Households tend to hold substantial amounts of non-financial assets in the form of consumer goods inventories that are unobserved by traditional measures of wealth, about $725 on average for products covered by our sample. Such holdings can eclipse total financial assets among households in the lowest income quintile. Households can obtain significant financial returns from strategically shopping and managing these inventories. In addition, they choose to maintain liquid savings—household working capital—not just for precautionary motives but also to support this inventory management. We demonstrate that households earn high marginal returns from investing in household working capital, well above 20% at low levels of inventory, though these marginal returns decline rapidly as inventory increases. Nevertheless, average returns from inventory management are high—about 50% for the typical household—and affect household portfolio returns substantially for all but the top income and asset quintiles. We provide evidence from scanner and survey data that supports this conclusion. For many households, working capital is therefore an important asset class that has been largely ignored by the household finance literature, and inventory management provides them with an alternative to investing in risky financial markets at low levels of liquid wealth.

We use newspapers to create Equity Market Volatility (EMV) trackers at daily and monthly frequencies. Our headline EMV tracker moves closely with the VIX and the S&P500 returns volatility in and out of sample. We exploit the volume of newspaper text to construct forty category-specific EMV trackers. News about commodity markets, interest rates, real estate markets, aggregate activity, and inflation figure prominently in EMV articles. Policy news is another major source of market volatility: 30 % of EMV articles discuss tax policy, 30 % discuss monetary policy, and 25 % refer to some form of regulation. Combining our newspaper-based trackers with textual analysis of 10-K filings, we obtain monthly firm-level risk exposure measures. These measures help explain the cross-sectional structure of realized volatilities and its evolution over time, even after conditioning on firm and time fixed effects.