We fine-tune a suite of Large Language Models (LLMs) to identify AI patents with high out-of-sample precision and recall and study AI inventor migration between the US and China. We document a sharp increase in both volume and quality of the US-to-China migration, particularly after the onset of US–China trade tensions in 2018. These migrants significantly increase their patent output and exploratory innovation, with the largest gains observed among those joining research institutions, having greater experience, and a consistent research focus. In contrast, China-to-US migrants show declined patenting activity but gains in originality. Post-migration, immigrants significantly expand their collaborator networks. AI inventor migration generates significant and persistent local spillovers through multiple layers of collaborators, benefiting both countries. These findings highlight China’s growing attractiveness to top-tier AI talent and suggest that recent US policy changes may have accelerated the global reallocation of innovation activity.

Stress tests are conducted frequently, and they disclose valuable information about the bank. A credible stress test design must account for stakeholders’ strategic responses to the test results and that any bank passing a test will not fail thereafter. We build a dynamic coordination framework to study the optimal design of credible stress tests. Higher testing frequency reduces the need for greater stringency, enlarging the set of credible designs. We identify the optimal mix of test frequency and stringency and show how it depends on macroeconomic conditions, the bank’s systemic importance, private incentive distortions, administrative costs, and liability asynchronicity.

In 2016, China shifted from a one-child policy to a two-child policy, increasing female workers’ childbearing responsibilities. Using data from a peer-to-peer lending platform, we find that post-reform loan applications from female college students decrease by 15.6% compared to male students. This decline is more pronounced for long-term large loans and those aimed at human capital investment. Applications drop further after staggered provincial maternity leave extensions and with higher expected motherhood penalties. Our results indicate that female students anticipate worse job prospects, leading to reduced borrowing and lower investment in human capital. Credit supply channels are unlikely driving the results.

We develop a model with multiple sectors connected through both knowledge and physical input-output networks, in which network structures determine economic dynamics and constitute systematic risk in asset pricing. The model predicts that the sparsity of knowledge network increases aggregate consumption and leads to positive risk premium. Our model implications receive empirical support as follows: (i) consumption growth increases with changes in knowledge network sparsity; (ii) firms with higher exposures to knowledge network sparsity carry higher expected stock returns; and (iii) knowledge network sparsity exists in the stochastic discount factor and helps price the cross section of stock returns.

Traditional measures of mutual fund skills focus on portfolio activeness or performance but offer little insight into how managers select stocks. To decipher this managerial skill, we employ Large Language Models (LLMs) to measure the semantic distance between mutual fund prospectuses and the strategic priorities outlined in firms’ 10-K filings, which captures difficult-to-understand information that requires managerial expertise to process. Funds with high traditional skill measures outperform unskilled peers only when they invest heavily in distant stocks. Moreover, distant investments made by skilled funds predict future stock returns. Our findings reveal a novel mechanism behind managerial skills.

We study the market pricing of the risks associated with uninsured bank deposits, finding that banks heavily reliant on uninsured deposits exhibit significantly higher average stock returns. During the March 2023 banking turmoil, banks with higher levels of uninsured deposits were more adversely affected; however, they experience higher returns during stable periods, which compensates for the run risk that they face. This relation between run risk and returns is evident in all banks except the largest and is particularly pronounced in those with liabilities sensitive to interest rate changes, significant asset-liability mismatches, and lower capital ratios.

We examine the impact of financial influencers (``finfluencers'') on retail investment with detailed data from actual investment decisions in four Nordic countries. Using an instrument that randomly assigns influencers to followers, we find the following: (1) Investors tend to follow influencers with high Sharpe ratios, frequent trades, a shared country of residence or language, and male gender. (2) Influencers significantly impact their followers’ portfolio choices and trading activity. (3) This influence is particularly strong when influencers have a large follower base, occupy a central position in the social network, or actively engage in group discussions. The effect is also more pronounced among investors who follow fewer influencers, female investors, and when trading passive funds.

This paper examines the relationship between currency fluctuations and firm values,

focusing on the role of international trade networks. We argue that the relative currency

value of export destinations to import origins currencies is important. A local currency

appreciation relative to exporting destination countries reduces stock prices, while an

appreciation relative to import origin countries increases stock prices. These effects are

amplified by greater trade intensity and are observed at both the country-industry and

aggregate country levels. Moreover, we demonstrate that the differences in corporate

profit margins and net export volume of corporations explain a substantial fraction of

the cross-country variation in country-level currency betas measured against the USD.

This paper studies the implications of data technology for firm dynamics and asset prices. We develop a heterogeneous firm model in which firms optimally hire data scientists to learn about unobserved consumer preferences. Data enhances firms' demand forecasting accuracy, enabling them to charge higher markups. Firms that are constrained in expanding production capacity have stronger incentives to hire data scientists. This results in countercyclical data scientist hiring, which amplifies firms' exposure to aggregate risk via the operating leverage channel. Using a novel dataset that tracks firms' employment of data scientists, we document three key empirical findings that support the model's main mechanisms: firms with a higher share of data scientists exhibit larger markups, higher information quality, and higher stock returns.

