This article studies the empirical relationship between firms’ asset growth and idiosyncratic stock return volatility. In the cross-section, firms’ idiosyncratic return volatility is V-shaped with respect to their lagged asset growth rates: the volatility is higher for firms with extreme (either high or low) asset growth rates than for firms with moderate growth rates. In the time series, a higher dispersion across firms in asset growth rates predicts a higher average idiosyncratic return volatility. Moreover, the dispersion in asset growth rates has the strongest time series predictive power among alternative explanations of the average idiosyncratic return volatility, such as cash flow volatility and growth options. These findings indicate the importance of nonlinearity in studying the cross-sectional return volatility and provide a new explanation of the idiosyncratic return volatility that is significant in both the cross-section and the time series.

We propose a new approach, based on investment data, to determine firms’ return exposure to investment-specific technology (IST) shocks. When applied to U.S. data, we find that, in contrast to the pattern estimated from empirical IST proxies, value firms have higher exposure to IST shocks than growth firms. When applied to simulated data from existing theoretical models, our approach reveals that existing empirical findings may result from measurement errors in the IST proxies. Importantly, our simulation analysis uncovers the key role played by investment data in determining the economic mechanism through which IST shocks affect cross-sectional asset prices.

Capital utilization and market power crucially affect asset prices in an economy exposed to shocks that improve real investment opportunities through capital-embodied technological innovations. We embed these two mechanisms in a standard general equilibrium model and show that (i) the price of risk for investment shocks is negative under fixed capital utilization, but positive under sufficiently flexible capital utilization, and (ii) the equity return exposure to investment shocks is negative under perfect competition, but positive under high market power. We further show that, high market power, persistent components in technology growth, and a strong preference for early resolution of uncertainty are jointly important to quantitatively match the observed equity risk premium.

We investigate the determinants and effects of firm-initiated trading suspension during the Chinese stock market crash in July 2015. Our findings reveal that firms implemented suspensions to prevent investors’ panic selling, mitigate negative economic feedback, and safeguard specific shareholders’ interests. Once trading resumes, suspended stocks quickly align with the returns of comparable stocks, indicating that investors do not appear to penalize these firms in terms of valuation. Regarding post-resumption trading profits, small individual investors and institutions experience losses, whereas large individual investors and state agencies realize gains.