Is heterogeneous capital depreciation important for estimating firm-level productivity? Evidence from Chinese manufacturing firms

This paper contributes to the existing literature on estimating firm-level production functions. Using Chinese manufacturing survey data, we employ the firm-level heterogeneous capital depreciation rate to measure firms’ investment and assess its role using Olley and Pakes (1996) (OP) production function estimation technique. Although there is some ongoing debate on the econometric soundness of the OP technique, we argue quantitatively that the heterogeneous depreciation rate muffles the measurement error associated with the key input demand investment. In our sample, it significantly narrows the gap of total factor productivity (TFP) estimates between the OP technique and a state-of-the-art estimation method that works without investment. We further reveal that the improved performance primarily originates from the dynamic evolution in the distribution of the capital depreciation rate.     

Risk aversion,public disclosure,and partially informed outsiders

In this paper, we examine the impact of public disclosure and partially informed outsiders on a risk-averse insider’s trading behavior, market efficiency, and market depth. In our model, under disclosure requirements, except for the final auction, market depth is the same at every auction. When informed outsiders are risk-neutral, in contrast to the case of a risk-averse insider with no informed outsiders, the insider is more concerned about the uncertainty about future price risk. When the number of informed outsiders increases, market liquidity improves, and the insider increases the variance of her random component to conceal her trading strategy. However, since the insider is relatively more risk-averse, she pays less attention to doing this on her own. Besides, the order flow provided by informed outsiders and randomly added by the insider injects additional liquidity into the market. When informed outsiders are risk-averse, compared to risk-neutral informed outsiders, an insider is most concerned about trading risks brought by informed outsiders at the beginning of trading. Furthermore, whether the trader is an insider or informed outsider, the more risk-averse trader has lower expected profits. Moreover, outsiders’ greater risk aversion leads to a smaller market depth.     

Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

A political economy perspective on horizontal FDI in a dynamic Cournot-oligopoly with endogenous entry

Entry in a homogeneous Cournot-oligopoly is excessive if and only if there is business-stealing (Amir et al., 2014). The excessive entry prediction has been derived primarily for closed economies and using a welfarist benchmark. We extend this framework and allow for (1) horizontal FDI in a multi-period setting and (2) interest group-based government behaviour. Opening the market to greenfield investments from abroad tends to aggravate the entry distortion. Moreover, market opening may reduce welfare if a more pronounced entry distortion dominates the gain in consumer surplus. Finally, a government, which places sufficiently little weight on the interests of consumers, will object to market opening, even if welfare rises.     

Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Bankruptcy prediction is still important topic receiving notable attention. Information about an imminent bankruptcy threat is a crucial aspect of the decision-making process of managers, financial institutions, and government agencies. In this paper, we utilize a newly acquired dataset comprising financial parameters derived from the annual reports of small- and medium-sized companies. The data, which reveal the true ratio between bankrupt and non-bankrupt companies, are severely imbalanced and only contain a small fraction of bankrupt companies. Our solution to overcome this challenging scenario of imbalanced learning was to adopt three one-class classification methods: a least-squares approach to anomaly detection, an isolation forest, and one-class support vector machines for comparison with conventional support vector machines. We provide a comprehensive analysis of the financial attributes and identify those that are most relevant to bankruptcy prediction. The highest prediction performance in terms of the geometric mean score is 91%. The results are validated on two datasets from the manufacturing and construction industries.     

Does board gender diversity influence firm profitability? A control function approach

We investigate the relation between board gender diversity and firm profitability using the control function (CF) approach recently suggested by Wooldridge (2015). The CF method takes account of the problem of endogenous explanatory variables that have potential to bias the results. Using a sample of firms that made up the S&P 500 over the period 2004–2015, we find that the presence of women on corporate boards (measured either by the percentage of female directors on corporate boards or the Blau index of heterogeneity) has a positive and significant (at the 1% level) effect on firm profitability (measured by the return on assets). We compare our results to more traditional approaches (such as pooled OLS or the fixed-effects model). Through this study, we shed light on the effect of women on corporate boards on firm performance, as it is still a controversial issue (Post and Byron, 2015).     

媒体关注与企业创新绩效

媒体关注是会发挥信息功能的正面效应进而促进企业创新,还是会发挥负面效应给管理者形成外部压力从而抑制企业创新？在梳理媒体关注信息功能的基础上,基于2010~2018年A股上市公司数据分析媒体关注与企业创新绩效的关系。研究发现：媒体关注对企业创新绩效起到促进作用,在控制了内生性问题后,这一结论依然成立。通过对报道性质进行区分,发现非负面报道对创新绩效的促进作用更加明显,而负面报道并没有显著降低企业创新绩效。在影响机制分析中发现融资约束在媒体关注与企业创新绩效之间起到中介作用,即媒体关注通过缓解企业融资约束进而提高创新绩效。     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

Measuring firm performance: Differentiating between uncontrollable and controllable bad outputs

This paper introduces a new model that differentiates between controllable and uncontrollable bad outputs in the measurement of firm performance; the airline industry is used as the empirical example. The model allows us to measure technical inefficiency, as well as the inefficiency in the production of bad outputs, and the inefficiency in the investments allocated to the reduction of uncontrollable bad outputs. Moreover, we provide a direct measure of the total cost of controllable bad outputs. As the model involves a number of latent (unobservable) variables that cannot be easily integrated out of the likelihood function without producing complicated expressions, we resort to Bayesian techniques. We find notable differences in inefficiency across airlines. More importantly though, we find that the production of bad or undesirable outputs impose significant costs on airlines. However, these cost issues could be reduced if airlines invest more in maintenance and service training.     

Antecedents of the propensity to learn management practices and their impacts on firm outcomes in emerging markets: A Bayesian Model Averaging approach

This article explores the factors that motivate firms to learn new management practices. The hypotheses are empirically tested using a representative sample of 3676 small, medium and large firms from four South Asian countries and across all main sectors of economic activity. Given that we know little about the antecedents of the propensity to learn management practices in emerging markets, the study employs Bayesian Model Averaging approach to overcome the potential issue of model uncertainty. The results reveal that market competition, resource allocation towards internal and external R&D, good quality mobile network coverage and the use of external certified financial auditors have all positive and significant effects on the propensity to learn management practices. The results also suggest that private intellectual property rights protection in the context of inefficient legal systems can deter firms from learning, perhaps in fear of legal ramifications. Finally, the study shows that firms with a higher propensity of learning management practices are more likely to become profitable while exhibiting higher levels of both potential and actual innovation.