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Information content and sentiment: the role of environmental disclosure in stock price crash risk

International Review of Financial Analysis 2025年 104卷

作者： Long, Wen Ma, Ruiqi Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Environmental disclosure is a topic of global importance. When negative environmental news emerges and is eventually revealed, it can influence investor decisions and lead to significant stock price movements, potentially resulting in stock price crashes. Therefore, using a sample of A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2017 to 2022, we construct an environmental sentiment dictionary, establish an environmental disclosure content index system, and explore the influence mechanism of environmental disclosure on stock price crash risk. Findings show that increased environmental disclosure sentiment intensity can boost stock price crash risk, whereas greater environmental disclosure content can significantly reduce it. Furthermore, we identify a significant threshold effect related to the sentiment intensity of environmental disclosure. Notably, in environmental disclosure, sentiment tends to have a stronger impact than content. Mechanism analysis reveals that environmental disclosure content is influenced by external disclosure sources, the tone of internal annual reports, and internal control mechanisms. Meanwhile, environmental disclosure sentiment intensity is driven by earnings management and investor risk perception. This study develops a more comprehensive method for measuring environmental information disclosure and offers important insights for improving environmental management among listed companies. © 2025

关键词： Environmental information disclosure Sentiment analysis Stock price crash risk

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Deep Transformation Model

arXiv

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arXiv 2024年

作者： Wang, Tong Zhang, Shunqin Zhang, Sanguo Huang, Jian Ma, Shuangge Department of Biostatistics Yale School of Public Health New HavenCT United States School of Mathematical Sciences University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

There has been a significant recent surge in deep neural network (DNN) techniques. Most of the existing DNN techniques have restricted model formats/assumptions. To overcome their limitations, we propose the nonparametric transformation model, which encompasses many popular models as special cases and hence is less sensitive to model mis-specification. This model also has the potential of accommodating heavy-tailed errors – a robustness property not broadly shared. Accordingly, a new loss function, which fundamentally differs from the existing ones, is developed. For computational feasibility, we further develop a double rectified linear unit (DReLU)based estimator. To accommodate the scenario with a diverging number of input variables and/or noises, we propose variable selection based on group penalization. We further expand the scope to coherently accommodate censored survival data. The estimation and variable selection properties are rigorously established. Extensive numerical studies, including simulations and data analyses, establish the satisfactory practical utility of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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An ES-based model with contemporaneous and temporal aggregation for forecasting intermittent and lumpy retail demand

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Procedia Computer Science 2024年 242卷 264-271页

作者： Erjiang E Xin Tian Ye Tao Huiqiang Sun School of Management Guangxi Minzu University Nanning 530006 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy Sciences Beijing 100190 China Beijing Haolinju CVS Co. Ltd. Beijing 100018 China Shenghe (Beijing) Technology Development Co. Ltd. Beijing 101399 China

The demand for products in the retail industry is often characterized by intermittent and volatile patterns. Specifically, at the SKU level, the demand exhibits intermittent and lumpy behavior, which presents challenges for accurate forecasting due to demand fluctuations and interval uncertainty. This study proposes an exponential smoothing (ES)-based model, called ES-C-T, that incorporates contemporaneous and temporal aggregation to forecast intermittent and lumpy retail demand. The ES-C-T model consists of three stages. Firstly, the ES model predicts the sales of each product by aggregating contemporaneously across all stores, and the resulting predictions are allocated to the SKU level using a weight vector. Secondly, the ES model forecasts the sales of each SKU by aggregating temporally within each store. Finally, the ES-C-T model derives a weight coefficient based on the predictions from the first and second stages, adjusts the initial predictions, and utilizes this as the final forecast. To validate the effectiveness of the ES-C-T model, sales data from a large Chinese convenience store chain are employed. The results demonstrate that the proposed ES-C-T model outperforms the benchmark model in terms of MAE, RMSE, and WRMSSE, effectively predicting retail sales for intermittent and lumpy demand.

关键词： forecasting retailing intermittent demand lumpy demand aggregation

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A Review on the Construction of Business Intelligence System Based on Unstructured Image data

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Procedia Computer Science 2022年 199卷 392-398页

作者： Linzi Zhang Zhiquan Qi Fan Meng School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Department of Infomation Management Peking University Beijing 100871 China

Business Intelligence (BI) has been an emerging significant issue in modern government and corporate functions, which attracts the attention of both researchers and merchants. Using the raw data from customers, BI provides effective decision-making knowledge for various cooperate governance operations with the method of data Warehouse and data mining. As the explosive growth of data volume, it has evolved into using unstructured data including image and text in business intelligence system, from the initial descriptive analysis of computational statements. This paper will review the methods of storing and analyzing the unstructured image data used in the construction of BI system, including traditional image processing and novel deep learning methods. In each method, the specific algorithms and application scenarios will be summarized. Together, we will discuss the possible innovative progress of BI system construction and its future trends.