Does bank expertise in identifying productive projects affect economic structures? Using the U.S. interstate banking deregulation of the 1980s as a quasi-natural experiment, we find that deregulation facilitated the convergence of economic structures across states. This effect is particularly pronounced among state pairs with significantly different pre-deregulation economic structures or when one state depends on external finance while the other is more financially developed. We then develop a general equilibrium model with imperfect searching and matching between banks and firms. Calibrated to U.S. data, banks’ merger and acquisition activities penetrating new markets account for approximately half of the observed convergence of economic structures between service- and manufacturing-dominated states following deregulation. While deregulation improves social welfare, banks may deviate from the socially optimal level of entry, either under- or over-penetrating markets, because of congestion externality and banks’ market power.

We study the transmission of financial knowledge through digital platforms and its effects on users’ investment in stocks and performance. Leveraging a proprietary dataset from one of China’s largest digital platforms, we measure users’ acquisition of financial knowledge through various platform information channels. Using a quasi-experimental setting to instrumentalize the acquisition, we find that the acquisition substantially increases stock market participation and enhances the sophistication and performance of investment behaviors, especially for older, inexperienced, and less wealthy households. Thus, digitalization democratizes finance by providing low-cost and scalable financial education, empowering individuals to make investment decisions aligned with portfolio theory.

This paper emphasizes the critical role of indirect carbon risk exposure transmitted through supply chain linkages in assessing carbon risk and its cross-sectional asset pricing implications. This channel has been overlooked in the burgeoning literature on the carbon risk premium (or greenium). We develop a tractable general equilibrium model that incorporates input-output linkages, carbon emissions, and climate regulatory risks. Analytically, we demonstrate that the carbon risk premium is captured by two sufficient statistics: direct and indirect carbon risk exposure, with the latter representing the network effect. Guided by the model, we empirically measure both direct and indirect exposures and demonstrate a significant cross-sectional carbon risk premium after accounting for firms' indirect carbon risk exposure. Through a decomposition, we quantify that indirect carbon risk exposure accounts for the majority of the premium, surpassing the impact of direct carbon emissions. Furthermore, we provide strong evidence that climate regulatory risks propagating through supply chains help explain the premium.

We develop an empirically tractable model of the U.S. Treasury market that incorporates investors heterogeneous demand shocks and their responses to macroeconomic factors in determining equilibrium yields. Our estimated model enables us to (i) quantify investors’ sensitivities to yields and factors, (ii) decompose yield movements by investors, and (iii) analyze investor responses during specific economic episodes. First, investor demand and factor sensitivities vary significantly across sectors and market conditions, with the overall market being quite inelastic. Second, decomposing yield drivers reveals a structural shift since the financial crisis: Before 2008, foreign investors were the primary drivers of yields, but their influence has declined markedly since then, while the Federal Reserve has stepped in as a state-contingent liquidity provider. Third, contrary to conventional wisdom, we find that foreign investors do not exhibit flight-to-safety behavior for Treasuries during market turmoil; instead, domestic investors drive the countercyclical movement of Treasury prices.

This study examines whether AI-generated reference prices improve intraday price discovery in markets with infrequent trading. Using corporate bond transactions and CP+ reference quotes from MarketAxess, we find that CP+ quotes are generally more informative about future trade prices than the most recent trade. CP+ fills information gaps between trades by incorporating signals from bond, equity, and options markets, as well as bond-specific proprietary information. Coverage extends to most bonds on nearly all trading days, including less liquid issues. CP+’s added value to price discovery follows a bell-shaped pattern with respect to bond liquidity and increases during periods of elevated market uncertainty. Around block trades and rating downgrades, CP+ for less active bonds deviates more from trade prices and converges faster to post-event values. For active bonds, CP+'s strong reliance on trade data, even during large reversals, limits its independent contribution to price discovery.

We examine the diffusion and consequences of technical errors through spillovers using the predicate list in the 510(k) program in which a new device is proved substantially equivalent to existing ones (i.e., predicates). We match a recalled device with its unrecalled “sibling” and find that the follow-on devices being equivalent to a recalled device are more likely to be recalled (and have more adverse events), especially when the former’s manufacturers are more likely to copy technical errors from the recalled device. Such diffused errors negatively influence recalled device manufacturers’ growth while providing competitors with opportunities for market shares and inventions.

