内部控制对公司价值的影响——基于关联交易的中介效应检验

以2010—2017年沪深两市上市公司为样本，基于关联交易视角，采用中介效应检验方法，从对外关联担保、对外提供资金和非公允关联购销3个角度，系统考察内部控制对公司价值的影响及影响机理。研究结果表明:健全的内部控制能显著提升公司价值，良好的内部控制能够抑制对外关联担保，进而提高公司价值，内部控制对外提供资金和非公允关联购销对公司价值没有显著影响；内部控制主要通过抑制关联担保提升公司价值；内部控制对公司价值的影响不随监管强度、市场化程度和产权性质的变化而变化；内部控制对关联担保的抑制作用，仅在内部控制建设监管较严时、市场化程度较高的地区存在。     

First record of the genus Tanaostigma (Hymenoptera,Chalcidoidea, Tanaostigmatidae) from the Afrotropical region with description of three new species

ABSTRACT

The genus Tanaostigma is newly recorded from the Afrotropical region and three new species are described: Tanaostigma lasallei van Noort sp. nov. (South Africa), Tanaostigma mulu van Noort sp. nov. (Kenya) and Tanaostigma ukumbusho van Noort sp. nov. (Kenya). We provide comprehensive images of the holotypes and an illustrated identification key to the African species. New country distribution records are provided for Tanaostigmodes tambotis Prinsloo & LaSalle, 1995. All images presented here as well as supplementary images and online keys are available on www.waspweb.org

www.zoobank.org/urn:lsid:zoobank.org:pub:61D1A59D-3702-480A-B146-73067C29CD82     

The carcinofauna found in stomach contents of the flying gurnard (Dactylopterus volitans) on the continental shelf of the Campos Basin,Brazil

A total of 3109 crustaceans belonging to 50 taxa distributed in 42 families were found in 117 analysed stomachs of flying gurnard (Dactylopterus volitans). Samples were obtained in April 2008 by the R/V Gyre using a bottom trawl towed in 12 stations at 14–100 m depth on the continental shelf of the Campos Basin, Brazil. The carcinofauna was analysed and the order Calanoida (Copepoda) found to be the most important item in terms of relative abundance and frequency of occurrence, followed by the order Amphipoda (Peracarida), the infraorder Brachyura (Decapoda), the order Stomatopoda and the subclass Myodocopa (Ostracoda). In the order Calanoida, the species Pontellopsis cf. villosa (Pontellidae) represented 98.04% of total crustacean abundance. The diet of Dactylopterus volitans varied according to fish size, with higher diversity of Crustacea at smaller size classes, decreasing in larger fishes. A similar pattern regarding depth was obtained, with greater diversity of taxa in gurnard stomachs caught at shallower depths. Flying gurnard is considered a generalized carnivore of invertebrates, eating mobile macrobenthic organisms, such as crustaceans, and its diet varies with its life stage, without any specific group as its main food source.     

The material theory of induction and the epistemology of thought experiments

John D. Norton is responsible for a number of influential views in contemporary philosophy of science. This paper will discuss two of them. The material theory of induction claims that inductive arguments are ultimately justified by their material features, not their formal features. Thus, while a deductive argument can be valid irrespective of the content of the propositions that make up the argument, an inductive argument about, say, apples, will be justified (or not) depending on facts about apples. The argument view of thought experiments claims that thought experiments are arguments, and that they function epistemically however arguments do. These two views have generated a great deal of discussion, although there hasn't been much written about their combination. I argue that despite some interesting harmonies, there is a serious tension between them. I consider several options for easing this tension, before suggesting a set of changes to the argument view that I take to be consistent with Norton's fundamental philosophical commitments, and which retain what seems intuitively correct about the argument view. These changes require that we move away from a unitary epistemology of thought experiments and towards a more pluralist position.     

Debunking material induction

In this paper, we present an explanatory objection to Norton's material theory of induction, as applied to predictive inferences. According to the objection we present, there is an explanatory disconnect between our beliefs about the future and the relevant future facts. We argue that if we recognize such a disconnect, we are no longer rationally entitled to our future beliefs.     

Countering medical nihilism by reconnecting facts and values

A pessimistic strain of thought is fomenting in the health studies literature regarding the status of medicine. Ioannidis’s (2005) now famous finding that “most published research findings are false” and Stegenga’s (2018) book-length argument for medical nihilism are examples of this. In this paper, we argue that these positions are incorrect insofar as they rest on an untenable account of the nature of facts. Proper attention to fallibilism and the social organization of knowledge, as well as Bayesian probabilities in medical reasoning, prompt us to ask why the cynics expect the results of quantitative studies to be incontrovertibly true in the first place. While we agree with Ioannidis and others’ identified flaws in the medical research enterprise, and encourage rectification, we conclude that medical nihilism is not the natural outcome of the current state of research.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.