Knowledge that Transforms

Tick sizes affect market quality through a tradeoff between pricing fidelity and undercutting. The U.S. Tick Size Pilot (TSP), which raised the minimum tick from 1¢ to 5¢, provides a natural experiment to study this tradeoff. We find that the TSP harmed liquidity for stocks with spreads below 10¢ but improved liquidity for stocks with spreads above 15¢. These opposing effects explain the mixed results across prior studies which pool together stocks with very different prevailing spreads. We recommend researchers using the TSP for causal inference should, at minimum, split samples at 10¢-spreads to account for these heterogeneous liquidity effects.

High-frequency traders (HFTs) mainly operate on public exchanges. Since the European regulatory changes in 2018 (Markets in Financial Instruments Directive II), some HFTs began operating Systematic Internalizers (SIs), i.e., single-dealer platforms where clients trade against the dealer's inventory. Using Swedish equity market data, we show that higher HFT dealer-platform activity reduces displayed exchange liquidity: quoted spreads widen and depth falls. Effective spreads are largely unchanged for HFTs but increase for non-HFT traders. Price efficiency improves as return autocorrelations and excess variance ratios move closer to random-walk benchmarks. Evidence suggests HFT dealers' inventory management is the main channel behind these results.

We examine whether real-time business news predicts firm bankruptcy. Using full-text daily articles from the Dow Jones Newswires database, we generate firm-level predictors with ChatGPT and benchmark against FinBERT and dictionary-based models. ChatGPT-based variables outperform alternatives, with sentiment scores showing predictive power across horizons. Full-text news significantly enhance predictive accuracy over headlines. News-based measures add explanatory power beyond financial variables. Finally, we show that news captures timely information on macroeconomic conditions relevant to bankruptcy prediction, such as VIX, real GDP growth, and recession probability.

We study the impact of large, successful manufacturing plants on other local producers in China, focusing on “Million-Rouble Plants” built in the 1950s during a brief alliance with the U.S.S.R. The ephemeral geopolitical situation and the locations of allied and enemy airbases provide exogenous variation in plant siting. We find a boom-and-bust pattern: Counties hosting these plants were 80% more productive than control counties in 1982 but 20% less productive by 2010. This decline reflects the performance of local establishments, which exhibit low productivity, limited innovation, and high markup. Specialization hindered spillovers, preventing the emergence of new clusters and local entrepreneurship.

Using a unique dataset of Chinese households, we document a robust negative causal effect of past house price growth on local entrepreneurial activity. This finding stands in sharp contrast to a positive effect typically documented in developed countries. We find that strong past house price growth fosters extrapolative housing-market optimism and stimulates greater housing investment. Consistent with crowding out, housing optimism — and the ensuing surge in housing investment — redirects resources away from entrepreneurship. This belief-based channel highlights an important mechanism through which housing booms may dampen real economic activity in emerging markets.

We empirically investigate the impact of financial analysts' integrity on their information outputs and career success. Using analysts' off‐the‐job behavior, specifically their legal records, to proxy for analyst integrity, we predict and find that weak‐integrity analysts engage more in opportunistic behaviors, including “speaking in two tongues” and earnings forecast walk‐down. These analysts obtain favorable management access and make more accurate earnings forecasts. Further analyses indicate that while the market as a whole does not distinguish weak‐integrity analysts from others, sophisticated investors discount their information outputs. Weak‐integrity analysts also experience less favorable career outcomes. Our results have important implications for investors, professional bodies, employers, and regulators.

With the widespread use of narrative feedback in companies, understanding how such feedback can be valuable for employee performance improvement is important. Drawing on proprietary data from an e‐commerce company, we investigate the role of specificity and causality—two key language characteristics for self‐regulation and learning. Our findings suggest that neither specificity nor causality is always beneficial; instead, their effects depend on whether the feedback refers to strengths or weaknesses. Specifically, employees are more likely to improve when they receive more specific narrative feedback on their strengths, consistent with employees engaging in more systematic exploration when feedback provides concrete references to desirable behaviors. In contrast, we find that increases in the specificity of narrative feedback on weaknesses can have negative performance consequences, as employees who are confronted with many specific examples of undesirable behaviors may attempt too many behavioral changes at once, undermining learning and improvement. Furthermore, employees are more likely to improve when feedback on their weaknesses uses more causal language, suggesting that explanations of why certain behaviors were ineffective help employees understand and correct those behaviors. Our study informs HR leaders, supervisors, and experts responsible for designing management control systems by showing that narrative feedback should be specific when describing strengths, but more selective and richer in causal explanations when addressing weaknesses.