Gao, Sockin, and Xiong develop a model of neighborhood choice to analyze information aggregation and learning in housing and commercial real estate markets. In the presence of pervasive informational frictions, housing prices serve as important signals to households and commercial developers about the economic strength of a neighborhood. Through this learning channel, noise from housing market supply and demand shocks can propagate from housing prices to the local economy, distorting not only migration into the neighborhood, but also supply of commercial facility. The researchers' analysis also provides testable, nuanced implications on how the magnitudes of these noise effects vary across neighborhoods with different elasticity of housing supply and degree of complementarity of their industries.
This paper was distributed as Working Paper 26907, where an updated version may be available.
A number of theoretical research papers in both micro- and macroeconomics model and analyze attention, but direct empirical evidence is scarce. Olafsson and Pagel analyze the determinants of attention to financial accounts using panel data from a financial-management software provider including daily logins, discretionary spending, income, balances, and credit limits. The researchers argue that their findings cannot be explained by rational theories of inattention, i.e., mechanical information costs and benefits. Instead, they suggest that information- or belief-dependent utility generates selective attention and Ostrich effects. First, the researchers find that individuals are considerably more likely to log in and pay attention to their finances because they get paid. Second, they show that attention is decreasing in spending and overdrafts and increasing in cash holdings, savings, and liquidity within individuals' own histories. Third, attention jumps discretely when balances change from negative to positive. Olafsson and Pagel finally show that some of their findings can be explained by a recent influential model of belief-dependent utility developed by Kőszegi and Rabin (2009).
This paper was distributed as Working Paper 23945, where an updated version may be available.
Duffie and Antill compute optimal mechanism designs for each of a sequence of size-discovery sessions, at which traders submit reports of their excess inventories of an asset to a session operator, which allocates transfers of cash and the asset. The mechanism design induces truthful reports of desired trades and perfectly reallocates the asset across traders. Between sessions, in a dynamic auction market, traders strategically lower their price impacts by shading their bids, causing socially costly delays in rebalancing the asset across traders. As the expected frequency of size-discovery sessions is increased, market depth is further lowered, offsetting the efficiency gains of the size-discovery sessions. Adding size-discovery sessions to a double-auction market has no social value, beyond that of an initializing session. If the mechanism design relies on the double-auction market for information from prices, bidding incentives are further weakened, strictly reducing overall market efficiency.
Drechsler, Moreira, and Savov show, both theoretically and empirically, that liquidity creation induces negative exposure to volatility risk. Intuitively, liquidity creation involves taking positions that can be exploited by privately informed investors. These investors' ability to predict future price changes makes their payoff resemble a straddle (a combination of a call and a put). By taking the other side, liquidity providers are implicitly short a straddle, suffering losses when volatility spikes. Empirically, the researchers show that short-term reversal strategies, which mimic liquidity creation by buying stocks that go down and selling stocks that go up, have a large negative exposure to volatility shocks. This exposure, together with the large premium investors demand for bearing volatility risk, explains why liquidity creation earns a premium, why this premium is strongly increasing in volatility, and why times of high volatility like the 2008 financial crisis trigger a contraction in liquidity. Taken together, these results provide a new, asset pricing view of the risks and rewards to financial intermediation.
Bian, He, Shue, and Zhou provide direct evidence of leverage-induced fire sales contributing to a major stock market crash. The researchers' analysis uses proprietary account-level trading data for brokerage- and shadow-financed margin accounts during the Chinese stock market crash in the summer of 2015. They find that margin investors heavily sell their holdings when their account-level leverage edges toward their maximum leverage limits, controlling for stock-date and account fixed effects. Stocks that are disproportionately held by investors facing financial constraints experience high selling pressure and abnormal price declines that subsequently reverse over the next 40 trading days. Unregulated shadow-financed margin accounts, facilitated by FinTech lending platforms, contributed more to the market crash even though these shadow accounts had higher leverage limits and held a smaller fraction of market assets relative to regulated brokerage accounts.
This paper was distributed as Working Paper 25040, where an updated version may be available.