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

作者： Long, Wen Gao, Jing Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing100190 China

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect. © 2024 The Authors.

关键词： data accuracy

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Artificial cheerleading in IEO: Marketing campaign or pump and dump scheme

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INFORMATION PROCESSING & management 2024年第1期61卷

作者： Tian, Yingjie Xie, Yuhao Univ Chinese Acad Sci Sch Econ & Management 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 MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

Initial coin offerings (ICOs) have been vulnerable to pump-and-dump schemes, where fraudsters spread false information on social media to inflate coin prices. However, initial exchange offerings (IEOs) are considered more reliable as exchanges conduct due diligence on projects. This study investigates the effectiveness of IEOs in mitigating pump-and-dump schemes by analyzing over 11,000 tweets posted by different accounts from 54 IEO projects one month prior to the IEO. We select 22 projects with at least 1000 tweets and use topic modeling to perform a descriptive analysis. We use botometer to produce a bot score for each tweet account and a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for sentiment analysis. We introduce penalized sentiment scores and interaction term and use an OLS regression model to identify the relationship between bot scores, sentiment values, and post-IEO performance. Our research finds that bot score is negatively associated with 30-day return and is positively associated with 30-day volatility, indicating the presence of pump-and -dump schemes in IEOs. We suggest that investors, exchanges, and regulators should take steps to assess the risks associated with IEOs and design appropriate interventions to prevent market manipulation.

关键词： Deep learning Social media analysis Initial exchange offering Bot detection

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Consistent estimation of the number of communities via regularized network embedding

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BIOMETRICS 2023年第3期79卷 2404-2416页

作者： Ren, Mingyang Zhang, Sanguo Wang, Junhui Chinese Univ Hong Kong Dept Stat Ma Liu Shui Hong Kong Peoples R China Univ Chinese Acad Sci Sch Math Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

The network analysis plays an important role in numerous application domains including biomedicine. Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. In this paper, we propose a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector toward its center and pushes similar community centers collapsed with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. Extensive numerical experiments have also been conducted on both synthetic networks and brain functional connectivity network, which demonstrate the superior performance of the proposed method compared with existing alternatives.

关键词： brain network community detection fusion penalty latent space model stochastic block model

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Class-view graph knowledge distillation: A new idea for learning MLPs on graphs

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NEUROCOMPUTING 2025年 637卷

作者： Tian, Yingjie Xu, Shaokai Li, Muyang Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Graph Neural Networks (GNNs), while effective for processing non-Euclidean structured data, suffer from computationally intensive neighbor fetching, which hinders their deployment in low-latency applications. Cross-architecture graph knowledge distillation (KD), which trains high-performance Multi-layer Perceptrons (MLPs) to emulate teacher GNNs, offers a promising solution. However, existing GNN-MLP distillation methods rely on the sample-view KD paradigm, where student models directly mimic the teacher's parameter space. Given the fundamentally different architectures and parameter spaces of GNNs and MLPs, this direct mimicry approach limits effective knowledge transfer. Inspired by the inherent properties of MLPs, we propose a novel class-view KD paradigm for GNN-MLP distillation. Unlike sample-view KD, our method guides student MLPs to generate more discriminative parameter configurations within their own parameter space while preserving the similarity of prediction distributions with the teacher, rather than directly imitating the teacher's parameter configurations. Extensive experiments on public benchmark datasets demonstrate that class-view KD outperforms sample-view KD across various evaluation metrics and can be seamlessly integrated into existing GNN-MLP distillation methods to improve performance without additional computational cost. The code is available at https://***/xsk160/Class-View-Graph-KD.

关键词： Graph knowledge distillation Cross-architecture Class-view

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Measuring China's Real Estate Financial Innovation from the Perspective of Government,Enterprises and the Public:Index Compilation and Its Spatial-Temporal Characteristics Analysis

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Journal of Systems Science and Information 2023年第1期11卷 1-34页

作者： Jichang DONG Lijun YIN Xiaoting LIU Xiuting LI School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China China Construction Second Engineering Bureau Co.Ltd Beijing 100070China

In recent years,China has witnessed the rapid development in housing finance,and there have emerged constantly real estate finance innovations;however,there exists no relevant index for measuring the innovations of China's real estate *** on the perspectives of the governments,enterprises and the public,this paper constructs the"innovation index of real estate finance"on a quarterly basis from 2009 to 2019,with the method of empowerment which combines the subjective method(analytic hierarchy process)and the objective one(range coefficient method).It clearly and concretely depicts the innovations in housing finance and the related temporal-spatial characteristics in China since the outbreak of the financial crisis in *** index covers 30 provinces,autonomous regions and municipalities directly under the central government,and analyzes its temporal and spatial *** findings show that there exist a strong spatial autocorrelation and a big regional difference in innovations.

关键词： governments enterprises public real estate finance innovation

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Fine-grained multi-view clustering with robust multi-prototypes representation

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APPLIED INTELLIGENCE 2023年第7期53卷 8402-8420页

作者： Yin, Hongwei Wang, Guixiang Hu, Wenjun Zhang, Zhao Huzhou Univ Sch Informat Engn & Zhejiang Prov Key Lab Smart Management Applicat Modern Agr Resources Huzhou 313000 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China

Multi-view clustering is a hot research topic that improves clustering performance by leveraging complementary information from multiple views. Recently, many multi-view clustering methods have been proposed. Most of them take the entire sample space as a fusion object and treat the local structures within each view equally. This paradigm is considered coarse-grained information fusion. However, in many real-world applications, different local structures with strong or weak clustering capacities could coexist in one view. To fully exploit valuable information of local structures, it is necessary to distinguish these local structures with different clustering capacities. In this paper, we propose a novel fine-grained multi-view clustering method. First, the sample space of each view is divided into many sub-clusters by using multi-prototypes representation. Second, the robustness of the multi-prototypes representation is enhanced by reducing the overlap between sub-clusters, which can reduce the effect of noise data. Finally, each sub-cluster's contribution weights are automatically assigned based on its clustering capacity. In addition, the robust multi-prototypes representation, the fine-grained multi-view fusion, and the clustering process are integrated into a unified framework. An effective alternating optimization algorithm is adopted to solve the objective function. Extensive experiments on two toy datasets and several real-world datasets prove that our method outperforms the traditional methods in clustering accuracy.

