Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China

It is difficult to predict the financial distress of unlisted public firms due to their longer disclosure cycle of accounting information and more inadequate continuity of market trading information compared to listed firms. In this paper, we propose a framework to predict the financial distress of unlisted public firms using current reports. Specifically, to better represent the meaning of current report texts, we propose a semantic feature extraction method based on a word embedding technology. Empirical results show that current reports contain more effective information for predicting the financial distress of unlisted public firms compared with periodic reports. In addition, semantic features extracted using our proposed method significantly improve the predictive performance, and their enhancing effect is superior to that of topic features and sentiment features. Our study also provides implications for stakeholders such as investors and creditors.     

Exploring the factors that drive consumers to use contactless delivery services in the context of the continued COVID-19 pandemic

The spread of the COVID-19 pandemic has resulted in the launch of contactless delivery services. This research integrates resource matching, service quality evaluation, and perceived value theories to explore the factors that promote the use contactless delivery services. The data was obtained through questionnaire surveys, and research hypotheses were verified through the structural equation modelling approach. With the exception of convenience, the results show that privacy, reliability, security, and flexibility have a significantly positive effect on consumers' intention to use “contactless” delivery services through consumers' perceived value. This study contributes to the literature by introducing theoretical frameworks from various paradigms and enriches the academic research on existing theoretical structure models. It also helps optimize resource allocation and realize the social environment related to coexisting with the COVID-19 pandemic.     

桑叶配方颗粒物质基准研究

桑叶配方颗粒迄今尚没有统一的质量标准。为提升桑叶配方颗粒的内在质量,对其物质基准评价方法进行了研究。选取具有代表性产地的15批桑叶制备物质基准,采用高效液相色谱法,以芦丁、异槲皮苷、紫云英苷作为评价指标,确定桑叶物质基准的出膏率范围、标志性成分含量及转移率范围,采用超高效液相色谱法,建立物质基准UPLC特征图谱,并对其主要成分进行指认。结果表明:15批物质基准中,芦丁转移率为19.94%～34.05%,异槲皮苷转移率为11.67%～43.58%,紫云英苷转移率为10.73%～43.05%;物质基准特征图谱中,标定了14个共有峰,指认出7个色谱峰,分别为新绿原酸、绿原酸、咖啡酸、芦丁、异槲皮苷、槲皮素-3-O-(6″-O-丙二酰基)-β-D-葡萄糖苷、紫云英苷。所建立的基准评价方法为桑叶配方颗粒质量标准的制定及其后续大规模生产提供了数据基础。     

电子商务技能型人才需求与高职教育培养匹配研究

产业转型升级发展对专业技能型人才的需求发生变化,作为技能型人才重要培养主体的高职院校,面临着如何培养新时代高素质技能人才的挑战。通过招聘大数据挖掘电子商务技能型人才的需求特征,同时调研相关高职院校电子商务专业人才培养现状,分析供需匹配度。结果表明:电商企业需要德技并修的高素质技能型人才,而高职院校的课程体系以专业技能、商务技能等硬技能教学为主,忽视综合素养软技能教育,同时,高职院校电子商务专业课程开设未能根据行业技术更新和产业转型发展及时调整。基于研究结果,从课程体系建设、校企合作深化、工匠精神培育等方面提出相关建议。     

基于MBP算法和深度学习的人脸识别

为了解决在深度学习提取人脸图像特征时,易忽略其局部结构特征和缺乏对其旋转不变性学习的问题,提出了一种基于单演局部二值模式(MBP)与深度学习相结合的高效率人脸识别方法。首先,用多尺度单演滤波器对图像进行滤波,得到幅值和方向信息;其次,用LBP算法和象限比特的方法进行编码,分块计算组合其直方图特征;然后,将提取的单演特征作为深度信念网络(DBN)的输入,逐层训练优化网络参数,得到优异的网络模型;最后,将训练好的DBN网络在ORL人脸数据库上进行人脸识别实验,进行识别率计算,其识别率为98.75%。所提出的方法使用无监督的贪婪算法,隐藏层设定为2层,使用反向传播算法优化网络。相较于已知的人脸识别方法,MBP+DBN算法对光照、表情和部分遮挡变化具有较好的鲁棒性,在人脸识别中识别率较高,具有一定的优势,为图像特征提供了一种新的识别方法。     

