Journal of Finance最新文献

Using contract-level data for the Canadian mortgage market, this paper provides evidence of an “invest-and-harvest” pricing pattern. We build a dynamic model of price negotiation with search and switching frictions to capture key market features. We estimate the model and use it to investigate the effects of market frictions and the resulting dynamic competition on borrowers' and banks' payoffs. We show that dynamic pricing and the presence of search and switching costs have important implications for public policies.

This address reviews macrofinance from the perspective of resilience. It argues for a shift in mindset, away from risk management toward resilience management. It proposes a new resilience measure, and contrasts micro- and macro-resilience. It also classifies macrofinance models in first- (log-linearized) and second-generation models, and links the important themes of macrofinance to resilience.

Moreira and Muir question the existence of a strong risk-return trade-off by showing that investors can improve performance by reducing exposure to risk factors when their volatility is high. However, Cederburg et al. show that these strategies fail out-of-sample, and Barroso and Detzel show they do not survive transaction costs. We propose a conditional multifactor portfolio that outperforms its unconditional counterpart even out-of-sample and net of costs. Moreover, we show that factor risk prices generally decrease with market volatility. Our results demonstrate that the breakdown of the risk-return trade-off is more puzzling than previously thought.

We study theoretically how the proliferation of new data (“data abundance”) affects the allocation of capital between quantitative and nonquantitative asset managers (“data miners” and “experts”), their performance, and price informativeness. Data miners search for predictors of asset payoffs and select those with a sufficiently high precision. Data abundance raises the precision of the best predictors, but it can induce data miners to search less intensively for high-precision signals. In this case, their performance becomes more dispersed and they receive less capital. Nevertheless, data abundance always raises price informativeness and can therefore reduce asset managers' average performance.

Although algorithmic trading now dominates financial markets, some exchanges continue to use human floor traders. On March 23, 2020 the NYSE suspended floor trading because of COVID-19. Using a difference-in-differences analysis around the closure of the floor, we find that floor traders are important contributors to market quality. The suspension of floor trading leads to higher spreads and larger pricing errors for treated stocks relative to control stocks. To explore the mechanism, we exploit two partial floor reopenings that have different characteristics. Our finding suggests that in-person human interaction facilitates the transfer of valuable information that algorithms lack.

We use a large cross section of equity returns to estimate a rich affine model of equity prices, dividends, returns, and their dynamics. Our model prices dividend strips of the market and equity portfolios without using strips data in the estimation. Yet model-implied equity yields closely match yields on traded strips. Our model extends equity term-structure data over time (to the 1970s) and across maturities, and generates term structures for various equity portfolios. The novel cross section of term structures from our model covers 45 years and includes several recessions, providing a novel set of empirical moments to discipline asset pricing models.