关键词： Business intelligence Unstructured image data big data fusion

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Structure Aware Loss for Deep Unsupervised Industrial Image Registration

Structure Aware Loss for Deep Unsupervised Industrial Image ...

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Yong Shi Mengjin Lyu Zhiquan Qi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha Omaha USA School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Image registration is extensively utilized in industrial applications. However, specific challenges inherent to these environments—such as low texture, significant displacement, and poor lighting conditions—complicate the registration process. This paper introduces a structure-aware loss module tailored to the unique characteristics of industrial images. This module promotes the network’s focus on salient regions while emphasizing structural features. Additionally, it is designed as a plug-in component, facilitating integration with any loss function and homography estimation method. We develop an unsupervised registration network and create an industrial dataset to evaluate the module’s effectiveness. A series of experiments demonstrate that our structure-aware module significantly enhances registration performance.

关键词： Deep learning Image registration Conferences Lighting Estimation data mining

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Analysis on the Transmission of International Oil Price to China’s Price Index--Based on Granger Causality Network

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Procedia Computer Science 2022年 199卷 1276-1283页

作者： Yijing Wang Xiuzhenzi Wang Kun Guo School of Economics and Management University of Chinese Academy of Science Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

This paper uses complex network theory and Granger causality method to construct Granger Causality Network among price indices (PIGCN), specifically based on crude oil price and China’s domestic price index from a systematic perspective and provides a statistical physical method to analyze conduction fluctuations. This paper selects four typical price indices in China, including 36 sub price indices as samples for empirical research. Through the analysis of the PIGCN structure, we have analyzed the volatility transmission path of the international crude oil price and Chinese domestic price index, and further revealed the dynamic relationship among the price indices. Also, we have put forward some policy recommendations to better monitor the spread of price fluctuation risks.

关键词： Granger causality network Price transmission International crude oil price Price indices

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Discovering the relationship between the number of film review topics and box office with NLP techniques 11

Discovering the relationship between the number of film revi...

引用

11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Li, Bo Dai, Wei Liu, Shang Shi, Yong School of Finance and Economics Wuhan City Vocational College No. 10 Yezhihu West Road Hongshan District Wuhan430064 China PKU-WUHAN Institute for Artificial Intelligence No. 770 Gaoxin Avenue Hongshan District Wuhan430070 China School of Economics Shanghai University 99 Shangda Road BaoShan District Shanghai200444 China School of Economics and Management University of Chinese Academy of Sciences No. 3 Zhongguancun South Yitiao Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website ***, and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work. © 2024 The Authors.

关键词： Natural language processing systems

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Impact of China Railway Express on Urban Exports: Evidence from Prefectural-Level City Economic data 9th

Impact of China Railway Express on Urban Exports: Evidence f...

引用

9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Yan, Bochao Tian, Xin Deng, Shiyong Wu, Junfeng Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China China Railway Fifth Survey and Design Institute Group Co. Ltd. Beijing102600 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai200126 China

As a key project of infrastructure connectivity in the "Five-Pronged Approach"of strategic construction "The Belt and Road", the China Railway Express, has significantly improved the level of trade level in countries along the railway. This study uses an empirical model to correlate the container export data of China Railway Express from June 2020 to June 2021 with the export value and finds that the trade volume of China Railway Express is positively correlated with the export value of cities. The relationship between the operation of the China Railway Express and the export trade of cities that enjoy the rail service is rigorously explained through the actual data of the operation of the China Railway Express provided by the enterprises. The empirical results further prove the positive impact of the China Railway Express on cities that enjoy the rail service. © 2022 The Authors. Published by Elsevier B.V.

关键词： Commerce

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Penetration Effect of Informatization on Technological Progress in Logistics Industry: Empirical Evidence from Inter-provincial Panel data in China 9th

Penetration Effect of Informatization on Technological Progr...

引用

9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Gong, Yaling Tian, Xin Jiang, Zuoliang Jiao, Yamin School of Economics and Management Yuzhang Normal University Nanchang330103 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Shanghai Heading Information Engineering Co. Ltd. Shanghai201112 China Shanghai Winjoin Information Technology Co. Ltd. Shanghai200126 China

This paper investigates pervasiveness effect of information technology on the technological progress of logistics in China. Using panel data of 30-regions in China during 2008 to 2020, we propose both static and dynamic panel data models, and find both resource level and spreading rate of information technology have negative influences on technological progress of logistics. The resource level of information technology has a short-term effect on technological progress of logistics, whereas the spreading rate of information technology has a long-term effect. Moreover, the result reveals that the discordant problem of information construction of logistics between resource level and spreading rate cause the negative influence of information technology. © 2022 The Authors. Published by Elsevier B.V.

关键词： Industry 4.0

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Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

引用

arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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