Using one of the largest panel datasets on corporate culture, we examine its evolution and origin, distinguishing between the values and norms that prevail within a company and the individual preferences of corporate insiders. We find that corporate culture is remarkably stable: firms with strong (or weak) cultures tend to remain so over the following decade. Most of the variation in corporate culture is explained by firm fixed effects, attributable to a firm's initial culture established around the time of its founding. Drawing on a rich set of founder characteristics, we show that a firm’s initial culture is significantly influenced by its founder’s cultural heritage, the racial diversity of her birthplace, and the business environment at the start of her career. Finally, we provide suggestive evidence that firm-CEO matching on values, as well as the direct influence of culture on CEOs, are potential mechanisms driving the persistence of corporate culture. We conclude that a firm’s initial culture—shaped by its founders and reinforced through employee selection, attrition, and workplace norms—helps explain the enduring nature of corporate culture.

What quantity of reserves should the Fed supply to support effective monetary policy implementation and an efficient interbank payment system? To answer this question, I construct a model linking interbank intraday payment timing with monetary policy implementation. I show that a low reserve supply causes banks to delay payments to each other and strategically hoard reserves, which in turn disincentivizes banks from providing liquidity to short-term funding markets, driving up the spreads between overnight risk-free market rates and the central bank deposit rate, impeding monetary policy implementation. As reserve balances get sufficiently low, even small reductions in reserves can have large impacts on these spreads, mirroring the events observed in September 2019. Fitted to data from 2019, my model predicts the funding rate spikes of September 16-18, 2019 as an out-of-sample event. The model also provides a counterfactual analysis of the sufficient reserve level that could have prevented the September 2019 repo spike, offering insights into the current discussions about the appropriate size of the Federal Reserve’s balance sheet and quantitative tightening (QT)

This paper examines the predictability in the cross-sectional returns of corporate bonds across 112 countries and 35 currencies. With a broad set of bond- and issuer-specific return forecasting characteristics, we document significant heterogeneity in their effectiveness across different markets. Specifically, equity-linked characteristics exhibit robust and economically meaningful predictive power, highlighting imperfect integration between equity and credit markets. Our analysis further establishes the dominance of a global corporate bond market factor: alternative bond risk factors contribute little incremental explanatory power beyond this global factor, and it outperforms the domestic counterpart in multi-country settings. The above findings contrast with equity market evidence, suggesting that global bond return comovements are driven by a shared systemic risk component rather than idiosyncratic local risks.

This study investigates the impact of Paid Sick Leave (PSL) mandates on hospital healthcare quality. Leveraging the staggered adoption of PSL mandates, we find that affected hospitals experience significant improvements in healthcare quality, as evidenced by lower mortality rates and higher patient satisfaction scores. Mechanism analysis suggests that PSL improves hospital financial conditions, lowers borrowing costs, and enables greater investment in both physical infrastructure and human capital, all of which contribute to enhanced care quality. The effects are more pronounced among hospitals with lower liquidity, less external financial support, weaker profit orientation, and stronger governance. Our findings highlight the broader spillover effects of labor market policies on hospital financial performance and healthcare outcomes.

A sizable amount of U.S. federal government spending is made via procurement through full and open competition. The procuring officers have a broad range of discretion in designing the contracts and selecting winners. To evaluate whether the winner of such competition is selected based on bidders' brand, sustainability, and political ideology, this paper develops and structurally estimates an auction game. Firms with hidden efficiency types and multi-dimensional characteristics participate in an auction designed by the government. We analytically characterize the optimal mechanism design to reveal the role of competition and the role of the pecuniary and non-pecuniary factors in determining procurement outcomes. By structurally estimating the model using detailed contract-level data, we show that bidders' brand, sustainability, and political ideology play significant roles in procurement and the government is willing to pay a significant premium for these attributes, though their relative importance exhibits large heterogeneity across industries.

Chinese private portfolio equity outflows, though small compared to other Chinese out-flows, are growing rapidly because of capital account liberalization and capital flight. Using granular stock-holding data for the Qualified Domestic Institutional Investor (QDII) mutual funds, we identify a nascent financial channel of international transmission of Chinese mon-etary policy to world stocks. A difference-in-differences analysis around monetary policy announcements reveals that monetary policy tightening depresses returns of country equity indexes and individual U.S. stocks with higher QDII fund exposure relative to less exposed assets. The results are robust to controlling for the real transmission channel of Chinese mon-etary policy and other confounders. The effect is driven by smaller and less liquid firms, but not solely by China-concept stocks or those highly exposed to China’s macroeconomic shocks. Furthermore, we find that the documented results are driven by household portfolio rebalanc-ing from more to less risky assets following the announcement. We validate our empirical findings with a similar international asset return analysis in the run-up to the annual Spring Festival, when liquidity demand increases on a seasonal basis, finding consistent results.

