A definition of market adjustment is proposed in terms of the time it takes market attributes to reflect new information. Properties of the proposed definition are discussed. In order to operationalize the concept, a statistical method is introduced to estimate the adjustment times. Empirical examples are used to illustrate the proposed method. Some possible economic interpretations are given. The properties of the estimator are also investigated by simulation and analytical methods.

Decentralized exchanges (DEXs) allow traders to express their willingness to pay for quick execution through a public priority fee bidding mechanism. We provide evidence that high-fee DEX trades are more informative and contribute more to price discovery. Using address-level blockchain transaction data, we show that informed traders persistently bid higher fees to secure early execution, revealing a strong willingness to pay for execution priority. Further, analysis of Ethereum mempool data demonstrates that informed traders employ a “jump bidding” strategy, placing high initial bids to deter potential competitors.

The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real-time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. We also find that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting, and option pricing.

[A new methodology is proposed to estimate theoretical prices of financial contingent claims whose values are dependent on some other underlying financial assets. In the literature, the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. This paper proposes a simulation-based method. When it is used in connection with ML, it can improve the finite-sample performance of the ML estimator while maintaining its good asymptotic properties. The method is implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond and bond option pricing model. It is especially favored when the bias in ML is large due to strong persistence in the data or strong nonlinearity in pricing functions. Monte Carlo studies show that the proposed procedures achieve bias reductions over ML estimation in pricing contingent claims when ML is biased. The bias reductions are sometimes accompanied by reductions in variance. Empirical applications to U. S. Treasury bills highlight the differences between the bond prices implied by the simulation-based approach and those delivered by ML. Some consequences for the statistical testing of contingent-claim pricing models are discussed.]

Prices of interest rate derivative securities depend crucially on the mean reversion parameters of the underlying diffusions. These parameters are subject to estimation bias when standard methods are used. The estimation bias can be substantial even in very large samples and much more serious than the discretization bias, and it translates into a bias in pricing bond options and other derivative securities that is important in practical work. This article proposes a very general and computationally inexpensive method of bias reduction that is based on Quenouille's (1956; Biometrika, 43, 353-360) jackknife. We show how the method can be applied directly to the options price itself as well as the coefficients in the models. We investigate its performance in a Monte Carlo study. Empirical applications to U.S. dollar swap rates highlight the differences between bond and option prices implied by the jackknife procedure and those implied by the standard approach. These differences are large and suggest that bias reduction in pricing options is important in practical applications.

Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment׳s observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu, and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor sentiment. The key is consistency across anomalies. Obtaining just the predicted signs for the regression coefficients across the 11 anomalies examined in the above study occurs only once for every 43 simulated regressors.

This study explores the role of investor sentiment in a broad set of anomalies in cross-sectional stock returns. We consider a setting in which the presence of market-wide sentiment is combined with the argument that overpricing should be more prevalent than underpricing, due to short-sale impediments. Long-short strategies that exploit the anomalies exhibit profits consistent with this setting. First, each anomaly is stronger (its long-short strategy is more profitable) following high levels of sentiment. Second, the short leg of each strategy is more profitable following high sentiment. Finally, sentiment exhibits no relation to returns on the long legs of the strategies.

Formal finance involves the costly acquisition of information about distant entrepreneurs, while relationship-based finance allows financiers to fund a narrow circle of close entrepreneurs without acquiring costly information. In developing economies with low capital endowments, relationship-based finance is optimal because only high-quality entrepreneurs receive funding. However, formal finance may emerge in equilibrium, and it has the only effect of shifting rents from entrepreneurs to financiers. In more-developed economies with higher capital endowments, formal finance becomes necessary to prevent funding of low-quality entrepreneurs. Nevertheless, relationship-based financing may persist in equilibrium, and low-quality close entrepreneurs are funded even when there are high-quality distant entrepreneurs.

Using hand-collected data from Form Ds on executives in venture capital (VC)-backed private firms, I show that VC-driven top management changes lead to a significantly greater quantity and quality of innovation, which potentially occurs through new management teams hiring more and higher quality inventors. My evidence demonstrates that both founder replacements and non-founder management changes are associated with enhanced innovation. Further, adding top managers with general managerial skills enhances innovation, whereas changing managers with a prior technical background does not. Finally, top management changes lead to the adoption of an exploitative (rather than explorative) innovation search strategy by private firms.