关键词： Multi-view clustering Fine-grained fusion Multi-prototypes representation Sub-cluster structure

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How China Deals with COVID-19 Pandemic

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第1期23卷 17-35页

作者： Shi, Yong Qu, Yi Zhang, Linzi Univ Chinese Acad Sci Sch Econ & Management 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 Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

Currently, the whole world is still suffering from the COVID-19 (variants) pandemic that has been lasting for over three years and resulted in countless losses. Since its outbreak at the end of 2019, China has first suffered, but quickly gained the upper hand through strict epidemic control and emergency management. Despite several recurrences of regional epidemics in 2022, both economic growth and people's livelihoods have been effectively safeguarded in China, with a regular prevention system established and social life recovery accomplished orderly. How does China deal with this pandemic? What practical measures taken and decisions made could be adopted and shared with other countries? In this paper, these issues are summarized and discussed as progressive lessons for the rest of the world. Though there's a major shift of pandemic policy in late 2022 and early 2023 in China, this study still provides insightful and beneficial implications to support decision- and policy-makings in future fights against any possible COVID-19 variants until the end of the pandemic.

关键词： COVID-19 pandemic China emergency management strategic decision making

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An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2025年

作者： Xiong, Yifei Yang, Xiliang Zhang, Sanguo He, Zhijian Purdue Univ Dept Stat W Lafayette IN USA South China Univ Technol Sch Math Guangzhou 510641 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods. Unlike approximate Bayesian computation, SNPE techniques learn the posterior from sequential simulation using neural network-based conditional density estimators by minimizing a specific loss function. The SNPE method proposed by Lueckmann et al. (NeurIPS 2017) used a calibration kernel to boost the sample weights around the observed data, resulting in a concentrated loss function. However, the use of calibration kernels may increase the variances of both the empirical loss and its gradient, making the training inefficient. To improve the stability of SNPE, this paper proposes to use an adaptive calibration kernel and several variance reduction techniques. The proposed method greatly speeds up the process of training and provides a better approximation of the posterior than the original SNPE method and some existing competitors as confirmed by numerical experiments. We also managed to demonstrate the superiority of the proposed method for a high-dimensional model with a real-world dataset.

关键词： Approximate Bayesian computation Normalizing flows Simulator-based inference

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A Prompt-Based Topic-Modeling Method for Depression Detection on Low-Resource data

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 1430-1439页

作者： Guo, Yanrong Liu, Jilong Wang, Lei Qin, Wei Hao, Shijie Hong, Richang Hefei Univ Technol HFUT Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Hefei Univ Technol HFUT Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore

Depression has a large impact on one's personal life, especially during the COVID-19 pandemic. People have been trying to develop reliable methods for the depression detection task. Recently, methods based on deep learning have attracted much attention from the research community. However, they still face the challenge that data collection and annotation are difficult and expensive. In many real-world applications, only a small number of or even no training data are available. In this context, we propose a Prompt-based Topic-modeling method for Depression Detection (PTDD) on low-resource data, aiming to establish an effective way of depression detection under the above challenging situation. Instead of learning discriminating features from a small amount of labeled data, the proposed framework turns to leverage the generalization power of pretrained language models. Specifically, based on the question-and-answer routine during the interview, we first reorganize the text data according to the predefined topics for each interviewee. Via the prompt-based framework, we then predict whether the next-sentence prompt is emotionally positive or not. Finally, the depression detection task can be achieved based on the obtained topicwise predictions through a simple voting process. In the experiments, we validate the effectiveness of our model under several low-resource data settings. The results and analysis demonstrate that our PTDD achieves acceptable performance when only a few training samples or even no training samples are available.

关键词： Depression Task analysis Interviews Bit error rate Predictive models Feature extraction data models Depression detection low resource prompt learning

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Social media and capital markets: an interdisciplinary bibliometric analysis

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FINANCIAL INNOVATION 2025年第1期11卷 1-32页

作者： Long, Wen Guo, Man Univ Chinese Acad Sci Sch Econ & Management 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

Social media platforms collect information from complex human social behaviors. It has been shown that they significantly impact capital markets. This article conducts a bibliometric analysis of 363 high-quality articles from the Web of Science on social media and capital markets. First, a descriptive analysis was performed, including discipline distribution, growth trends, institutions, publication sources, influential authors, articles, and countries. Second, collaborative networks identify cooperation between countries and institutions in the network analysis section. Bibliometric coupling analysis determines the relationship between journals of different disciplines. Cluster analysis and keyword co-occurrence networks identify hot research topics. Burst detection and keyword time zone analysis track field development. Finally, keyword analysis and clustering analysis are conducted on the research of different disciplines. These sample papers mainly include seven hot topics, including sentiment analysis and financial market prediction. Articles in business and finance focus more on social media's impact on capital markets. In contrast, information technology articles focus more on extracting information from social media. Each field also has its unique research directions. This paper contributes to a comprehensive understanding of the research on social media and capital markets. It offers perspectives on disciplinary differences, serving as a valuable scholarly reference for conducting in-depth research.

关键词： Social media Capital markets Bibliometric analysis Interdisciplinary analysis G12 G41 C55

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