We study the joint dynamics of stock and corporate bond returns using a structural credit risk model, where unlevered asset returns and volatility are driven by systematic risk factors. The model captures key time-series features of stock and bond volatilities, leverage, and credit spreads at the market, industry, and firm levels. It produces return forecasts that exceed Martin’s lower bound for equities and exhibit larger spikes in downturns than the average credit spreads for bonds. These forecasts significantly predict realized returns, outperforming benchmarks. We find that systematic variance risk commands a substantially larger premium in corporate bonds relative to equities.

Healthcare costs contribute to consumer medical liabilities and by extension should affect their creditworthiness and access to credit. Yet credit bureaus face significant frictions in collecting consumer medical debt liabilities data which spurred an intense ongoing policy debate. This paper sheds light on this debate by examining how information collection frictions affect the quality of the creditworthiness indicators and related consumer access to credit. Leveraging a novel driver of healthcare costs based on Medicare spending, we document that in the presence of the information collection frictions, local healthcare costs lead to upward (downward) bias in consumer credit scores (DTIs). Consumers in high-healthcare-cost CBSAs are 16.4% more likely to default than those in low-healthcare-cost CBSAs. These effects are more pronounced among consumers with low credit scores and high DTIs. Lenders internalize these information collection frictions and impose higher mortgage rejection rates in high-healthcare-cost CBSAs, particularly for riskier applicants. These effects intensify following a policy shift that removed small medical liabilities from credit reports, confirming the role of the information collection frictions. Our findings highlight that limiting the flow of medical liabilities data undermines the predictive accuracy of standard credit metrics, impairs the information value of credit bureau outputs, and leads to less efficient credit allocation.

A prevailing narrative of the rise of Bitcoin and DeFi is they provide a trustless solution to the trust-vulnerable centralized financial institutions (CeFi). The bankruptcy of the leading cryptocurrency exchange FTX severely impacted the trust of global cryptocurrency investors in centralized exchanges. Countering prevailing narrative, our research on this trust failure reveals that users did not subsequently adopt trustless decentralized alternatives. Instead, they chose seemingly more reliable centralized exchanges or quit the cryptocurrency market. We further find that users with experience or knowledge related to decentralized finance are more likely to choose decentralized finance after the event. This indicates that the reason most users do not opt for decentralized solutions is their lack of awareness or understanding of this option. This study reveals the importance of popularizing knowledge about decentralization and strengthening the regulation of centralized financial institutions.

We postulate that return predictability is an intrinsic and time-varying asset characteristic potentially related to the cross-section of expected returns, instead of just an attribute of the chosen predictors or models. We develop a tree-based clustering method to gauge heterogeneous return predictability by grouping a panel of asset returns using high-dimensional asset characteristics and market-wide predictors. Our approach tells what types of assets exhibit greater return predictability under what market conditions, and empirically reveals substantial predictability heterogeneity in the U.S. equity market. Stocks with high earnings surprises, high earnings-to-price ratios, and low trading volumes exhibit the strongest predictability; predictability diminishes sharply with low market dividend yield but peaks with high dividend yield and low market liquidity. Out-of-sample, a new anomaly linked to investors' model misspecification easily generates monthly excess alphas exceeding 1\%, and investing in highly predictable clusters significantly outperforms conventional benchmarks with Sharpe ratios approaching 2.

``Buy Now, Pay Later" (BNPL) and other forms of consumer credit create a wedge between consumption and payments. We introduce this wedge into a standard consumption-based capital asset pricing model (CCAPM). In equilibrium, the pricing kernel equals the marginal utility of consumption divided by the price of a perpetuity whose maturity equals the BNPL duration. When this duration is stochastic and comoves with market risk, the BNPL-CCAPM pricing kernel can jointly price size- and book-to-market-sorted stock portfolios as well as maturity-sorted bond portfolios.

Hiring restrictions on high-skilled foreign workers hinder financial innovation without increasing domestic high-skill employment. Using variation from the Employ American Workers Act (EAWA), which prohibited TARP banks from hiring new foreign workers until TARP repayment, we show that banks relying on foreign inventors pre-crisis experienced greater declines in patent quantity and quality, particularly in FinTech, cybersecurity, and payments. First-time foreign inventor patents dropped the most. Banks neither replaced internal innovation with external patents nor substituted foreign talent with domestic hires. Instead, affected banks retained pre-crisis foreign workers by offering higher wage premia over average domestic wages.

We systematically anatomize ESG funds’ selection, by comparing them to an “optimal portfolio” benchmark rather than average non-ESG peers. We show that ESG funds “walk the talk” by selecting portfolios with a significant 36% less absolute emission, but with (i) minimal holding deviation and thus little outperformance on other ESG or financial measures and (ii) limited impact on most firms’ ESG improvement or cost of capital: Over 90% of the emission reduction is achieved by selectively eliminating the 2% holdings comprising the top 25 highest-emitting companies; Excluding these top emitters, ESG funds fail to differ from their benchmarks. Perhaps surprisingly, ESG active funds select more by de-weighting the brownest industries, whereas ESG index funds select more by de-weighting the brownest firms within each industry. Although emission reductions are achieved without compromising risk profiles based on standard factors, ESG funds are significantly less effective as a hedge against some macroeconomic risk factors, such as energy-driven inflation.

