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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 Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sino Danish Coll Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R 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.

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

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NetNDP: Nonoverlapping (delta, gamma)-approximate pattern matching

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INTELLIGENT data ANALYSIS 2022年第6期26卷 1661-1682页

作者： Wu, Youxi Jian, Bojing Li, Yan Jiang, He Wu, Xindong Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Hebei Key Lab Big Data Comp Tianjin Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China Dalian Univ Technol Sch Software Dalian Liaoning Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei Anhui Peoples R China

Pattern matching can be used to calculate the support of patterns, and is a key issue in sequential pattern mining (or sequence pattern mining). Nonoverlapping pattern matching means that two occurrences cannot use the same character in the sequence at the same position. Approximate pattern matching allows for some data noise, and is more general than exact pattern matching. At present, nonoverlapping approximate pattern matching is based on Hamming distance, which cannot be used to measure the local approximation between the subsequence and pattern, resulting in large deviations in matching results. To tackle this issue, we present a Nonoverlapping Delta and gamma approximate Pattern matching (NDP) scheme that employs the (delta, gamma)-distance to give an approximate pattern matching, where the local and the global distances do not exceed delta and gamma, respectively. We first transform the NDP problem into a local approximate Nettree and then construct an efficient algorithm, called the local approximate Nettree for NDP (NetNDP). We propose a new approach called the Minimal Root Distance which allows us to determine whether or not a node has root paths that satisfy the global constraint and to prune invalid nodes and parent-child relationships. NetNDP finds the rightmost absolute leaf of the max root, searches for the rightmost occurrence from the rightmost absolute leaf, and deletes this occurrence. We iterate the above steps until there are no new occurrences. Numerous experiments are used to verify the performance of the proposed algorithm.

关键词： Patterns matching approximate pattern matching nonoverlapping condition (delta, gamma)-distance Nettree structure

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A Brief Survey of Distribution Robust Graph Neural Networks 11

A Brief Survey of Distribution Robust Graph Neural Networks

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

作者： Zheng, Lei Quan, Pei Shi, Yong Niu, Lingfeng The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China The College of Economics and Management Beijing University of Technology Beijing100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing1001090 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

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction. © 2024 The Authors.

关键词： Distribution generalization Graph neural networks Shift robust

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COMPREHENSIVE ANALYSIS OF OVER-SMOOTHING IN GRAPH NEURAL NETWORKS FROM MARKOV CHAINS PERSPECTIVE

arXiv

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arXiv 2022年

作者： Zhao, Weichen Wang, Chenguang Han, Congying Guo, Tiande Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is still a lack of comprehensive understanding and conclusion of this problem. In this work, we analyze the over-smoothing problem from the Markov chain perspective. We focus on message passing of GNN and first establish a connection between GNNs and Markov chains on the graph. GNNs are divided into two classes of operator-consistent and operator-inconsistent based on whether the corresponding Markov chains are time-homogeneous. Next we attribute the over-smoothing problem to the convergence of an arbitrary initial distribution to a stationary distribution. Based on this, we prove that although the previously proposed methods can alleviate over-smoothing, but these methods cannot avoid the over-smoothing problem. In addition, we give the conclusion of the over-smoothing problem in two types of GNNs in the Markovian sense. On the one hand, operator-consistent GNN cannot avoid over-smoothing at an exponential rate. On the other hand, operator-inconsistent GNN is not always over-smoothing. Further, we investigate the existence of the limiting distribution of the time-inhomogeneous Markov chain, from which we derive a sufficient condition for operator-inconsistent GNN to avoid over-smoothing. Finally, we design experiments to verify our findings. Results show that our proposed sufficient condition can effectively improve over-smoothing problem in operator-inconsistent GNN and enhance the performance of the model. © 2022, CC BY-NC-SA.

关键词： Message passing

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Partial label learning: Taxonomy, analysis and outlook

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NEURAL NETWORKS 2023年第1期161卷 708-734页

作者： Tian, Yingjie Yu, Xiaotong Fu, Saiji Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China

Partial label learning (PLL) is an emerging framework in weakly supervised machine learning with broad application prospects. It handles the case in which each training example corresponds to a candidate label set and only one label concealed in the set is the ground-truth label. In this paper, we propose a novel taxonomy framework for PLL including four categories: disambiguation strategy, transformation strategy, theory-oriented strategy and extensions. We analyze and evaluate methods in each category and sort out synthetic and real-world PLL datasets which are all hyperlinked to the source data. Future work of PLL is profoundly discussed in this article based on the proposed taxonomy framework.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Partial label learning Machine learning Weakly supervised learning Partial multi-label learning

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OPR-Miner: Order-Preserving Rule mining for Time Series

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2023年第11期35卷 11722-11735页

作者： Wu, Youxi Zhao, Xiaoqian Li, Yan Guo, Lei Zhu, Xingquan Fournier-Viger, Philippe Wu, Xindong Hebei Univ Technol Sch Artif Intelligence Tianjin 300401 Peoples R China Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA Shenzhen Univ Shenzhen 518060 Guangdong Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230009 Peoples R China

Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the orderpreserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance.

