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Impact of IT Capability on Inventory management: An Empirical Study

Procedia Computer Science 2022年 199卷 142-148页

作者： Xin Tian Haoqing Wang 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 of Sciences Beijing 100190 China

Despite the widespread adoption of technology in inventory management, empirical research that examines the effect of these technologies is scarce. Does IT capability really have a positive effect on the inventory management? This paper empirically studies the usage of information technology by the definition of IT capability. Our unique panel data set allows us to explore the relationship between technology usage and inventory management. Our results show that IT capability has a positive effect on the inventory strategy and inventory operation process, and has a negative effect on the out-of-stock level. Our research demonstrates the superiority of the usage of technology.

关键词： intelligent warehousing business intelligence inventory management logistics

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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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Value of High-Quality Distribution in Front Warehouse Mode Retailing

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Procedia Computer Science 2022年 199卷 110-117页

作者： Jiayi Zhu Xin Tian 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 of Sciences Beijing 100190 China

Front warehouse mode retailing originates from the e-commerce logistics industry, pre-positioning the warehouse and placing customer orders close to the community service station, so that goods can be delivered quickly. Consumers have put forward higher requirements for the timeliness of logistics distribution and the convenience of online purchases in the front warehouse mode. Using panel data of 37,362 transactions from 159 front warehouses in the online market, we study how consumers’ satisfaction on products and logistics, as well as the punctuality of sorting and delivery affect sales performance. Our results show that negative review of products and logistics will significantly reduce retail sales performance. Interestingly, we find that punctual sorting and delivery increase the number of daily orders while reduce the average sales amount per order. When the punctuality of delivery service is guaranteed, consumers are willing to place orders more frequently even for some low-priced daily goods. This paper emphasizes the importance of the quality of delivery service in the front warehouse mode, and inspires retailers to give full play to the role of the front warehouse in meeting the daily needs of consumers for high-frequency and fast-moving consumer goods.

关键词： front warehouse delivery service customer satisfaction sales performance retailing

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Comparative research on multi-source heterogeneous data fusion technologies

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Procedia Computer Science 2024年 242卷 1089-1095页

作者： Wen Long Jing Gao 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 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.

关键词： Multi-view Learning MVL-SVM Market data Financial news data Heterogeneous data fusion

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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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Review of graph construction and graph learning in stock price prediction

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Procedia Computer Science 2022年 214卷 771-778页

作者： Yunong Wang Yi Qu Zhensong Chen Research Center on Fictitious Economy and 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 School of Management and Engineering Capital University of Economics and Business Beijing 100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks.

关键词： Stock price prediction Graph construction Graph learning data structure

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The asset allocation utility of digital cryptocurrency

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Procedia Computer Science 2022年 214卷 1174-1181页

作者： Wen Long Maohua Wang 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 China

In recent years, the digital cryptocurrency market has witnessed rapid development, but its asset allocation function has yet to be verified. In this paper, DCC-GARCH model is used to estimate the dynamic correlation between digital cryptocurrency and current mainstream assets, and the corresponding hedging effect of digital cryptocurrency is studied. Then, three mainstream asset allocation strategies are selected to analyze the risk and return of asset allocation portfolio after adding digital cryptocurrency, which verifies the asset allocation utility of digital cryptocurrency. This study can provide investment basis for investors and has certain theoretical and practical significance.

关键词： Digital Currency Hedging Effect Asset Allocation DCC-GARCH Model

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The Further Development of Intelligent knowledge for Wisdom 9th

The Further Development of Intelligent Knowledge for Wisdom

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

作者： Gu, Jifa Xiao, Xiao Zhang, Lingling Academy of Mathematics and Systems 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 Moe Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at Ucas Beijing100190 China

This paper introduces some modes from D, I, K to W, M. From different angles we investigate the various process. Especially we utilize the intelligent knowledge to develop the whole process. Finally we design a new medical hall for workshop of meta-synthetic engineering for helping patient to get the appropriate treatment and useful knowledge for health. © 2022 The Authors. Published by Elsevier B.V.

关键词： Patient treatment

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Impact of Consumer Perceived Characteristics on Fruit Sales: Evidence from Community Group-buying 11

Impact of Consumer Perceived Characteristics on Fruit Sales:...

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

作者： Jiang, Hailiang Tian, Xin Tao, Ye Wang, Lulu 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 Beijing HAOLINJU CVS Co. Ltd Beijing100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers;fresh produce stores outperform regular stores in fruit sales of CGB;Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels. © 2024 The Authors.

关键词： community group-buying dark-colored fruits fresh produce stores subjectivity

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Linear Twin SVM for Learning from Label Proportions

Linear Twin SVM for Learning from Label Proportions

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Bo Wang Zhensong Chen Zhiquan Qi The Key Research Lab on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Management University of Chinese Academy of Sciences Beijing China

In this paper, we study the problem of learning from label proportions in which label information of data is provided in bag level. In this kind of problem, training data is grouped into various bags and only the proportions of positive instances is known. Inspired by proportion-SVM, we propose a new classification model based on twin SVM, which is also in a large-margin framework and only needs to solve two smaller problems. Avoiding making restrictive assumptions of the data, our model can learn the labels of every single instance based on group proportions information. In order to solve the non-convex problem in our new model, we propose an alternative algorithm to obtain the optimal solution efficiently. Also, we prove the effectiveness of our method in theoretical and experimental way.

关键词： Support vector machines Training Yttrium Linear programming Diseases Computational modeling Analytical models

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