We examine the impact of rebalancing by anomaly investors on stock prices. To do this, we introduce a simple proxy for anomaly-driven demand. Our proxy captures demand and supply arising from updates in the information set of anomaly investors. Empirically, we find that stock returns are increasing in our proxy with the effect primarily occurring at the beginning of the month. This points to a significant rebalancing effect by anomaly investors. Our findings suggest that by merely targeting the risk premia associated with different anomalies, anomaly investors impact stock prices.

This paper provides causal evidence for the impact of sales channels on insurance product adoption. Specifically, we utilize policy-level data provided by one of the largest life insurers in China, where we observe granular information on policy features and investor characteristics. We then exploit a regulatory change in the aftermath of the Global Financial Crisis that requires at least a portion of the insurance contracts sold through bank agents in each quarter to be long-term insurance products. Using a discontinuity-in-slope design, we show that bank agents falling below their target qualified ratios in the first two months of a quarter make up for the shortfall in the third month; conversely, bank agents that have exceeded their target ratios in the first two months do not alter their behavior in the last month of the quarter. This shift in qualified ratio in the last month of the quarter is entirely driven by a product-composition change -- switching from short-term life insurance to long-term annuity products. We further show that this switch is not achieved by changing the relative pricing (or features) of insurance contracts or client compositions.

We explore how labor unrest, as a shock to local social stability, prompts state-owned enterprises (SOEs) to adopt socially responsible hiring practices at the cost of productivity. The impact is more pronounced when labor unrest occurs in the industries of the exposed SOEs, local fiscal budgets are strong, governing mayors have better incentives, and local private sectors experience slower growth. While SOEs gain more fiscal benefits, these do not fully offset the associated costs. In contrast, non-SOEs do not respond to labor unrest, and their performance remains unchanged. Various tests address the selection effects of labor unrest. Macro-level results indicate that SOEs can help mitigate the negative economic and social impact of labor unrest.

A prevailing view attributes the “greenium”—the cost-of-capital gap between carbon-intensive and greener firms—to climate risks and investor preferences. We challenge this by showing that oil shocks are pivotal: rising prices, driven by sudden disruptions in global oil supply or oil-demand surge, reduce energy firms' cost of capital by enhancing their growth opportunities, creating a divergence from other brown firms. This energyspecific component explains 20% of greenium fluctuations, peaking at 50%. Reassessing events like the Paris Agreement suggests the impact of investor discipline weakens when oil’s role is considered. Overall, markets price climate risks less effectively than assumed.

Belief skewness---the asymmetry in investors' cash-flow growth rate expectations---has a negative impact on the stock mean return, controlling for the average bias in beliefs and belief dispersion. When investors are sufficiently optimistic on average, however, the relationship reverses. Belief skewness also has a positive impact on the stock price and a negative impact on the stock volatility. To show this, we first develop a continuous-time general equilibrium model with heterogeneous investors having skewed beliefs. We then use analyst forecast data to construct belief skewness proxies and verify the model implications for the aggregate market returns empirically.

This paper highlights leasing as a key corporate risk management mechanism

for hedging capital valuation risks, extending beyond its traditional financing role.

Financially constrained firms often face a trade-off between financing and hedging

due to collateral competition, a challenge known as the ”corporate risk management

paradox” (Rampini and Viswanathan, 2010, 2013). Leasing contracts, where the lessor

serves as both creditor and insurance provider, offer a more collateral-efficient hedging

solution—an aspect previously overlooked in the literature. We develop a dynamic

agency-based model to explore leasing’s dual role in financing and hedging. Using

the staggered implementation of U.S. anti-recharacterization laws as a quasi-natural

experiment, our empirical findings show that firms with greater capital value volatility—

and thus stronger hedging needs—are more likely to lease, even when financing

conditions improve. This evidence strongly supports our theoretical framework.

This paper studies the effects of Artificial Intelligence (AI) innovation on firm-level employment dynamics and corporate valuation. We apply large language models (LLMs) and generative AI to U.S. patent data during 2007-2023 to identify AI-related innovations in seven key functional areas. Using microdata on individual worker skill sets and job transitions between occupations and firms, we find that AI innovations related to engagement, learning, or creativity augment human labor, while those related to perception or motor control displace it. Although both augmenting AI innovations and displacing AI innovations significantly increase firm value, they appear to do so through different channels. In particular, augmenting innovations raise firm-level productivity, whereas displacing innovations improve cost efficiency. We also find that firms obtain more value from their augmenting (displacing) innovations when access to external hires is better (potential cost savings from workforce reduction are larger). Overall, our findings suggest that AI innovation can bring significant value gains to innovating firms, but the extent of those gains depends critically on what frictions are present in external labor markets.

