# Limits to the linkages between asset prices and activity

A number of empirical studies report that firm investment reacts less to asset prices than what standard models predict. Research casts some doubt about the role of asset prices, in general, and Tobin’s q, in particular, in explaining investment. Blanchard et al (1993), for example, find a limited role for market valuation in explaining investment given fundamentals and current profits (see also Stein (2003) and Butzen et al (2003)). Other research finds that factors other than q or growth opportunities also drive investment (even though these may also be correlated with q).<sup>22</sup> Some studies suggest that, consistent with the presence of financial frictions, an important channel operates through the quantity rather than the cost (as reflected in q) of external financing.

The impact of asset price volatility on investment and other macroeconomic aggregates has been a fertile area of research. In theory, uncertainty associated with volatility has ambiguous effects on investment. On one hand, as Abel (1983) argues, uncertainty can increase the value of a marginal unit of capital and lead to more investment. On the other, as Dixit and Pindyck (1994) suggest, volatility may create incentives to delay investment, as more information about future payoffs becomes available over time, particularly given that investment may be irreversible (Bernanke (1983)). Households’ response to high uncertainty can be similar to that of firms; they reduce their consumption of durable goods as they wait for uncertainty to abate. On the supply side, firms’ hiring plans are also negatively affected by higher uncertainty because of the cost of adjusting personnel (Bentolila and Bertola (1990)).

Some recent empirical studies report that the macroeconomic uncertainty associated with volatile asset prices tends to lead to a decline in output. Evidence based on VAR models points to a significant negative impact of uncertainty shocks on output, investment and employment (Bloom (2009, 2014), Hirata et al (2012), Nakamura et al (2017) and Kose et al (2017b)). For example, a 1% increase in uncertainty is associated with a slightly larger than 1% decline in output in the first year (Figure 2). Using disaster data as instruments, Baker and Bloom (2013) offer evidence that causality runs from uncertainty to recessions and Bloom et al (2012) also report that (low) growth does not cause uncertainty. Predictions of theoretical models and findings from empirical studies collectively indicate that uncertainty, including that relating to asset prices, can play a dual role over the business cycle: it can be an impulse and a propagation mechanism (Gilchrist et al (2014) and Kose and Terrones (2012)).

Studies of household behaviour also suggest many deviations from the predictions of the standard models of consumption smoothing over the life cycle. Although the empirical linkages between financial wealth and household consumption are less contested, numerous puzzles remain. For example, household consumption depends as much on disposable income as on lifetime wealth. This is in large part due to financial “imperfections”: households have limited ability to borrow against future labour income, leading to liquidity constraints. Zeldes (1989) presents a test of the permanent income hypothesis against the alternative hypothesis that consumers optimise subject to borrowing constraints. He finds supportive evidence from household data that an inability to borrow against future labour income affects the

> 22 For evidence and reviews, see Fazzari et al (1988), Hubbard (1998) and Davis (2010b). Another channel is between productivity and q (Gomes (2001) and Abel and Eberly (2011)). Cuthbertson and Gasparro (1995) show that the average q alone is not a sufficient statistic but that output, capital gearing and the average q jointly provide an adequate model for capturing aggregate manufacturing investment behaviour in the United Kingdom (see also Bolton et al (2013)).

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