Intermediary asset pricing posits that financial institutions play a central role in financial markets, and that their decisions shape asset prices beyond simply reflecting the preferences of the average household. This perspective helps make sense of key empirical patterns: the excess volatility of asset prices, differences in price movements across asset classes, the cross-section of expected returns within asset classes, and specific arbitrage opportunities and price dislocations. We also review the implications of intermediary asset pricing for macroeconomic dynamics, international economics, and policy. In this approach, a primary channel of financial regulation and monetary policy is through alleviating constraints or removing risk from intermediary balance sheets during periods of stress. We highlight both existing progress and gaps for future research.

Poor financial health of intermediaries coincides with low asset prices and high risk premiums. Is this because intermediaries matter for asset prices, or because their health correlates with economy‐wide risk aversion? In the first case, return predictability should be more pronounced for asset classes in which households are less active. We provide evidence supporting this prediction, suggesting that a quantitatively sizable fraction of risk premium variation in several large asset classes such as credit or mortgage‐backed securities (MBS) is due to intermediaries. Movements in economy‐wide risk aversion create the opposite pattern, and we find this channel also matters.

Banks' balance sheet exposure to fluctuations in interest rates strongly forecasts excess Treasury bond returns. This result is consistent with optimal risk management, a banking counterpart to the household Euler equation. In equilibrium, the bond risk premium compensates banks for bearing fluctuations in interest rates. When banks' exposure to interest rate risk increases, the price of this risk simultaneously rises. We present a collection of empirical observations that support this view, but also discuss several challenges to this interpretation.

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.

We document extreme disruption in debt markets during the COVID-19 crisis: a severe price crash accompanied by significant dislocations at the safer end of the credit spectrum. Investment-grade corporate bonds traded at a discount to credit default swaps; exchange-traded funds traded at a discount to net asset value, more so for safer bonds. The Federal Reserve’s announcement of corporate bond purchases caused these dislocations to disappear and prices to recover. These facts inform potential theories of the disruption. The best explanation is an acute liquidity need for specific bond investors, such as mutual funds, leading them to liquidate large positions.

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.

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.

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.

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.