This paper investigates the causal effect of patent examiners’ climate change beliefs on their green patent review behavior and green innovation outcomes. Using granular data on U.S. patent examiners across geographic locations from 2012–2020, we leverage climatic natural disasters as plausibly exogenous shocks to examiners’ climate change beliefs. We find that examiners grant significantly more green patents following local climatic disasters, with these patents showing significantly higher quality. Further analysis suggests that the effect stems from increased effort by disaster-exposed examiners during green patent reviews. These higher-quality green patents subsequently lead to greater reductions in corporate carbon emissions. We find no comparable effects for either non-green patents or non-climatic natural disasters, highlighting that examiners’ climate change beliefs exclusively influence green innovation outcomes.

This study exploits the non-fungible token (NFT) setting to estimate the value of non-pecuniary benefits, a long-standing empirical challenge in private-value markets such as art, antiques, and other collectibles. After developing and validating our emotional dividend proxy (LOVE), we apply deep learning algorithms and discover that contemporaneous price fluctuations, certain collection features, and ownership wealth significantly contribute to its formation. Leveraging these parameters, we employ AI-powered models to estimate NFT prices with high accuracy, but find their predictive ability is decreasing in LOVE. Additionally, we demonstrate that LOVE-driven trading results in long-term financial losses for the average investor, highlighting a trade-off between wealth and emotional utility. Our study provides novel economic insights into the factors shaping emotional dividends and their role in the pricing of private-value assets. It also surfaces the limitations AI faces in emotionally charged markets, revealing new challenges for algorithmic trading when assets carry significant emotional utility.

We find that the sensitivity of CEO pay to firm size (pay-size sensitivity) drops by 60% after the first-time disclosure of a relatively higher CEO-worker pay ratio under the 2017 Pay Ratio Disclosure Mandate. The sensitivity of CEO “flow” pay to positive performance (“upside” pay-performance sensitivity) also declines by 86%, while downside pay-performance sensitivity remains unchanged. These results are consistent with the notion that greater public scrutiny of CEO compensation in high pay-ratio firms curbs CEO pay growth. We further show that the change in pay sensitivities is associated with a shift in the type of M&A deals firms pursue, as well as the market reaction to deal announcement. Specifically, firms engage in fewer large deals but more small deals, with large deals exhibiting higher quality but small deals showing lower quality. These results suggest that the weaker link between CEO pay and firm size incentivizes CEOs to switch screening effort from small deals to large deals, as they can no longer derive as much additional pay from undertaking large-scale but potentially value-destroying deals. Our results provide novel evidence on how arguably exogenous changes in the drivers of CEO compensation affect CEO decisions and firm outcomes. We provide a simple model showing that while the magnitude of pay-size sensitivity affects the allocation of screening effort between large and small deals, the magnitude of “upside” pay-performance sensitivity is irrelevant.

We construct detailed ex ante firm-level exposures to government spending by linking federal procurement data to appropriations bills, which grant agencies the budget authority to procure goods and services. We find that government spending is large, persistent, and disproportionately allocated to a select number of firms within industries. These firms earn large abnormal returns and steadily gain market share, with these effects concentrated in a few trading days coinciding with contract award announcements. Reflecting their growing market share, firms that repeatedly receive government contracts experience rising profitability and increasing markups, creating rising industry stars.

This paper relates jumps in high frequency stock prices to firm-level, industry and macroeconomic news, in the form of machine-readable releases from Thomson Reuters News Analytics. Most relevant news, both idiosyncratic and systematic, leads quickly to price jumps, as market efficiency suggests they should. However, in the reverse direction, we find that the vast majority of intraday price jumps do not have identifiable public news that can explain them, in a departure from the ideal of a fair, orderly and efficient market. We show that jumps without news do not substantially correlate with observable proxies for asymmetric or private information, and that microstructure-driven variables have some limited power to help predict the occurrence of jumps without news.

In this paper, we trace how crypto risk from the crypto ecosystem transmits systemic risk to the financial system. First, we measure crypto risk and crypto-driven systemic risk at the financial firm level. Using these metrics, we show that firm-level crypto risk has been rising significantly in recent years, thus leading to greater systemic risk exposure and transmission through two different channels. Specifically, crypto risk affects systemic risk exposure through both blockchain technology and asset class channels but elevates systemic risk transmission mainly via the blockchain technology channel. Notably, financial firms’ management tends to neglect the transmission of risk through the blockchain technology channel. Our findings highlight the integration of two parallel systems and underscore the importance of a global regulatory framework.

