A Tensor-Based Information Framework for Predicting the Stock Market

By Qing Li, Yuanzhu Peter Chen, LiLing Jiang, Ping Li, Hsinchun Chen. 2016

90 citations recorded by the source corpus.

Subfields: Computational Mathematics, Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mathematics

1. Philip S. Yu
2. Tat-seng Chua
3. Weiwei Jiang
4. Senzhang Wang
5. Xiangnan He
6. A. Ozbayoglu
7. Omer Berat Sezer
8. M. Ugur Gudelek

In Electrical and Electronic Engineering

1. Philip S. Yu
2. Tat-seng Chua
3. Weiwei Jiang
4. Juho Kanniainen
5. Alexandros Iosifidis
6. Martin Magris
7. Xiangnan He
8. Moncef Gabbouj

In Management Science and Operations Research

1. Weiwei Jiang
2. Xiangnan He
3. Xiang Wang
4. Tat-seng Chua
5. Fuli Feng
6. Yuntao Yao
7. Philip S. Yu
8. Yiqun Liu

Citations in the corpus

• Improving stock market prediction via heterogeneous information fusion (2017, background): “…a tensor-based computational framework has been developed to model the joint impacts of different information sources for stock prediction [12, 13], each stock prediction in that framework is modeled as an individual task and thus learned independently, regardless of the correlations among…”
• Tensor representation in high-frequency financial data for price change prediction (2017, background): “However, there are few works investigating the performance of the tensor-based multilinear methods in financial problems [28].”
• Applications of deep learning in stock market prediction: recent progress (2020, methodology): “D CNN DNN, SVM Deng et al. (2019) KDTCN ARIMA, LSTM, CNN, TCN Sezer & Ozbayoglu (2019) CNN Buy&Hold Sim et al. (2019) CNN ANN, SVM Recurrent Neural Network Type Huynh et al. (2017) BGRU LSTM, GRU, DNN (Ding et al., 2014), DNN (Peng & Jiang, 2016) Li & Tam (2017) WT+LSTM LSTM Li et al. (2017a) LSTM S”
• Temporal Relational Ranking for Stock Prediction (2018, background): “Some recent evidences indicate the predictability of stock markets, which motivates further exploration of stock prediction techniques [17, 23, 36, 42].”