Journal of Financial and Quantitative Analysis最新文献

This article presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long–short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep-learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors (hidden layers). Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.

Post-earnings-announcement drift (PEAD) is one of the most solidly documented asset pricing anomalies. We use the controlled conditions of the experimental lab to investigate whether earnings autocorrelation is the driving cause of this anomaly. We observe PEAD in settings with uncorrelated and correlated earnings surprises, confirming that earnings autocorrelation is not a necessary condition for PEAD. Instead, it acts as an accelerator: PEAD is stronger when earnings surprises are correlated. We further show that market prices underadjust to fundamental value changes, and that trading strategies can profitably exploit the PEAD.

Using a novel data set of over 3,500 public and private firms, we construct the network of executive and director connections prior to the 1929 financial market crash. We find that more connected firms have 17% higher 10-year survival rates. Consistent with a working capital channel, the results are strongest for small, private, cash-poor firms, and firms located in counties with high bank suspension rates. Moreover, connections to cash-rich firms that increase accounts receivable matter the most. Our results suggest that network connections can play a stabilizing role during a financial crisis by easing the flow of capital to constrained firms.

A eurozone exit or breakup exposes bondholders to currency redenomination risk. I quantify redenomination risk since the sovereign debt crisis: It contributes substantially to credit spreads around changes in government in France and Italy. Bond prices suggest that markets have priced a potential Italian exit as isolated, and a French one as a breakup. Unlike conventional default risk, redenomination risk can be negative depending on the strength of the national “shadow” currency. Countries with strong shadow currencies earn breakup-insurance premia from the eurozone analog of “exorbitant privilege.” Yield effects are quantitatively large for implied exit probabilities as low as 1%.

Using random H-1B visa lotteries as a natural experiment, we find that firms respond to shortages of high-skilled workers by acquiring firms that employ such workers. The effect is stronger among firms with high human capital and more senior workforces, firms facing tight labor markets and legal barriers to poaching workers, and firms lacking foreign affiliates. The acquired workers are highly educated, sharing skills and occupations similar to those of the acquirer’s existing workers. Our findings suggest skilled labor is an important driver of acquisitions and acquiring is an effective means of obtaining skilled labor.

We use New York City (NYC) taxi data to identify trips between mutual fund offices and local firm headquarters. NYC funds overweight the stocks of local firms they visit via taxi, and firm visits are associated with superior investment performance. Firm visits are elevated prior to earnings announcements, and mutual fund trades that are associated with firm taxi visits predict earnings surprises. The results are generally stronger when fund and firm executives share educational connections. Additional tests support the conclusion that funds’ local bias and investment performance are driven by portfolio managers’ efforts and ability to actively gather material information.