Information aversion—a preference-based fear of news flows—has rich implications for decisions involving information and risk-taking. It can explain key empirical patterns on how households pay attention to savings, namely, that investors observe their portfolios infrequently, particularly when stock prices are low or volatile. Receiving state-dependent alerts following sharp market downturns, such as during the financial crisis of 2008, improves welfare. Information-averse investors display an ostrich behavior: overhearing negative news prompts more inattention. Their fear of frequent news encourages them to hold undiversified portfolios.

Abstract The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, is more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

At the announcement of a new policy, agents form a view of state-contingent policy actions and impact. We develop a method to estimate this state-contingent perception and implement it for many asset-purchase interventions worldwide. Expectations of larger support in bad states—“policy puts”—explain a large fraction of the announcements' impact. For example, when the Fed introduced purchases of corporate bonds in March 2020, markets expected five times more price support had conditions worsened relative to the median scenario. Perceived promises of additional support in bad states alter asset prices, risk, and the response to future announcements. (JEL E52, E58, G12, G13, G14, G21, G28)

Abstract We construct a new data set tracking the daily value of life insurers’ assets at the security level. Outside of the 2008–2009 crisis, a $ 1 drop in the market value of assets reduces an insurer’s market equity by $ 0.10. During the ?nancial crisis, this pass-through rises to $ 1. We explain this pattern by viewing insurance companies as asset insulators, institutions with stable, long-term liabilities that can ride out transitory dislocations in market prices. Illustrating the macroeconomic importance of insulation, insurers’ market equity declined by $50 billion less than the duration-adjusted value of their securities during the crisis.

The conventional wisdom in finance is that competition is fierce among investors: if a group changes its behavior, others adjust their strategies such that nothing happens to prices. We estimate a demand system with flexible strategic responses for institutional investors in the US stock market. When less aggressive traders surround an investor, she adjusts by trading more aggressively. However, this strategic reaction only counteracts two-thirds of the impact of the initial change in behavior. In light of these estimates, the rise in passive investing over the last 20 years has made the demand for individual stocks 11 percent more inelastic. (JEL G11, G14, G23, G41)

Booming innovation often coincides with intense speculation in financial markets. Using over a million patents, we document two ways the market valuation of innovation and its economic impact become disconnected during bubbles. Specifically, an innovation raises the stock price of its creator by 40% more than is justified by future outcomes. In contrast, competitors’ stock prices move little despite their profits suffering. We develop a theory of investor disagreement about which firms will succeed that reconciles both the facts, unlike existing models of bubbles. Optimal innovation policy during bubbles must account for the disconnect.