关键词： Pattern mining rule mining time series order-preserving frequent trend

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Learning Hierarchical Modular Networks for Video Captioning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第2期46卷 1049-1064页

作者： Li, Guorong Ye, Hanhua Qi, Yuankai Wang, Shuhui Qing, Laiyun Huang, Qingming Yang, Ming-Hsuan Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA 5005 Australia Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100045 Peoples R China Univ Calif Merced Merced CA 95343 USA Yonsei Univ Seoul 03722 South Korea Google Mountain View CA 94043 USA

Video captioning aims to generate natural language descriptions for a given video clip. Existing methods mainly focus on end-to-end representation learning via word-by-word comparison between predicted captions and ground-truth texts. Although significant progress has been made, such supervised approaches neglect semantic alignment between visual and linguistic entities, which may negatively affect the generated captions. In this work, we propose a hierarchical modular network to bridge video representations and linguistic semantics at four granularities before generating captions: entity, verb, predicate, and sentence. Each level is implemented by one module to embed corresponding semantics into video representations. Additionally, we present a reinforcement learning module based on the scene graph of captions to better measure sentence similarity. Extensive experimental results show that the proposed method performs favorably against the state-of-the-art models on three widely-used benchmark datasets, including microsoft research video description corpus (MSVD), MSR-video to text (MSR-VTT), and video-and-TEXt (VATEX).

关键词： Video captioning hierarchical modular network scene-graph reward reinforcement learning

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Generating Clarifying Questions with Web Search Results 22

Generating Clarifying Questions with Web Search Results

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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhao, Ziliang Dou, Zhicheng Mao, Jiaxin Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450387323

Asking clarifying questions is an interactive way to effectively clarify user intent. When a user submits a query, the search engine will return a clarifying question with several clickable items of sub-intents for clarification. According to the existing definition, the key to asking high-quality questions is to generate good descriptions for submitted queries and provided items. However, existing methods mainly based on static knowledge bases are difficult to find descriptions for many queries because of the lack of entities within these queries and their corresponding items. For such a query, it is unable to generate an informative question. To alleviate this problem, we propose leveraging top search results of the query to help generate better descriptions because we deem that the top retrieved documents contain rich and relevant contexts of the query. Specifically, we first design a rule-based algorithm to extract description candidates from search results and rank them by various human-designed features. Then, we apply an learning-to-rank model and another generative model for generalization and further improve the quality of clarifying questions. Experimental results show that our proposed methods can generate more readable and informative questions compared with existing methods. The results prove that search results can be utilized to improve users' search experience for search clarification in conversational search systems.

关键词： Clarifying Question Search Clarification Conversational Search

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Improving Session Search by Modeling Multi-Granularity Historical Query Change 22

Improving Session Search by Modeling Multi-Granularity Histo...

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15th ACM International Conference on Web Search and data mining (WSDM)

作者： Zuo, Xiaochen Dou, Zhicheng Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Key Lab Data Engn & Knowledge Engn MOE Beijing Peoples R China

ISBN: (纸本)9781450391320

In session search, it's important to utilize historical interactions between users and the search engines to improve document retrieval. However, not all historical information contributes to document ranking. Users often express their preferences in the process of modifying the previous query, which can help us catch useful information in the historical interactions. Inspired by it, we propose to model historical query change to improve document ranking performance. Especially, we characterize multi-granularity query change between each pair of adjacent queries at both term level and semantic level. For term level query change, we calculate three types of term weights, including the retained term weights, added term weights and removed term weights. Then we perform term-based interaction between the candidate document and historical queries based on the term weights. For semantic level query change, we calculate an overall representation of user intent by integrating the representations of each historical query obtained by different types of term weights. Then we adopt representation-based matching between this representation and the candidate document. To improve the effect of query change modeling, we introduce query change classification as an auxiliary task. Experimental results on AOL and TianGong-ST search logs show that our model outperforms most existing models for session search.

关键词： Session Search Query Change Document Ranking

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Movie Recommendation Algorithm Based on Sentiment Analysis and LDA 8

Movie Recommendation Algorithm Based on Sentiment Analysis a...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhang, Yilin Zhang, Lingling Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Sentiment Analysis Topic Extraction Content-based Recommendation Movie Reviews

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