Exploiting Twitter Moods to Boost Financial Trend Prediction Based on Deep Network Models

By Yifu Huang, Kai Huang, Yang Wang, H. Zhang, Jihong Guan, Shuigeng Zhou. 2016

17 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Sentiment & Opinion Analysis, Time-Series & Forecasting

How researchers see this paper

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

In Artificial Intelligence

1. Omer Berat Sezer
2. M. Ugur Gudelek
3. A. Ozbayoglu

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

In Sentiment & Opinion Analysis

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[201] S&P500, NYSE Composite, DJIA, NASDAQ Composite 2009-2011 Twitter moods, index data 7d 1d DNN, CNN Error rate Keras, Theano”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[158] used several models including Hidden Markov Model (HMM), DMLP and CNN using Twitter moods along with the financial price data for prediction of the next day’s move (up or down).”