Knowledge that Transforms

Dark pools can restrict access for specific trader types. We compare execution outcomes between dark pools that restrict high frequency trader access and those that do not. We find that trades executed in dark pools with more access restrictions have less order flow information leakage, adverse selection risk and post-trade order imbalances than trades in less restricted pools. Evidence from exogenous dark pool closures demonstrates that these differences are causal. The ability to segment order flow can benefit investors because it allows them to make trade-offs between execution risk and information leakage across different dark venues.

Financial benchmarks such as LIBOR underpin the pricing of trillions of dollars of contracts around the world. We evaluate the quality of benchmark prices using a state-space model to separate information from noise. Applying the method to LIBOR benchmarks and their replacements, we find that alternative reference rates (ARRs) are less noisy in four of the five currencies. However, the USD ARR is considerably more noisy, resulting in billions of dollars of noise-related wealth transfers between contract counterparties. We show that benchmark reforms such as expanding the reference market and using a trimmed mean can reduce noise in ARRs.

We document that the reallocation of central bank reserves towards banks with higher liquidity needs fosters credit supply. Exploiting the ECB's tiered reserve remuneration system for identification, we show that this system encouraged banks with low reserve holdings to obtain more reserves through the money market. Concomitantly, these banks reduced their securities holdings and extended more credit. We estimate that the reallocation of one euro of reserves towards banks with ex ante low reserve holdings resulted in an increase in credit supply of about 15 cents.

The transition from London Interbank Offered Rate (LIBOR) to Secured Overnight Financing Rate (SOFR) affects the reference rate of floating-rate debt worth trillions of dollars. We provide the first evidence highlighting a benefit of the benchmark transition for debt markets. Focusing on the market for dollar-denominated floating rate notes (FRNs), we compare the yield spreads of FRNs linked to LIBOR and SOFR, issued by the same entity during the same month. After adjusting for the maturity-matched spreads from derivatives markets, we find significantly lower spreads for SOFR-linked FRNs. We link this SOFR discount to the enhanced price stability of SOFR-linked FRNs.

Do banks extending government-guaranteed loans simultaneously reduce their risk exposure to firms? Using unique euro-area credit register data and the COVID-19 guarantee programs as a laboratory, we find that 1 euro of guaranteed lending was associated with a reduction of 28 cents in non-guaranteed credit, relative to other banks lending to the same firm. Substitution was highest for riskier and smaller firms in more affected sectors and for stronger banks. Nevertheless, banks offered cheaper credit and longer maturities to guaranteed loan recipients, especially more fragile ones. This improvement in lending terms is the flipside of credit substitution.

We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns , generalizing high-dimensional sorting with economic guidance and interpretability. Under the mean–variance efficient framework, P-Trees construct test assets that significantly advance the efficient frontier compared to commonly used test assets, with alphas unexplained by benchmark pricing models. P-Tree tangency portfolios also constitute traded factors, recovering the pricing kernel and outperforming popular observable and latent factor models for investments and cross-sectional pricing. Finally, P-Trees capture the complexity of asset returns with sparsity, achieving out-of-sample Sharpe ratios close to those attained only by over-parameterized large models.

Our paper analyzes whether a planner should design a taxonomy for sustainable investment products when conventional tools for environmental regulation can also be used to address externalities arising from firm production. We first show that the private market provision of ESG funds marketed to retail investors involves greenwashing , so that a mandatory taxonomy is necessary to generate real effects of sustainable finance . However, the introduction of such a taxonomy can only improve welfare, on top of optimally chosen environmental regulation, if financial frictions constrain socially valuable economic activity. Otherwise, environmental policy alone is sufficient to optimally address externalities.

This paper analyzes the reputational effects of forced CEO turnovers on outside directors. We find that directors interlocked to a forced CEO turnover experience large and persistent increases in withheld votes at subsequent re-elections relative to non-turnover-interlocked directors. Directors are not penalized for an involvement in a turnover per se but for forced CEO turnovers that are related to governance failures by the board. Our results challenge the widespread view that forcing out a CEO can generally be understood as a sign of a well-functioning corporate governance.

Short sellers are widely known to be informed, which would typically suggest that they demand liquidity. We obtain comprehensive transaction-level data to decompose daily short volume into liquidity-demanding and liquidity-supplying components. Contrary to conventional wisdom, we show that the most informed short sellers are actually liquidity suppliers, not liquidity demanders. They are particularly informative about future returns on news days and trade on prominent cross-sectional return anomalies. Our analysis suggests that market making and opportunistic risk-bearing are unlikely to explain these findings. Instead, our results align with recent market microstructure theory, pointing to strategic liquidity provision by informed traders.

This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in credit markets. The empirical setting exploits the outbreak of the Wells Fargo scandal as a negative shock to borrowers’ trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political affiliation to proxy for the magnitude of trust erosion in banks in a triple-differences specification, I find that, conditional on the same exposure to the scandal, a county experiencing a greater erosion of trust has a larger increase in FinTech share relative to a county experiencing less of an erosion of trust. Estimating treatment effect heterogeneity using generic machine learning inference suggests that borrowers with the greatest decrease in trust in banks and the greatest increase in FinTech adoption have similar characteristics.