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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Yu, Anqi Peng, Shuyang Li, Yizhi Guo, Kun 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 Dornslife School of Arts and Science University of Southern California Los AngelesCA90007 United States Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As global climate change intensifies, carbon emissions trading has become an important tool for countries to address climate change. China, as the world's largest carbon emitter, has been conducting carbon trading pilots since 2011, and officially launched a national carbon market in 2017. The construction of China's carbon market is crucial for China and global efforts to address climate change. Market efficiency is a key indicator of its success. This article aims to measure and analyze the effectiveness of China's carbon market, and to explore policy measures to enhance its effectiveness. It selected data from effective trading days and conducted an exploratory study on the effectiveness of China's pilot carbon markets. To analyze the effectiveness of the carbon market, we combined the efficient market hypothesis theory with the fractal market hypothesis theory, adopted the variance ratio test method, and the rescaled range analysis method to conduct a comprehensive analysis of the Chinese carbon market, while also accounting for the reasons behind their computational inconsistency. As our research shows that China's pilot carbon market has not reached the weak efficiency level, we have provided some suggestions based on the current operational status of China's carbon market. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Carbon

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A Comprehensive Survey of Loss Functions in Machine Learning

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Annals of data Science 2022年第2期9卷 187-212页

作者： Wang, Qi Ma, Yue Zhao, Kun Tian, Yingjie School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 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 School of Logistics Beijing Wuzi University Beijing101149 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

As one of the important research topics in machine learning, loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has a big gap to summarize, analyze and compare the classical loss functions. Therefore, this paper summarizes and analyzes 31 classical loss functions in machine learning. Specifically, we describe the loss functions from the aspects of traditional machine learning and deep learning respectively. The former is divided into classification problem, regression problem and unsupervised learning according to the task type. The latter is subdivided according to the application scenario, and here we mainly select object detection and face recognition to introduces their loss functions. In each task or application, in addition to analyzing each loss function from formula, meaning, image and algorithm, the loss functions under the same task or application are also summarized and compared to deepen the understanding and provide help for the selection and improvement of loss function. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Surveys

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Stock index prediction using global market indices: A Granger causality-based graph representation learning method 10

Stock index prediction using global market indices: A Grange...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Wang, Yunong Wu, Jie Shi, Yong 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 Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Global stock markets are increasingly connected and may experience strongly correlated stock price fluctuations due to external shocks, cooperation, and competition. In this paper, we propose a Granger causality-based graph representation learning method to improve the accuracy of stock index prediction. In the first stage, a Granger causality test is performed using the historical data of the target stock market and other global stock markets to select the neighboring stock markets with Granger causality and get the relational weight of each neighboring stock market. Then, the temporal weight of trading days is obtained based on the constructed VAR model coefficients. Subsequently, the daily graph embedding of the target stock index is learned by graph convolution operation for the prediction of the next day's closing price. The method developed in this paper experimented on four well-known Chinese stock indices, and it outperformed the benchmark model used in this paper. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Financial markets

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research on Liquidity Measurement and Influencing Factors of China Carbon Emission Allocation Trading Market

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Procedia Computer Science 2023年 221卷 1448-1457页

作者： Yong Shi Shuyang Peng Anqi Yu Kun Guo 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

Considering multi-dimensional concepts of liquidity, we introduces two classical liquidity measures to quantify the liquidity of China carbon emission allowance(CCEA) trading markets, and analyze their trading activities. Then monthly unbalanced panel was constructed based on daily trading data to further study on the influencing factors of CCEA market. We find that: 1) No matter what method we take, the Hubei pilot is the most liquid in the context of overall running periods, and the national carbon market and Guangdong pilot are also well performed compared to the other markets. 2)The foundation of national generalized market could bring about noticeable changes on the liquidity of carbon market, and Guangdong market which is one of the 8 carbon market pilots become more developed and liquid, while no significant variation in the rest of 7 pilots. 3) The emission control enterprises of each pilot have no inborn incentive to trade proactively, therefore transactions are concentrated on the performance cycle. 4)Regression results show that the price at the end of each month, trading volumes aggregated in each month, trading frequency of each month are all positively correlated with the liquidity, and return volatility of each month, carbon market fragmentation are negatively related to the liquidity. Finally we put forward some suggestion and policy recommendation rested on the above qualitative and quantitative analysis.

