Research

US public state pension deficits are very large, accounting for 18.5% of an average state's GDP and up to 50% in Illinois. In principle, households should respond to this heavy future burden by increasing current savings, particularly in safe assets, since pension deficits are countercyclical. Comparing households residing on opposing sides of a state's border, I document that households in larger-deficit states save more, investing more in safe bank deposits and less in risky stocks. Specifically, households hold 0.70 dollars more in deposits and 0.33 dollars less in stocks for each additional dollar of pension deficit. This effect strengthened further following the implementation of new accounting standards in 2015 that made deficits more salient by requiring states to publicly disclose them. Exploiting staggered state pension reforms, I also find that households respond consistently when states reduce pension deficits; they decrease deposits and increase stock holdings. These reallocations spill over onto local economic activity: as households withdraw deposits following a pension reform, exposed local banks cut lending to local businesses.

We study how digital disruption impacts bank competition, considering consumers’ heterogeneous digital preferences. Exploiting the rollout of 3G mobile networks, we find that digital disruption geographically expands bank lending but contracts bank branch networks. Meanwhile, more (less) branch-reliant banks increase (decrease) prices. These developments result in a distributional effect, reducing the unbanked rate among young consumers while increasing it among the elderly. A structural model shows that increased digital preference of young borrowers drives banks’ branch adjustment, causing significant surplus losses for older savers. Regulating branch closures could mitigate the distributional impact as the banking sector undergoes digital transformation.

We document aggregate outflows from corporate bond funds days before and after the announcement of increases in the Federal Funds Target rate (FFTar). To rationalize this phenomenon, we build a model in which funds’ net-asset-values (NAVs) are stale and investors strategically redeem to profit from the mispricing when they learn about the increases of FFTar. Consistent with the model's predictions, we find that stale NAVs and loose monetary policy environments weaken (strengthen) outflows sensitivity to increases in FFTar during illiquid (liquid) market conditions. Our results highlight when and how monetary policy could systematically exacerbate the fragility of corporate bond funds.

While regulators stress the importance of passive investors' fiduciary duty in corporate governance, we argue that when passive funds fulfill their fiduciary duty to increase a firm’s value, they unintentionally create market inefficiencies. In our model, investors acquire information, optimize portfolios, and affect a firm's value through governance. In equilibrium, bad firms are left governed by passive investors, a consequence of market clearing. Therefore, when passive investors increase a firm's value, they increase it more for bad firms, reducing information sensitivity and generating market inefficiencies. We offer unique empirical predictions and explore applications on ESG policies and product market competition.

This paper uncovers a novel phenomenon of flight-to-Bitcoin (FTB) whereby local demand for Bitcoin increases with local economic policy uncertainties. FTB holds across Bitcoin demand proxies including local premiums over the US market, turnover, and web traffic on cryptocurrency exchanges. We find that FTB is mainly driven by a lack of confidence in local authorities. Consistent with this interpretation, FTB is stronger during corruption incidents and Bitcoin ownership shifts from centralized exchanges to decentralized wallets amid turbulence. A comparison with safe-haven assets further differentiates FTB from other forms of flight-to-safety. The evidence collectively illustrates one overlooked motivation of Bitcoin investment: investors hold Bitcoin to mitigate their concerns about local authorities.

This paper develops a structural model to examine high-frequency price dynamics. The key innovation is to allow trades' permanent price impact to be time-varying—dynamic trade informativeness. A distribution-free filtering technique pins the real-world data to the model. The filtered series (i) significantly recover the efficient price innovation through the dynamics of trade informativeness, (ii) improve trades' explanatory power for future returns, (iii) distinguish informativeness from trades' aggressiveness, (iv) gauge informed investors' patience, and (v) capture systematic patterns around scheduled and unscheduled events, as well as general intraday trends. The framework contributes to the better utilization of high-frequency trading data.