Knowledge that Transforms

A contrast effect occurs when the value of a previously observed signal inversely biases perception of the next signal. We present the first evidence that contrast effects can distort prices in sophisticated and liquid markets. Investors mistakenly perceive earnings news today as more impressive if yesterday's earnings surprise was bad and less impressive if yesterday's surprise was good. A unique advantage of our financial setting is that we can identify contrast effects as an error in perceptions rather than expectations. Finally, we show that our results cannot be explained by an alternative explanation involving information transmission from previous earnings announcements.

We match administrative panel data on portfolio choices with survey measures of financial literacy. When we control for portfolio risk, the most literate households experience 0.4% higher annual returns than the least literate households. Distinct portfolio dynamics are the key determinant of this difference. More literate households hold riskier positions when expected returns are higher, they more actively rebalance their portfolios and do so in a way that holds their risk exposure relatively constant over time, and they are more likely to buy assets that provide higher returns than the assets that they sell.

This paper shows that the latest generation of asset pricing models with long‐run risk exhibit economically significant nonlinearities, and thus the ubiquitous Campbell‐Shiller log‐linearization can generate large numerical errors. These errors translate in turn to considerable errors in the model predictions, for example, for the magnitude of the equity premium or return predictability. We demonstrate that these nonlinearities arise from the presence of multiple highly persistent processes, which cause the exogenous states to attain values far away from their long‐run means with nonnegligible probability. These extreme values have a significant impact on asset price dynamics.

We develop a dynamic asset pricing model in which monetary policy affects the risk premium component of the cost of capital. Risk‐tolerant agents (banks) borrow from risk‐averse agents (i.e., take deposits) to fund levered investments. Leverage exposes banks to funding risk, which they insure by holding liquidity buffers. By changing the nominal rate the central bank influences the liquidity premium, and hence the cost of taking leverage. Lower nominal rates make liquidity cheaper and raise leverage, resulting in lower risk premia and higher asset prices, volatility, investment, and growth. We analyze forward guidance, a “Greenspan put,” and the yield curve.

In this paper, I examine asset pricing in a multisector model with sectors connected through an input‐output network. Changes in the network are sources of systematic risk reflected in equilibrium asset prices. Two characteristics of the network matter for asset prices: network concentration and network sparsity. These two production‐based asset pricing factors are determined by the structure of the network and are computed from input‐output data. Consistent with the model predictions, I find return spreads of 4.6% and −3.2% per year on sparsity and concentration beta‐sorted portfolios, respectively.

I develop a dynamic model of leverage with tax deductible interest and an endogenous cost of default. The interest rate includes a premium to compensate lenders for expected losses in default. A borrowing constraint is generated by lenders' unwillingness to lend an amount that would trigger immediate default. When the borrowing constraint is not binding, the trade‐off theory of debt holds: optimal debt equates the marginal interest tax shield and the marginal expected cost of default. Contrary to conventional interpretation, but consistent with empirical findings, increases in current or future profitability reduce the optimal leverage ratio when the trade‐off theory holds.

Rankings are omnipresent in the finance industry, yet the literature is silent on how they impact financial professionals' behavior. Using lab‐in‐the‐field experiments with 657 professionals and lab experiments with 432 students, we investigate how rank incentives affect investment decisions. We find that both rank and tournament incentives increase risk‐taking among underperforming professionals, while only tournament incentives affect students. This rank effect is robust to the experimental frame (investment frame vs. abstract frame), to payoff consequences (own return vs. family return), to social identity priming (private identity vs. professional identity), and to professionals' gender (no gender differences among professionals).

Because uncertainty is high in bad times, investors find it harder to assess firm prospects and hence should value analyst output more. However, higher uncertainty makes analysts’ tasks harder, so it is unclear whether analyst output is more valuable in bad times. We find that in bad times, analyst revisions have a larger stock‐price impact, earnings forecast errors per unit of uncertainty fall, and analyst reports are more frequent and longer. The increased impact of analysts is also more pronounced for harder‐to‐value firms. These results are consistent with analysts working harder and investors relying more on analysts in bad times.

In this paper, I examine the effect of pension policy on the structure of financial systems around the world. In particular, I explore the hypothesis that policies that promote pension savings also promote the development of capital markets. I present a model that endogenizes the extent to which savings are intermediated through banks or capital markets, and derive implications for corporate finance, household finance, banking, and the size of the financial sector. I then present a number of facts that are broadly consistent with the theory and examine a variety of alternative explanations of my findings.

This paper constructs a liquidity mismatch index (LMI) to gauge the mismatch between the market liquidity of assets and the funding liquidity of liabilities, for 2,882 bank holding companies over 2002 to 2014. The aggregate LMI decreases from +$4 trillion precrisis to −$6 trillion in 2008. We conduct an LMI stress test revealing the fragility of the banking system in early 2007. Moreover, LMI predicts a bank's stock market crash probability and borrowing decisions from the government during the financial crisis. The LMI is therefore informative about both individual bank liquidity and the liquidity risk of the entire banking system.