城市造林工程农户行为意向影响因素研究——以北京市平原造林工程为例

以北京市平原造林工程为例,利用典型项目区调查数据建立结构方程模型,探讨影响退耕农户行为意向的主要因素。结果表明:农户综合素质、家庭经济基础和农户土地依赖性对农户满意度和农户行为意向具有显著正向影响;同时,农户综合素质、家庭经济基础和农户土地依赖性还会通过农户满意度对农户行为意向产生影响。据此,后续城市造林过程中应进一步加强对农户满意度的服务工作,同时根据个体农户特征,对被征地农户在再就业、技能培训、农地补偿等方面开展更具针对性的服务工作。     

广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中，最大似然（ML）检测可以取得最优的检测性能，然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题，提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引，接着对候选天线索引按正则化标准进行可靠性验证，剔除错误索引，缩小信号的搜索空间，最后通过求解最小二乘问题估计信号。仿真结果表明，与经典的正交匹配追踪（OMP）算法相比，所提算法以少许复杂度的增加为代价极大提升了检测性能，能够在检测性能与复杂度之间取得更好的折中。     

Does managerial ability matter for cross-border M&As: Evidence from Chinese listed firms

With the deepening of economic globalization, cross-border mergers and acquisitions (CBMAs) are increasingly becoming an important choice of transnational investment, and more Chinese firms are becoming involved in them. However, the success rate of CBMAs for firms in China seems to be lower than that for firms in other countries. Using data on the CBMA deals initiated by Chinese A-share listed enterprises from 2003 to 2018, we examine the association between managerial ability and the likelihood of completing CBMAs. The empirical results show that the average impact of managerial ability on the likelihood of completing CBMAs is positive. Furthermore, by taking heterogeneity of the ownership structure into account, we find that the positive effect of managerial ability is significant only for non-state-owned enterprises. Moreover, the results from the mediation model show that relaxing financing constraints is a possible channel by which managerial ability affects the likelihood of completing CBMAs. Our results are robust to various model specifications.     

Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach

It is commonly observed that high grade loans with better ratings are often associated with low recoveries if they default (i.e. with relatively high loss-given-default (LGD)). To address the mismatch problem, this paper proposes a credit risk approach by minimizing LGD for higher rated loans as a risk-rating matching standard in the sense that the decreasing LGD from creditors’ perspective is associated with higher credit rating for the borrower. This standard forces customers’ credit rating of each grade to be optimally determined in correspondence to its LGD, which means the LGD of high grade loans tends to be low. The approach is then tested using three credit datasets from China, i.e. credit data from 2044 farmers, 2157 small private businesses and 3111 SMEs. The empirical results show that the proposed approach indeed guides the way to solve the mismatch phenomenon between credit ratings and LGDs in the existing credit rating literature. By optimally determining credit ratings, the findings derived from this paper help provide a valuable reference for bankers, and bond investors to manage their credit risk.     

Waste of money or growth opportunity: The causal effect of EU subsidies on Hungarian SMEs

Although EU subsidies aiming at economic development play a pivotal role not only for Hungary but for the entire European Union as well, there is a debate regarding their effectiveness in the literature. This paper investigates the impact of direct economic development subsidies extended in the context of Structural Funds and the Cohesion Fund as part of the 2007–2013 programming period of the European Union on Hungarian micro, small and medium-sized enterprises. Based on a micro database, we evaluate the impact of corporates’ first subsidies on various performance indicators, using a combination of propensity score matching and fixed effects panel regression. According to our results, economic development funds had a significant positive effect on the number of employees, sales revenue, gross value added and, in some cases, operating profit. However, the labour productivity of enterprises was not significantly affected by any of the support schemes. Furthermore, by explicitly comparing non-refundable subsidies (grants) and refundable assistance (financial instruments), we find that there is no significant difference in the effectiveness of the two types of subsidy.