Sentiment Predictability for Stocks

By Jordan Prosky, Xingyou Song, Andrew Tan, Michael Zhao. 2017

15 citations recorded by the source corpus.

In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.

Subfields: Artificial Intelligence, Management Science and Operations Research, Sentiment & Opinion Analysis, Economics & 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 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 Economics & Operations Research

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

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In [207], sentiment/mood prediction and price prediction based on sentiment, price prediction with text mining and DL models (LSTM, NN, CNN) were used for trend forecasting.”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[156] performed sentiment, mood prediction using news from Reuters and used these sentiments for price prediction.”