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An ICAPM which includes bank credit growth as a state variable explains 94% of the cross-sectional variation in the average returns on the 25 Fama–French portfolios. We find compelling evidence that bank credit growth is priced in the cross-section of expected stock returns, even after controlling for well-documented asset pricing factors. These results are robust to the inclusion of industry portfolios in the set of test assets. They are also robust to the addition of firm characteristics and lagged instruments in the factor model. Bank credit growth is important because of its ability to predict business cycle variables as well as future labor income growth. These findings underscore the relevance of bank credit growth in stock pricing.

A growing financial economics literature highlights the importance of bank credit in promoting future economic growth (Levine and Zervos, 1998, Beck and Levine, 2004, Levine, 2006 and Beck et al., 2008). Levine and Zervos (1998) find that developed financial systems measured by the level of bank credit, as well as market liquidity, predict future economic growth. King and Levine (1993) and Levine and Zervos (1998) construct and test an endogenous growth model in which developed banking systems spur economic growth through the innovation channel. Indeed, banks provide a unique range of services, such as assessing, monitoring and providing funding for productive entrepreneurs, which are critical contributors to innovation and productivity growth, and hence promote broader economic growth. Moreover, as noted by Levine (2006), banks alleviate informational asymmetries and therefore facilitate transactions, which in turn leads to future economic growth. This is particularly true for small firms which face external financing constraints (Beck et al., 2008). Well-developed banking systems ease these frictions, thus contributing to the expansion of small business and, accordingly, future economic growth. In addition, Levine and Zervos (1998) and Beck and Levine (2004) show that banking developments influence economic growth, even after controlling for various political and economic factors. Beck et al. (2007) also find that higher levels of bank credit to the private sector reduce income inequality by boosting the lowest labor incomes. These previous studies demonstrate that bank credit growth predicts future economic growth. This paper also finds that bank credit growth is a strong predictor of labor income growth. This result is consistent with the recent findings of Lynch and Tan (2011) which show that labor income growth is procyclical. In fact, labor income growth tends to be higher during expansions than during recessions. As higher (lower) bank credit growth predicts periods of strong (weak) economic growth during which labor income growth increases (decreases), it is not surprising to find that bank credit growth also helps in predicting labor income growth. The fact that bank credit growth predicts labor income growth suggests that bank credit growth should be a state variable in Campbell’s (1996) intertemporal capital asset pricing model (ICAPM). Indeed, Campbell (1996) demonstrates that any variable that predicts future labor income growth or stock returns is a candidate risk factor in asset pricing models. In the empirical tests, we find strong evidence that bank credit growth is a priced factor in the cross-section of expected stock returns, even after controlling for well-documented risk variables such as the Fama–French factors and liquidity risk. These findings are robust to different specification tests, including the misspecification robust t-test proposed by Kan and Robotti (2009) and Kan et al. (forthcoming). Adding industry portfolios to the set of test assets does not alter the main conclusion of the paper that bank credit growth is relevant in the pricing of stock returns in the United States. While bank credit growth’s impact on economic growth is well documented, our paper is among the few studies that examines the impact of bank credit growth on stock returns. An exception is Gorton and He (2008) who develop a model in which the strategic interaction between banks leads to bank credit cycles, which in turn bring about macroeconomic fluctuations.1 They also argue that since bank credit cycles determine business cycle conditions, they should be a priced factor in an asset pricing model of stock returns. As a proxy for bank credit cycles, Gorton and He (2008) construct an aggregate Performance Difference Index (PDI) 2 and find that the coefficient on the PDI is significant in the time-series regressions of 10 size portfolios. They thus conclude that bank credit cycles are priced. Our work differs from Gorton and He (2008) in two ways. First, as a proxy for bank credit cycles, we use bank credit growth instead of the PDI. 3 The advantage of bank credit growth over the PDI is that it can be computed with high frequency data and for a long sample period, while the PDI can only be computed at a quarterly frequency for a short sample period. Second, this paper investigates whether bank credit growth explains the cross-section of expected returns, whereas Gorton and He (2008) rely on a simple time-series analysis and do not perform any formal tests of model adequacy. Our study also complements the large existing literature which conducts empirical tests of asset pricing models. Petkova (2006) shows that an ICAPM that includes market excess returns and innovations in the variables that predict future returns performs better than the Fama and French (1993) model in explaining the average returns on the 25 Fama–French portfolios. However, Kan and Robotti (2009) and Kan et al. (forthcoming) find that, under the assumption of potentially misspecified models, Petkova’s (2006) model and the Fama–French model do not produce significantly different goodness-of-fit measures at conventional levels. This paper contributes to the existing literature by demonstrating that the ICAPM that includes bank credit growth outperforms the Fama–French model, even when the Kan et al. (forthcoming) test of equality of cross-sectional R2s is implemented. The rest of the paper is organized as follows. Section 1 presents Campbell’s (1996) ICAPM framework and introduces the two-pass methodology under potentially misspecified models. It also presents the GMM methodology under the same assumption. Section 2 reports the empirical results of the ICAPM that includes bank credit growth. Section 3 provides tests for robustness. Section 4 concludes.

A segment of existing economic literature has referenced that bank credit growth contributes to future economic growth as well as business cycle fluctuations. We contribute to this literature by documenting that bank credit growth is also a strong predictor of labor income growth. Given the ability of bank credit growth to forecast business cycle variables as well as future labor income growth, we believe that it could be an important state variable in Campbell’s (1996) ICAPM. Accordingly, we investigate whether bank credit growth is a determinant of expected stock returns in the U.S. The empirical investigation is carried out within the new framework developed by Kan and Robotti (2009) and Kan et al. (forthcoming). The former develops statistical tests within the GMM/discount factor framework under the assumption of potentially misspecified models. The latter constructs econometric measures within the two-pass cross-sectional regression framework also under the assumption of potentially misspecified models. We provide empirical evidence that bank credit growth is important when pricing stock returns in the U.S. The ICAPM with bank credit growth performs very well in explaining the average returns on the 25 Fama–French portfolios. In the presence of shocks to bank credit growth, the Fama–French factors and liquidity risk lose their explanatory power in the cross-section of expected returns. These findings are robust to the inclusion of industry portfolios in the set of test assets. They are also robust to the inclusion of firm characteristics or lagged instruments in the factor model. Moreover, bootstrap simulations show that our empirical results are not due to chance. These findings collectively highlight the importance of bank credit growth in stock pricing.