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We investigate whether the returns of industry portfolios predict stock market movements. In the US, a significant number of industry returns, including retail, services, commercial real estate, metal, and petroleum, forecast the stock market by up to two months. Moreover, the propensity of an industry to predict the market is correlated with its propensity to forecast various indicators of economic activity. The eight largest non-US stock markets show remarkably similar patterns. These findings suggest that stock markets react with a delay to information contained in industry returns about their fundamentals and that information diffuses only gradually across markets.

In this paper, we investigate whether the returns of industry portfolios are able to predict the movements of stock markets. We begin our analysis with the US stock market. Over the period 1946–2002, we find that 14 out of 34 industries, including commercial real estate, petroleum, metal, retail, financial, and services, can predict market movements by one month. A number of others such as petroleum, metal, and financial can forecast the market even two months ahead. After adding a variety of well-known proxies for risk and liquidity in our regressions as well as lagged market returns, the predictability of the market by these 14 industry portfolios remains statistically significant. We have also done numerical simulations to gauge just how many industries will (with statistical significance) forecast the market simply by chance, and in only 0.04% of the simulations are 14 or more industries able to forecast the market and on average, only five (in contrast to the 14 we find) are able to do so at the 10% level of significance. In addition, we provide a few metrics regarding the statistical and economic significance of the documented predictability. First, we examine the ability of these industries to predict the market in comparison to well-known predictors such as inflation, default spread, and dividend yield and find comparable forecasting power. Second, we show that a portfolio incorporating information in past industry returns can lead under certain circumstances to a higher Sharpe ratio than simply holding the market. And third, we extend our analysis to each of the largest eight stock markets outside of the US, including Japan, Canada, Australia, UK, Netherlands, Switzerland, France, and Germany. In contrast to the US, these time series are limited to the period of 1973–2002 and we are unable to obtain the same set of controls (e.g., market dividend yield, default spread). With these caveats in mind, we find that the US results hold up remarkably well for the rest of the world. Our investigation is motivated by recent theories that explore the implications of limited information-processing capacity for asset prices. Many economists have recognized for some time that investors, rather than possessing unlimited processing capacity, are better characterized as being only boundedly rational (see Shiller, 2000; Sims, 2001). Even from casual observation, few traders can pay attention to all sources of information, much less understand their impact on the prices of the assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource (see, e.g., Kahneman, 1973; Nisbett and Ross, 1980). More specifically, our investigation builds on recent work by Merton (1987) and Hong and Stein (1999). Merton develops a static model of multiple stocks in which investors have information about only a limited number of stocks and trade only those that they have information about. As a result, stocks that are less recognized by investors have a smaller investor base (neglected stocks) and trade at a greater discount because of limited risk-sharing. Hong and Stein develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. As a result, the price underreacts to the information and there is stock return predictability.1 The hypothesis that guides our analysis of whether industries lead stock markets is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. The basic idea is that certain investors, such as those that specialize in trading the broad market index, receive information originating from particular industries such as commercial real estate or commodities like metals only with a lag. As a result, the returns of industry portfolios that are informative about macroeconomic fundamentals will lead the aggregate market. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset market reaches investors in other markets only with a lag, i.e., news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors might not pay attention to or be able to extract the information from the asset prices of markets that they do not specialize in. These two assumptions taken together lead to cross-asset return predictability. Our hypothesis appears to be a plausible one for a few reasons. To begin with, even among equity money managers, there is specialization along industries such as sector or market-timing funds. Investors have their hands full trying to understand the markets that they specialize in. As a result, they are unable to devote the attention needed to process potentially valuable information from other markets in a timely manner. Moreover, as pointed out by Merton (1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets and could be another rationale for why investors in one market are slow to adjust to information emanating from another. Individual investors also participate in a limited number of markets as they hold very undiversified portfolios (see Blume