Shanghai Key Laboratory of Financial Information Technology

Shanghai, China

Shanghai Key Laboratory of Financial Information Technology

Shanghai, China
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Wang G.,Hefei University of Technology | Wang G.,Key Laboratory of Process Optimization and Intelligent Decision making | Wang G.,City University of Hong Kong | Zheng D.,Room 208 | And 4 more authors.
Information Systems and e-Business Management | Year: 2017

Opinion mining aiming to automatically detect subjective information has raised more and more interests from both academic and industry fields in recent years. In order to enhance the performance of opinion mining, some ensemble methods have been investigated and proven to be effective theoretically and empirically. However, cluster based ensemble method is paid less attention to in the area of opinion mining. In this paper, a new cluster based ensemble method, FCE-SVM, is proposed for opinion mining from social media. Based on the philosophy of divide and conquer, FCE-SVM uses fuzzy clustering module to generate different training sub datasets in the first stage. Then, base learners are trained based on different training datasets in the second stage. Finally, fusion module is employed to combine the results of based learners. Moreover, the multi-domain opinion datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that FCE-SVM gets the best performance through reducing bias and variance simultaneously. These results illustrate that FCE-SVM can be used as a viable method for opinion mining. © 2017 Springer-Verlag GmbH Germany


Du N.,Shanghai University of Finance and Economics | Du N.,Key Laboratory of Mathematical Economics SUFE | Huang H.,Shanghai Key Laboratory of Financial Information Technology | Li L.,Shanghai University of Finance and Economics | Li L.,Key Laboratory of Mathematical Economics SUFE
Decision Support Systems | Year: 2013

Consumer ratings are crucial in creating and sustaining trust and trustworthiness in e-commerce markets. Thus, it is important to know whether online trading can survive bad mouthing among participants. We use controlled lab experiments to test whether market efficiency (measured by the percentage of successful trades) is affected by unfair negative ratings, and whether announcing the percentage of unfair ratings in the market makes any difference. We find that market efficiency is higher when rating information is provided than when no rating information is provided, even when unfair and ambiguous ratings are present. We also find that buyers behave differently when unfair rating information exists; however, no matter whether the percentage of unfair ratings is known, market efficiency is not significantly different from that in the market without unfair ratings. © 2012 Elsevier B.V.


Han J.,Shanghai University of Finance and Economics | Han J.,Shanghai Key Laboratory of Financial Information Technology | Cao Y.,Shanghai University of Finance and Economics
Complex Systems and Complexity Science | Year: 2017

We build an interbank network model based on risk-averse behaviors. On a heterogeneous network structure, we explore the relationship between risk-averse behaviors of banks and systemic risk contagion, specifically, liquidity hoarding, fire sales behaviors and the composition of risk-averse behaviors. The simulation results show that liquidity hoarding behaviors mitigate systemic risk contagion at early stage, fire sales behaviors have little effect on mitigating systemic risk contagion and the composition of risk-averse behaviors exacerbate the systemic risk contagion. Heterogeneous network is more robust than the homogenous network if risk-averse behaviors exist, otherwise the homogeneous network is more stable. Furthermore, bank asset heterogeneity has no significant effect on systemic risk contagion. © 2017, The Journal of Agency of Complex Systems and Complexity Science. All right reserved.


Huang H.,Shanghai University of Finance and Economics | Huang H.,Shanghai Key Laboratory of Financial Information Technology | Li Y.,Shanghai University of Finance and Economics | Li Y.,Shanghai Key Laboratory of Financial Information Technology | And 2 more authors.
Information Systems and e-Business Management | Year: 2017

In China, the Growth Enterprise Market (GEM) is a brand new market that provides an additional way of financing for firms with good growth potential. Whereas there has been a massive amount of stocks breaking on the first trading day since 2010, the risk of IPO’s overpricing in China’s GEM has drawn more and more attention in recent years. Based on the theory of behavioral finance and limited attention, investors’ attention may be a quite indicative determinant to the IPO overpricing. Accordingly, we collected data from Internet including online stock forums and search engines, then built multiple investors’ attention metrics that were distinct to the traditional metrics from the offline stock market. In the empirical study, we built regression models to dig out the determinants of IPO’s overpricing and found that the hybrid model containing both online metrics and financial metrics outperformed the others considerably. The adjusted R-square of the hybrid model containing both online metrics and financial metrics is as high as 82.8%, in contrast to 18.2% for the model containing only the financial metrics and 59.3% for that containing only investors’ attention. © 2017 Springer-Verlag GmbH Germany


