ABSTRACT I assemble and examine an annual time series of bid–ask spreads on Dow Jones stocks from 1900 to 2000 and average commission rates for trading NYSE stocks since 1925. Spreads are cyclical, especially during periods of market turmoil. The sum of half‐spreads and one‐way commissions, multiplied by annual turnover, estimates the aggregate cost of trading and drives a wedge between gross and net equity returns. This difference explains only a small part of the observed equity premium, though I show that the gross equity premium is perhaps 1% lower today than it was early in the 1900's. Finally, I find that the transaction cost measures (spreads and turnover), which serve as a proxy for liquidity, predict stock returns 1 year or more ahead and dominate traditional measures such as the dividend yield. The evidence suggests aggregate liquidity is a determinant of expected stock market returns.
ABSTRACT Predictive regressions are subject to small‐sample bias when innovations in persistent predictors are correlated with predictive errors. One approach to bias reduction is the multi‐predictor augmented regression method (mARM), which augments the predictive regression with bias‐corrected proxies for predictor innovations. We extend this framework to allow for first‐order autocorrelation in predictive errors, first‐order autocorrelation in predictor innovations, and contemporaneous correlations between shocks to the two processes. Simulations show that the mARM estimator can exhibit substantial bias, whereas our proposed extended mARM estimator significantly reduces bias relative to both mARM and ordinary least squares. Looking at predictive regressions of stock returns on several widely used financial ratios, we find that the mARM approach yields spurious statistical significance for some predictive coefficients, which is not observed with the proposed extended mARM.
ABSTRACT A widely cited working paper from the 2000s, now published (Jones, 2026), documents a substantial decline in trading costs and a positive relation between bid–ask spreads and excess returns from 1900 to 2000. We update the analysis and find that over the next quarter century, turnover surges, spreads decline sharply and subsequently cluster near zero, and excess returns become negatively related to spreads. Additional analysis attributes the reversal of the excess returns relation to distortions in the time‐series analysis created by the sudden reduction and clustering of spreads. We find similar distortions using the Amihud illiquidity measure and in simple cross‐sectional analyses, which suggest the effects we highlight might be pervasive.
ABSTRACT We quantitatively analyze the impact of entrepreneurial (EN) optimism on venture capital (VC) projects in a dynamic calibratable model. VCs and entrepreneurs (ENs) have differing beliefs and risk attitudes about projects. The equilibrium features staged VC investments, progressive vesting of the EN's stake, and intertemporal performance targets. Optimism is a major driver of VC projects. The EN's mean project quality assessment is over twice the VC's mean assessment. Optimism increases pay‐performance sensitivities by 75% and almost doubles project values. We derive quantitative implications for the effects of optimism, project qualities, productivities, and risks on VC contracts and project values.
We investigate whether startups respond differently to male and female investors during outbound origination, a setting where female investors often report being marginalized by male founders. Using a randomized field experiment, we examine gender differences in startups' responses to investor outreach. Leveraging the rapid increase in outbound origination, our field experiment involves 40,572 startups sending identical unsolicited emails from fictitious male and female investors. Startups are substantially more likely to read emails from male investors, visit their websites, and attempt to make contact. This pattern is stronger for angel investors than for VC investors. Disclosing professional certifications increases a male investor's email's likelihood of being read but reduces the corresponding female investor's chances. A second experiment with 4836 recently incorporated startups reveals similar gender-based choices. The findings provide large-scale field evidence suggesting that gender-based frictions in entrepreneurial finance may extend beyond founders' access to capital to investors' access to deal flow.