Exploiting plausibly exogenous variation in litigation information disclosure generated by mandated online publication of court decisions in China, we examine the effect of judicial transparency on entrepreneurship. We show that enhanced judicial transparency promotes entry into entrepreneurship. The effect is more pronounced in industries that are historically more prone to litigation, especially those more inclined to be plaintiffs. Increased access to credit finance, reshaped beliefs about the quality of institutions and enhanced technology-driven legal services are three plausible underlying channels through which judicial transparency reduces legal uncertainty and promotes entrepreneurship. Judicial transparency improves financial, operating, and innovation performance of entrepreneurial firms. Overall, our findings highlight the importance of a transparent legal system in shaping entrepreneurial ecosystems via its nuanced effects on uncertainty reduction.

This paper investigates how judicial independence reduces expropriation risk in government contracts. I exploit a staggered reform in China that curtailed local governments’ influence over court adjudication. Using a purpose-built dataset covering firm-government contract disputes in local courts, I first show that firms’ win rates against local governments increased following the reform, with the probability of firm bankruptcy after court rulings declining. I then document that firms with a greater share of government contracts tied to reformed jurisdictions experienced larger reductions in credit spreads. The effects are significant for private firms but absent for state-owned enterprises. These firms also improved receivables liquidity and obtained more debt financing. Stock prices reacted more positively to procurement award announcements after the reform. The findings suggest that judicial independence reduces financial frictions for private suppliers involved in the provision of public goods.

Are there risk factors that are pervasive across major classes of corporate securities:

stocks, bonds, and options? We employ a novel econometric procedure that

relies on asset characteristics to estimate a conditional latent factor model. A common

risk factor structure prominently emerges across asset classes. Several common

factors explain a substantial amount of time-series variation of individual asset returns

across all three asset classes, and have sizable Sharpe ratios. Several of our factors

are highly correlated with some of asset-class-specific factors as well as macroeconomic

and financial variables. While a small set of common factors does not fully capture

the cross-section of average returns, imposing the factor structure is useful in practice,

especially in out-of-sample analysis. A mean-variance efficient portfolio that utilizes

asset characteristics achieves a high Sharpe ratio as different asset classes hedge each

other’s exposures to the common factors

This study investigates whether and when investors can distinguish genuine from superficial signals of innovation in blockchain startups’ open-source disclosures during fundraising. We introduce the concept of code-washing—the superficial use of source code repositories to mimic authentic development—as a novel mechanism of information manipulation in entrepreneurial finance. Using a global dataset linking GitHub activity to detailed fundraising outcomes, we classify startups as code-producers or code-washers based on the depth and timing of development activity. We estimate treatment effects on fundraising success, controlling for extensive startup characteristics and market conditions, and validate our proxies through tests of commit timing, external developer engagement, and responses to the 2017 SEC crackdown on unregistered token offerings. Our main analysis stratifies startups by investor attention—proxied by Ether market returns—and information richness, testing whether markets shift from pooling to separating equilibria as scrutiny increases. Consistent with signaling theory, we find that code-producers outperform only in periods of low investor optimism or high information availability, while code-washers’ fundraising advantage disappears. Post-fundraising, code-producers sustain innovation and deliver significantly higher returns, whereas code-washers experience declines in code activity and negative performance. These results highlight how information frictions enable superficial transparency to attract capital during hot markets, but only authentic innovation sustains long-run value, extending theories of signaling and market learning in entrepreneurial finance.

Managerial incentives are substantially related to a firm's market value, so how information is revealed to the market affects managerial behavior. We analyze a model in which the manager needs to exert costly effort to implement a risky, long-term project and the project may generate verifiable information revealing its value. The optimal disclosure rule to motivate managerial effort is the manager's strategic disclosure because it protects the manager from the downside of the project and induces the rational market to punish nondisclosure. A more transparent information regime is not always preferred because it may reduce the manager's discretion over disclosure. We also derive the optimal disclosure when both effort stimulation and project selection are considered.

We study government investment across US states and counties in a Q-theoretical framework. We develop novel measures of regional government Qs, derived from municipal bonds, and private sector Qs, derived from a decomposition of corporate Qs. The main findings are that government investment is positively correlated with government Qs and negatively correlated with private sector Qs. We explain these findings with a model in which governments invest to maximize social welfare, considering their impact on private investment. Accordingly, we find stronger effects in regions with large subsidy programs, low levels of corruption, and following adverse employment shocks. Overall, the findings have two key implications: (1) Government investment crowds-in private sector activity, and (2) Governments leverage information from capital markets to inform government investment decisions.

