Passive blockholders,informational efficiency of prices,and firm value

This paper analyzes the role of passive blockholders in corporate governance using data on Schedule 13G filings. We show that firm value increases with the number and aggregate ownership of passive blockholders after controlling for other possible determinants of firm value. More importantly, we show that the informational efficiency of prices (IEP) increases with the number and aggregate ownership of passive blockholders, and IEP is a channel through which passive blockholders affect firm value. Overall, our results suggest that managers perform better when stock prices reflect the economic consequences of their actions promptly and accurately through information‐based trading of blockholders.     

Estimating Mann–Whitney‐type Causal Effects

Mann–Whitney‐type causal effects are generally applicable to outcome variables with a natural ordering, have been recommended for clinical trials because of their clinical relevance and interpretability and are particularly useful in analysing an ordinal composite outcome that combines an original primary outcome with death and possibly treatment discontinuation. In this article, we consider robust and efficient estimation of such causal effects in observational studies and clinical trials. For observational studies, we propose and compare several estimators: regression estimators based on an outcome regression (OR) model or a generalised probabilistic index (GPI) model, an inverse probability weighted estimator based on a propensity score model and two doubly robust (DR), locally efficient estimators. One of the DR estimators involves a propensity score model and an OR model, is consistent and asymptotically normal under the union of the two models and attains the semiparametric information bound when both models are correct. The other DR estimator has the same properties with the OR model replaced by a GPI model. For clinical trials, we extend an existing augmented estimator based on a GPI model and propose a new one based on an OR model. The methods are evaluated and compared in simulation experiments and applied to a clinical trial in cardiology and an observational study in obstetrics.     

Time-varying comovement and changes of comovement structure in the Chinese stock market: A causal network method

The driving force for the comovement in stock returns is a long-standing debate between classical asset pricing theory and behavioral finance theory. It has become critically important recently for understanding systemic risk and risk contagion in the market. In this study, we propose complex networks enabled new methods to measure the causal comovement of individual stocks and the comovement structure of the market, which facilitate the examination of all kinds of hypotheses of comovement theories in a unified framework. Using a sample of the Chinese stock market from Jan. 1, 2006 to Dec. 31, 2016, we find that the degree of comovement generally intensifies over time, with a drastic increase from 2011 to 2015, while the comovement structure of the market changes with different market situations. Most importantly, our study reveals the driving force of causal comovement among individual stocks; that is, sentiment-based factors related to the market index indeed induce excess causal comovement in returns beyond that can be justified by fundamental factors including beta coefficient, book-to-market ratio, liquidity, profitability and volatility. Our study also reveals the determinants of comovement structure, which are attributable to the change of investors' behaviors in different periods. It turns out that investors in the Chinese stock market care about risk-return relationship in normal periods, while they seem to care only about risk in crisis periods.     

Direct causal mechanisms of profit: dominant paradigm of profit seeking

This paper develops the direct causal mechanisms of profit (DCMP) to meet the conditions/requirements normatively drawn from the conceptual, logical and methodological dimensions of profit seeking, direct causal mechanisms, holistic and non-egalitarian approach, and deductive inference, which holds the proximate causal mechanism between technological change and needs evolution at the macro-foundations, and the ultimate causal mechanism between needs-focused innovation and explicit needs at the micro-foundations. From DCMP it derives propositions about matching goodness of the proximate causal mechanism and adaptive goodness of the ultimate causal mechanism, verifies them with the experiences of the winners/losers, and finally concludes that DCMP can work as the dominant paradigm of profit seeking, the determinants of firm/business success, and a powerful theoretical and practical foundation/framework for business model design and innovation, asserting that DCMP explains profit as outcome/effect in a more robust/comprehensive/persuasive manner than the existing paradigms because DCMP always guarantees the genuine causality.     

Inference for treatment effects of job training on wages: using bounds to compute Fisher’s exact p-value

In the context of a training program’s randomized evaluation, where estimating wage effects is of interest, we propose employing bounds that control for sample selection as a model-based statistic to conduct randomization-based inference à la Fisher. Inference is based on a sharp null hypothesis of no treatment effect for anyone. In contrast to conventional inference, Fisher p-values are nonparametric and do not employ large sample approximations.     

Adversarial and Amiable Inference in Medical Diagnosis,Reliability and Survival Analysis

In this paper, we develop a family of bivariate beta distributions that encapsulate both positive and negative correlations, and which can be of general interest for Bayesian inference. We then invoke a use of these bivariate distributions in two contexts. The first is diagnostic testing in medicine, threat detection and signal processing. The second is system survivability assessment, relevant to engineering reliability and to survival analysis in biomedicine. In diagnostic testing, one encounters two parameters that characterize the efficacy of the testing mechanism: test sensitivity and test specificity. These tend to be adversarial when their values are interpreted as utilities. In system survivability, the parameters of interest are the component reliabilities, whose values when interpreted as utilities tend to exhibit co‐operative (amiable) behavior. Besides probability modeling and Bayesian inference, this paper has a foundational import. Specifically, it advocates a conceptual change in how one may think about reliability and survival analysis. The philosophical writings of de Finetti, Kolmogorov, Popper and Savage, when brought to bear on these topics constitute the essence of this change. Its consequence is that we have at hand a defensible framework for invoking Bayesian inferential methods in diagnostics, reliability and survival analysis. Another consequence is a deeper appreciation of the judgment of independent lifetimes. Specifically, we make the important point that independent lifetimes entail at a minimum, a two‐stage hierarchical construction.     

Estimating the J function without edge correction

The interaction between points in a spatial point process can be measured by its empty space function F , its nearest-neighbour distance distribution function G , and by combinations such as the J function J = (1 G )/(1 F ). The estimation of these functions is hampered by edge effects: the uncorrected, empirical distributions of distances observed in a bounded sampling window W give severely biased estimates of F and G . However, in this paper we show that the corresponding uncorrected estimator of the function J = (1 G )/(1 F ) is approximately unbiased for the Poisson case, and is useful as a summary statistic. Specifically, consider the estimate _W of J computed from uncorrected estimates of F and G . The function J _W ( r ), estimated by _W , possesses similar properties to the J function, for example J _W ( r ) is identically 1 for Poisson processes. This enables direct interpretation of uncorrected estimates of J , something not possible with uncorrected estimates of either F , G or K . We propose a Monte Carlo test for complete spatial randomness based on testing whether J _W ( r ) 1. Computer simulations suggest this test is at least as powerful as tests based on edge corrected estimators of J .     

Quality Measurement in Professional Services Firms

The purpose of this paper is to examine the nature of the association between service quality as perceived by consumers and its determinants. In particular, the SERVQUAL instrument is discussed and then it is demonstrated how it can be adapted 1.0 fit the needs of small professional services firms using a CPA firm as an example. The entire analysis can be performed with a spreadsheet package and the results are easy to interpret. The results are presented and the managerial implications are discussed.     

一种基于证据反推法的装备保障模式分析方法

现有装备保障模式分析方法多局限于定性分析，分析过程的起点就没有必要的数 据支撑，从而导致结论真实性和有效性不高。针对此问题，提出了一种基于证据反推法的装 备保障模式分析 方法。在分析装备保障模式概念与内涵的基础上，结合装备保障模式运行特点，阐述了基于 证据反推法的基本原理，提出了基于证据反推法分析的基本流程，并给出了基于证据反推法 的装备保障模式分析的具体步骤。