Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency.In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency -Technical Efficiency, we assume Variable Returns to Scale, and decompose Technical Efficiency into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the levels of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, whilst the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power is improved by this corporate efficiency information.
For the management of coal fly ashes (CFAs) from coal-fired power plants (CFPPs), characterization of PAHs and PCBs in CFAs is imperative. The 18 PAH and 86 PCB congeners in CFAs collected from 18 large-scale CFPPs in China were detected using GC/MS system. The PAH concentrations were in the range of 5.51-70.9 ng g -1 for 16 CFPPs with individual block power capacity as 600 MW (IBPC-600), significantly lower than 886-916 ng g -1 for 2 CFPPs with IBPC as 200 and 300 MW (IBPC-200/300). Both PAH and PCB congeners for 18 CFPPs were dominated by low molecular weight ones. The 3-and 2-ring PAHs, di-, tri-and tetra-PCBs were the predominant homologs. PAH profiles for 16 CFPPs with IBPC-600 were significantly different from other industrial stacks based on higher coefficients of divergence. The BaP-based toxic equivalency (BaPeq) concentration and BaP-based equivalent carcinogenic power (BaPE) for 16 CFPPs with IBPC-600 were 0.834 ng g -1 and 0.570, much lower than corresponding 20.5 ng g -1 and 15.4 for 2 CFPPs with IBPC-200/300. No difference existed for Σ 86 PCBs between CPFFs with IBPC-600 and -200/300, which ranged from 9.60 to 32.1 ng g -1 . Higher mean carcinogenic PAH concentrations for 2 CFPPs with IBPC-200/300 and PCBs-TEQ concentration for 18 CFPPs indicated the application of CFAs as soil amendment should be prohibited. The PAH concentrations for 18 CFPPs were well correlated with the total organic carbon (TOC) values, while PCB concentrations showed not this trend, indicated the different formation mechanism between PCBs and PAHs.
Purpose: The bid-ask spread is important for many reasons. Because spread data are not always available, many methods have been suggested for estimating the spread. Existing papers focus on the performance of the estimators either under ideal conditions or in real data. The gap between ideal conditions and the properties of real data is usually ignored. The consistency of the estimates across various sampling frequencies is also ignored. This paper investigates the performance of estimators of the bid-ask spread in a wide range of circumstances and sampling frequencies. Design:The estimators and the possible errors are analysed theoretically. Then we perform simulation experiments, reporting the bias, standard deviation and root mean square estimation error of each estimator. More specifically, we assess the effects of the following factors on the performance of the estimators: the magnitude of the spread relative to returns volatility, randomly varying of spreads, the autocorrelation of mid-price returns, and mid-price changes caused by trade directions and feedback trading. 1 the sampling frequency. In small samples, the standard deviation can be more important to the estimation error than bias; in large samples, the opposite tends to be true. Originality:There is a conspicuous lack of simulation evidence on the comparative performance of different estimators of the spread under the less than ideal conditions that are typical of real-world data. This paper aims to fill this gap.
A new estimator of bid-ask spreads is presented. When the trade direction is known, any estimate of the spread is associated with a unique series of conjectural midprices derived by adjusting the observed transaction price by half the estimated spread. It is shown that the covariance of successive conjectural mid-price returns is maximised (or least negative) when the estimated spread is equal to the true spread. A search procedure to maximise this covariance may therefore be used to estimate the true spread. The performance of this estimator under various conditions is examined both theoretically and with Monte Carlo simulations. The simulations confirm the theoretical results. The performance of the estimator is good.
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