We introduce a simple and tractable methodology for estimating semiparametric

conditional latent factor models. Our approach disentangles the roles of characteristics

in capturing factor betas of asset returns from “alpha.” We construct factors

by extracting principal components from Fama-MacBeth managed portfolios. Applying

this methodology to the cross-section of U.S. individual stock returns, we

find compelling evidence of substantial nonzero pricing errors, even though our factors

demonstrate superior performance in standard asset pricing tests. Unexplained

“arbitrage” portfolios earn high Sharpe ratios, which decline over time. Combining

factors with these orthogonal portfolios produces out-of-sample Sharpe ratios

exceeding 4.

Using a unique data set, we provide the first comprehensive evidence of trading before successful cyberattacks. Although only hackers should expect an imminent breach, short selling in victim firms intensifies a few weeks earlier—particularly when shares are widely lendable—whereas insider and institutional trades remain flat. Retail investors, ostensibly the least informed, likewise presciently divest/short soon-to-be-attacked stocks, coinciding with spikes in “<company-name>+hacking” Google queries and rising trading/short costs. Post-cyberattack, victims earn negative returns—implying a wealth transfer far exceeding widely-publicized ransom demands. Therefore, cyberattacks—the tip of iceberg of outsider-generated information—undermine the traditional paradigm of insider-centric information asymmetry.

The likelihood of a patent application being approved by the USPTO is significantly lower for female inventors than for male inventors, even within narrow technological groups. We investigate the reasons for this gap, focusing on the role of gender bias, and analyze the implications for the quality and value of the granted patents. We find that the gender gap in examiners’ first-action approval decisions declines significantly and becomes close to zero when inventor names are rare and thus more gender-blind. Additionally, granted patents authored by female inventors are associated with higher estimated market values, but this difference diminishes for the gender-blind group—a pattern also reflected in forward-citations. Consistent with the effects of statistical discrimination, the gender gap in approval rates is smaller for more experienced inventors and in technological art units with higher female representation. We estimate that more than half of the residual gender gap in the patent grant rates can be attributed to gender bias. Our findings shed light on the causes of gender differences in patent examination outcomes and offer implications for policies aimed at creating a level playing field in patenting.

We surveyed all Chinese public firms in 2019 and 2022 to examine the real effects of financial markets. More than 90% of firms monitor the stock market for learning new information about real investment and for accessing external financing, suggesting the wide existence of real effects through a learning channel and a financing channel. Firms learn about information on dimensions on which aggregation of external information is valuable. To validate our survey approach, we conduct analysis to link firms’ survey responses to their characteristics and actions. Finally, we provide additional validation by exploring the setting of trading suspensions.

We investigate whether managerial rent extraction plays a role in the increasing manager-worker pay disparities in public firms. Utilizing granular individual-level compensation data from the U.S. Census Bureau, we find that managers experience substantially higher pay growth than rank-and-file workers during our sample period, even after accounting for worker composition changes. While pay growth differences align with market movements — as suggested by models like Gabaix and Landier (2008) — we also uncover evidence in support of managerial rent extraction. A rent extraction model predicts that pay growth disparities are asymmetrically more sensitive to positive idiosyncratic stock returns than to negative ones, and that this asymmetry is absent for returns driven by observable industry or market factors. These predictions are confirmed empirically. Additionally, we demonstrate that the asymmetry in pay growth disparities increases following exogenous reductions in corporate governance and is more pronounced in firms with less external monitoring by analysts or unions. Overall, our results suggest that rent extraction is one of the factors that contributed to the rising CEO-worker pay ratio.

Regulators manage conflicts of interest within banking conglomerates by enforcing China Walls—internal information barriers around dealers—and to evaluate their effectiveness, this study maps information sharing between dealers and funds using Israeli Shekel foreign exchange trades, comparing trading activities of affiliates against unrelated firms around exceptionally large trades to detect information sharing. The research documents islands of informational autarky between dealers and their affiliate funds surrounded by a sea of information sharing, finding that dealers never trade nor share information with their affiliated funds, dealers consistently share information with their client funds including on non-trading days, and affiliated funds intensely share information among themselves since they are free to do so. Back-of-the-envelope calculations suggest that establishing China Walls between affiliated funds would eliminate $16.1 billion in trades, comprising 37% of their trades on event dates, with these results holding during both crisis and noncrisis periods across granular cells of firm and asset characteristics, ultimately revealing remarkable regulatory capacity to control information flows within financial conglomerates.

We develop a growth model that endogenizes occupational choice of workers with different ability levels to study how digitalization impacts labor income inequality. Our analysis incorporates digitalization's multifaceted effects on the real economy: (i) it influences economic growth through automation, and (ii) affects labor income inequality through two channels---labor productivity (demand side) and labor time modulation (supply side). We examine how digitalization, operating through the labor demand and supply channels, impacts wage inequality (i) between skilled and unskilled workers, (ii) within each skill group, and (iii) economy-wide. Our analysis reveals that digitalization's impact on labor income inequality---whether exacerbating or mitigating disparities---depends on the inequality metric employed.