关键词： CEA,Carbon market,Liquidity,Measurement,Influencing factors

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A dynamic key node learning algorithm in evolving financial network: case study during the period of coronavirus

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Procedia Computer Science 2022年 214卷 1587-1594页

作者： Doudou Lu Wei Deng Yongyin Xu Yong Shi College of Big Data Statistics Guizhou University of Finance and Economics Guizhou 550025 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Finding an influential set of nodes in the interconnected network, whose activities would maximally influence the development of overall network, is a fundamental topic in finance, biology, social science, epidemic control and other scenarios with respect to network science. In recent years, some works are proposed to find key nodes via network connectivity measures, these studies assume a static environment, and besides, key nodes are calculated through pairwise connectivity, the number of connected components and other measures from the perspective of graph theory. However, these studies leave out of consideration on the dynamic evolving environment and causal relationships in the network. In this paper, we introduce a key node learning algorithm in an evolving financial network. The intuition of the algorithm is to model the causality of financial entities over the time, and the metrics for cascading networks are proposed to grade the entities in the network. In the experiment, the worldwide financial activities before and during the period of coronavirus are analyzed. The proposed metrics can effectively capture the influential entities and quantitatively describe these influences on the whole financial system.

关键词： COVID-19 stock market indices high-impact entities

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

Not All Diffusion Model Activations Have Been Evaluated as D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques 38

Suppress Content Shift: Better Diffusion Features via Off-th...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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Volatility and risk contagion of international stock market in the context of COVID-19

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Procedia Computer Science 2023年 221卷 370-377页

作者： Wen Long Yuhan Zeng Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 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 P.R. China

In order to reveal the impact of the outbreak of COVID-19 epidemic on risk contagion in the international stock market, this paper establishes an ARMA-GARCH-Copula model based on the R-Vine structure to empirically analyze the stock index data of 19 important countries(regions) around the world, and uses complex network to display the risk contagion path. This research shows that the risk contagion correlation of the global stock market has significantly improved due to the impact of COVID-19; The correlation of risk transmission among European countries is significantly higher than that of other regions, reflecting a closer integration connection between EU countries; Finally, unlike previous literature, this article finds that stock market of Hong Kong in China has become an important node in the international stock market, playing an important role in the risk network before and after the epidemic.

关键词： COVID-19 R-Vine-Copula risk contagion complex network

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Macroprudential policy in retail lending: Concept and results – a case study of the Bank of Russia

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Procedia Computer Science 2024年 242卷 1331-1338页

作者： Ekaterina Eremina Yong Shi School of Economics and Management University of Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China

Macroprudential regulation of the banking system is a significant instrument for ensuring the financial stability of the state. Unsecured consumer lending is growing at a steadily high rate (in August-October 2022, the average monthly debt growth was 0.9%), and it remains a priority segment for banks because of its high margins. The share of unsecured consumer loans provided to borrowers with debt burden indicator (DBI) of more than 80% amounted to more than 32% in the third quarter of 2022 (28% in the second quarter of 2022). The share of consumer loans for a term of more than 5 years remains at a high level (15% in July-September 2022). The share of MFO loans issued to borrowers with a DBI of more than 80% was 41% in the second quarter of 2022 (38% in the first quarter of 2022). A further increase in the citizens’ debt burden in the context of the economy restructuring may create additional macroeconomic risks. Problems of citizens with servicing loans and borrowings can lead to a reduction in demand in the economy, and losses of banks as a result of writing off “bad” loans can lead to a decrease in their ability to lend to the economy. To limit the risks associated with an increase in the citizens’ debt burden, since the first quarter of 2023, the Bank of Russia has established direct quantitative restrictions on the issuance of certain types of credits (loans) and the application of increased buffers to risk coefficients if creditors exceed established limits. This article discusses the main approaches to implementation of the macroprudential policy of the Central Bank of the Russian Federation, its goals and instruments, and peculiarities of application in the current conditions.

关键词： macroprudential policy banking system risks macroprudential instruments Bank of Russia

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