feedback trading. More recently, a growing number of “behavioral” models have been proposed. These models suggest that momentum profits arise due to inherent biases in the way investors interpret information. Papers representative of this genre are Barberis, Shleifer, and Vishny (1998), Daniel, Hirshleifer, and Subrahmanyam (1998), and Hong and Stein (1999). While all three are behavioral models, momentum in the first model is an outcome of overreaction, while the last two models posit that momentum occurs due to underreaction – prices adjusting too slowly to news. 2. Those who argue that the returns to these strategies are compensation for risk: Conrad and Kaul (1998) argue that the profitability of momentum strategies can be entirely explained by the cross-sectional variation in mean returns of individual securities, rather than appealing to time-series predictability. More recently, Ahn, Conrad, and Dittmar (1999) utilize the stochastic discount factor methodology to study momentum trading strategies and conclude that momentum profits are not abnormal when judged against a non-parametric benchmark. These studies suggest that momentum profits are simply a manifestation of compensation for systematic risk factors that have yet to be included in extant asset pricing models. However, the preponderance of empirical evidence weighs heavily against a risk-based explanation. 1 3. Those who argue that these results are a product of data mining: Proponents of this argument maintain that momentum and other anomalies are simply the outcome of an elaborate data mining process. After all, return reversals and continuation are only two of the many patterns that empirical researchers have uncovered using the same stock price data. This argument suggests that once the momentum anomaly has been “discovered” and well documented, market participants will act in a way that will cause it to disappear in later years. However, Jegadeesh and Titman (1999) show that in the time period since 1990 (their 1993 paper analyzed stock return data up to 1989) momentum strategies continue to be profitable and past winners continue to outperform past losers by about the same magnitude as in the earlier period. Together with the Rouwenhorst (1998) international evidence, one may conclude that it is unlikely that the momentum phenomenon is due to mere chance. Fama (1998) argues that, consistent with the market efficiency hypothesis that most anomalies are chance results, apparent overreaction in financial markets is about as common as underreaction. However, in the same paper, he (cautiously) notes that the return continuation over the medium-term (momentum) seems to be an anomaly “above suspicion”. The empirical literature documents that momentum has been, and continues to be, a consistently profitable trading strategy, and continues to be so, at least before transaction costs are taken into account. It is a fairly robust phenomenon that has presented itself in various markets around the world. It is not surprising, therefore, that financial economists have devoted some attention in recent research to refining and improving the basic momentum strategy proposed by Jegadeesh and Titman (1993). One such paper is Moskowitz and Grinblat's (1999) that documents momentum as being primarily an industry phenomenon. Using data from a sample of NYSE, AMEX and Nasdaq firms during the period July 1963 through July 1995, they form industry portfolios of stocks based on 2-digit Standard Industry Classification (SIC) codes. They find that these industry portfolios exhibit significant momentum even after controlling for size, book-to-market equity (BE/ME), individual stock momentum, cross-sectional dispersion in mean returns, and potential microstructure influences. In their paper, once returns are adjusted for industry effects, individual stock momentum profits are significantly weaker, and, for the most part, statistically insignificant. Furthermore, industry momentum strategies are more profitable than individual momentum strategies.2 In this paper, we provide an extension to Moskowitz and Grinblatt's work, focusing on the relationship between industry growth and momentum. Absent a viable risk-based explanation, momentum in individual stocks is currently understood as a result of apparent systematic errors in valuation by the market; the actual mechanism at work might be either underreaction, overreaction, or both. Whatever the underlying mechanism, if one firm in an industry is likely to be misvalued systematically, it is likely that other firms in the same industry will be misvalued too. More generally, entire classes of industries might exist in which firms are systematically more misvalued than in others. This implies that momentum profits should vary along this dimension of classification. We posit that one such dimension that could prove useful in classifying stocks is industry growth. Hence, this study can be characterized as an attempt to refine the basic momentum strategy of buying winners and selling losers among the universe of firms, and to identify a class of stocks in which momentum profits are more prevalent and larger in magnitude. 3 It is well known that firms in growth industries are characterized by greater uncertainty and faster reversals of fortune than those operating in stable and mature industries. Thus, firms in growth industries are presumably harder to value accurately compared to firms in more mature industries. If systematic misvaluation is indeed the underlying cause of momentum profits, then such misvaluation is likely to be more pervasive in higher growth industries compared to other industries with lower growth rates. In a recent paper, Bakshi and Chen (2005) develop and empirically test a stock valuation model for the specific purpose of valuing individual stocks. Their model incorporates a pricing kernel consistent with a single-factor Vasicek (1977) term structure of interest rates, as well as a stochastic process for expected EPS growth. One of the principal conclusions from the empirical implementation of their model is that high-tech growth stocks (such as Intel) are more prone to misvaluation than traditional blue-chip stocks (such as GE). This result lends credence to our basic conjecture regarding higher momentum in stocks of companies belonging to higher growth industries relative to those in lower growth, mature industries. To illustrate, using two samples of firms for roughly the same time period, the first including NYSE, AMEX and Nasdaq firms and the second including only NYSE/AMEX firms, we investigate momentum profits for individual stocks split into quintiles by industry growth. We define industry growth as growth in total industry assets in the 2 years preceding portfolio formation. We find that individual stock momentum varies almost monotonically by industry growth. Firms in the highest industry growth quintile have significantly higher momentum compared to those in the lowest industry growth quintile. We also separately investigate momentum profits for two groups of firms within each industry growth quintile: those with asset growth above the industry average, and those with asset growth below the industry average. We find that the above-average growth group within each quintile has significantly higher momentum profits than the below-average group. The monotonicity of momentum profits by industry growth is preserved among the relatively higher growth firms. Further, the momentum profits of the highest industry growth quintile are always higher than those for the universe of firms, suggesting an economic benefit to stratifying firms based on industry growth and relative company growth intra-industry, while following a momentum investment strategy. The remainder of this paper is organized as follows. In Section 2, we describe the data and methodology employed in this study. In Section 3, we discuss the main empirical results. Section 4 concludes the paper.

In this study, we utilize two samples for analysis – one including only NYSE/AMEX firms, and the other including NYSE/AMEX and Nasdaq firms – and investigate momentum profits for individual stocks split into quintiles with industry growth. We find that individual stock momentum varies almost monotonically by industry growth. Firms in the highest industry growth quintile have significantly higher momentum compared to those in the lowest industry growth quintile. We also separately investigate momentum profits for two groups of firms within each industry growth quintile: those with asset growth above the industry average, and those below the industry average. We find that the above-average growth group within each quintile has significantly higher momentum profits than the below-average group. The monotonicity of momentum profits by industry growth is preserved among the higher relative growth firms. Moreover, the momentum profits of the highest industry growth quintile are always higher than those for the universe of firms, suggesting an economic benefit to stratifying firms based on industry growth and relative company growth intra-industry, while following a momentum investment strategy.