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 construct two types of trading shocks and examine their effects on stock prices. Common shocks capture the shared trading activity across funds, whereas granular idiosyncratic shocks place emphasis on large players. Common shocks related to stock sales exhibit a significantly stronger price impact than those related to purchases, in contrast to symmetric effects of purchases and sales for granular idiosyncratic shocks. The initial price impact persists in the short run and partially reverses after six months, suggesting underreaction to institutional trading. Our results underscore the impact of the common component across various funds on asset prices and market efficiency.