Zhang T.,Shanghai University of Finance and Economics | Zhang T.,Shanghai Key Laboratory of Financial Information Technology | Zheng Q.P.,University of Central Florida | Fang Y.,West Virginia University | Zhang Y.,Fudan University
Computers and Industrial Engineering | Year: 2015

This paper proposes a nonlinear integer programming model which co-optimizes the multi-level inventory matching and order planning for steel plants while combining Make-To-Order and Make-To-Stock policies. The model considers order planning and inventory matching of both finished and unfinished products. It combines multiple objectives, i.e., cost of earliness/tardiness penalty, tardiness penalty within delivery time window, production cost, inventory matching cost, and order cancelation penalty. This paper also proposes an improved Particle Swarm Optimization (PSO) method, where strategies to repair infeasible solutions and inventory-rematching scheme are introduced. Parameters of PSO and the rematching scheme are also analyzed. Three sets of real data from a steel manufacturing company are used to perform computational experiments for PSO, local search, and improved PSO. Numerical results show the validity of the model and efficacy of the improved PSO method. © 2015 Elsevier Ltd. All rights reserved.


Huang Y.,Tongji University | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | Wu Q.,Tongji University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Home energy management system is an important part in smart home, smart home is assumed to be equipped with smart meter which smart control of generators, storages, and demand response programs. In this paper, we study a versatile convex optimization framework for the automatic energy management of various household loads in a smart home. The scheduling algorithm determines how energy resources available to the end-users considering a number of constraints. Hence, a hybrid PSO-DE algorithm approach is proposed. We devise a model accounting for a typical household user, and present computational results showing that it can be efficiently solved in real-life instances. © Springer International Publishing Switzerland 2014.


Guo Y.,Tongji University | Kang Q.,Tongji University | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | Wu Q.,Tongji University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper presents a multivariable grey neural network (MGM-NN) model for predicting the state of industrial equipments. It combines the merit of MGM model and RBF-NN model on time series forecast. This mode takes the dynamic correlations among multi variables and environment’s impact on state of equipment into consideration. The proposed approach is applied to the melt channel state forecast. The results are contrasted to MGM model executed on the same test set. The results show the accuracy and promising application of the proposed model. © Springer International Publishing Switzerland 2014.


Huang Y.,Tongji University | Huang Y.,De Zhou Vocational and Technical College | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | And 2 more authors.
Electric Power Components and Systems | Year: 2016

This article describes a method for establishing an appliance scheduling scheme that can optimally coordinate a group of appliances in a consumer's premises. Finite-horizon scheduling optimizations are formulated to schedule the operation of appliances using a modeled predictive control method that incorporates both forecasts and newly updated information. A complex mixed discrete-continuous non-linear model is here in established, and a novel algorithm that hybridizes particle swarm optimization and constraint handing methods is proposed to derive optimum solutions within a limited computational time. Simulation results showed that the proposed algorithm can efficiently resolve real-life instances and can be embedded in resource limited devices. © Taylor & Francis Group, LLC.


Gong J.,Shanghai Key Laboratory of Financial Information Technology | Lu C.,Shanghai Key Laboratory of Financial Information Technology | Liu X.,Shanghai Key Laboratory of Financial Information Technology
Lecture Notes in Electrical Engineering | Year: 2014

Demand forecasting of auto parts is important for the management of the auto supply chain. After comprehensively exploring the characteristic of the auto parts and considering the strengths of some individual forecasting methods, this article chose ARIMA, SVR and RBF neutral network based regression to develop a nonnegative variable weight combination model to forecast demand of auto parts for the auto aftermarket. This model improved accuracy and stability and had wider applicability. © 2014 Springer-Verlag Berlin Heidelberg.


Shi L.,China University of Petroleum - East China | Shi L.,Shanghai Key Laboratory of Financial Information Technology | Jia C.,China University of Petroleum - East China | Gong J.,Shanghai Key Laboratory of Financial Information Technology | And 2 more authors.
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | Year: 2016

Concerning the resource-limited RFID tags, this paper presents a lightweight mutual authentication scheme based on Hash function, combining with the pseudo-random number and shared secret mechanisms, and implements the mutual authentication among the end database, reader and the tags. The anti-attack performance and the overhead of the scheme are analyzed in detail. Afterwards, the protocol security model is formalized using BAN logical analysis method. Theoretical analysis shows that the proposed authentication scheme could achieve the desired security goals, has good anti-attack performance and high efficiency. It can be applied to big population RFID since its low overhead for RFID tags. © 2016, Science Press. All right reserved.

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