and Friend, 1978; King and Leape, 1984). We recognize that this gradual-information-diffusion hypothesis might not be the only explanation for our findings. However, it does provide a key auxiliary prediction that we can take to the data—namely, that the ability of an industry to predict the market ought to be strongly correlated with its propensity to forecast market fundamentals such as industrial production growth or other measures of economic activity. We test this prediction by first using individual industry returns to separately forecast industrial production growth and the growth rate of the Stock and Watson (1989) coincident index of economic activity. Many of the same sectors that lead the market can also forecast these two proxies of market fundamentals. Indeed, industry returns that are positively (negatively) cross-serially correlated with the market are also positively (negatively) cross-serially correlated with future economic activity. For instance, high returns for some industries like retail mean good news for future economic activity and the market, while high returns for other industries such as petroleum mean just the opposite. Beyond the standard statistical inference techniques, we have also performed various numerical simulation exercises to rule out that such a relation is due purely to chance. This finding strongly supports our hypothesis that the documented cross-predictability is due to the market reacting with a delay to information contained in industry returns about its fundamentals. It also distinguishes our gradual-information-diffusion hypothesis from other behavioral explanations of stock return predictability (see Section 2.3). Importantly, we have also extended this analysis to the eight largest stock markets outside of the US. The key relation, that the propensity of industries to forecast the market is correlated with their propensity to forecast industrial production growth, is also present in seven of the eight countries. The only country in which this relation does not emerge is Japan. Our paper is related to the literature on lead-lag relations among stocks, epitomized by the finding of Lo and MacKinlay (1990) that large stocks lead small stocks. A number of other papers followed trying to rationalize this finding (see, e.g., Brennan, Jegadeesh, and Swaminathan, 1993; Badrinath, Kale, and Noe, 1995; Jegadeesh and Titman, 1995). These studies typically find that stocks that are in some sense more liquid (e.g., have more analysts following or have institutional ownership) tend to lead less liquid stocks. However, Boudoukh, Richardson, and Whitelaw (1994) argue that many of these lead-lag relations are due to the own-autocorrelation of portfolios and a high contemporaneous correlation among portfolios. In other words, a large stock portfolio does not significantly lead a small stock portfolio once the lagged returns of the small stock portfolio are included in a multiple regression. Our findings differ from those in the above papers in a few respects. First, we focus on predicting the aggregate stock market, whereas the papers in this literature have focused on stocks of different characteristics leading or lagging one another. We are able to link this predictability to information about fundamentals such as industrial production growth. Second, in contrast to the lead-lag relations between small stock and large stock portfolios, our findings do not have an obvious liquidity interpretation since there is not an obvious difference in liquidity between stocks in the typical industry portfolio (e.g., oil) and stocks in the value-weighted market portfolio. Third, our findings are not due to the own-autocorrelation of portfolios since we control for lagged market returns in our predictive regressions and each industry portfolio constitutes only a small fraction of the market portfolio.2 Our paper proceeds as follows. In Section 2, we develop a simple model to make clear the assumptions behind our hypothesis and generate some testable predictions. We describe the data in Section 3. We present our empirical findings for the United States in Section 4. We extend our analysis to the eight largest stock markets outside of the US in Section 5. We conclude in Section 6.

We find that the returns of industry portfolios are able to predict the movements of stock markets. An industry's predictive ability is strongly correlated with its propensity to forecast indicators of economic activity. The results are similar for the eight largest non-US stock markets. These findings indicate that markets incorporate information contained in industry returns about their fundamentals only with a lag because information diffuses gradually across asset markets. The logic of our hypothesis suggests that we also find cross-asset return predictability in many contexts beyond industry portfolios and the broad market index. The key is to first identify sets of assets whose payoffs are likely correlated. As such, other contexts for interesting empirical work include looking at whether returns of stocks from one industry predict those in another or looking at stocks and the options listed on them. Indeed, a number of papers following ours have taken up this task and found confirming results. For instance, Menzly and Ozbas (2004) find that industry returns lead and lag each other according to their place in the supply chain. And Pan and Poteshman (2004) find that information could diffuse slowly from option markets to stock markets as option volume seems to be able to predict stock price movements. But much more work remains to be done on this topic.