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.

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 empirically analyze how changes in access to housing collateral affect homeowner borrowing behavior. To isolate the role of collateral constraints from that of wealth effects, I exploit the fully anticipated expiration of resale price controls on owner‐occupied housing in Montgomery County, Maryland. I estimate a marginal propensity to borrow out of housing collateral that ranges between $0.04 and $0.13 and is correlated with homeowners' initial leverage. Additional analysis of residential investment and ex‐post loan performance indicates that some of the extracted funds generated new expenditures. These results suggest a potentially important role for collateral constraints in driving household expenditures.