most citedImproving Stock Market Prediction via Heterogeneous Information Fusion

210 citations · 278 across the 2 of their papers we have counts for

collaborators

6 papers

cs.SI2019

Scalable Explanation of Inferences on Large Graphs

Chao Chen, Yifei Liu, Xi Zhang +1

Probabilistic inferences distill knowledge from graphs to aid human make important decisions. Due to the inherent uncertainty in the model and the complexity of the knowledge, it i…

q-fin.ST2018

Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data

Xi Zhang, Yixuan Li, Senzhang Wang +2

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other inf…

cs.CR2018

The Coming Era of AlphaHacking? A Survey of Automatic Software Vulnerability Detection, Exploitation and Patching Techniques

Tiantian Ji, Yue Wu, Chang Wang +2

With the success of the Cyber Grand Challenge (CGC) sponsored by DARPA, the topic of Autonomous Cyber Reasoning System (CRS) has recently attracted extensive attention from both in…

cs.LG2018

A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction

Jieyun Huang, Yunjia Zhang, Jialai Zhang +1

The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model tha…

cs.CE201868 cited

Exploiting Investors Social Network for Stock Prediction in China's Market

Xi Zhang, Jiawei Shi, Di Wang +1

Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market…

cs.SI2018210 cited

Improving Stock Market Prediction via Heterogeneous Information Fusion

Xi Zhang, Yunjia Zhang, Senzhang Wang +3

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recen…