Deep neural networks understand investors better

By Nader Mahmoudi, P. Docherty, Pablo Moscato. 2018

50 citations recorded by the source corpus.

Abstract Studies that seek to examine the impact of sentiment in financial markets have been affected by inaccurate sentiment measurement and the use of inappropriate data. This study applies state-of-the-art techniques from the domain-general sentiment analysis literature to construct a more accurate decision support system that generates demonstrable improvement in investor sentiment classification performance compared with previous studies. The inclusion of emojis is shown significantly improve sentiment classification in traditional algorithms. Moreover, deep neural networks with domain-specific word embeddings outperform the traditional approaches for the classification of investor sentiment. The approach to sentiment classification outlined in this paper can be applied in future empirical tests that examine the impact of investor sentiment on financial markets.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

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

In Electrical and Electronic Engineering

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

In Management Science and Operations Research

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

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, methodology): “[190] extracted tweets from StockTwits to identify the user sentiment.”