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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Analysis of news sentiments using natural language processing and deep learning.

Mattia Vicari1, Mauro Gaspari2

  • 1University of Bologna, Bologna, Italy.

AI & Society
|December 7, 2020
PubMed
Summary

This study explores using deep learning (DL) to trade on news sentiment. Results indicate DL, specifically LSTM, can effectively forecast market sentiment for algorithmic trading strategies.

Keywords:
Deep learningMachine learningNLPNatural language processingSentiment analysisTradingTrading signalsTrading strategies

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Area of Science:

  • Computational finance
  • Natural Language Processing (NLP)
  • Machine Learning (ML)

Background:

  • This research examines the efficacy of deep learning (DL) models in analyzing news sentiment for financial market trading.
  • The study addresses the growing interest in leveraging AI for investment strategies, particularly DL's capability to identify complex patterns in large datasets.

Discussion:

  • Deep learning, especially Long Short-Term Memory (LSTM) networks, is evaluated for its potential in processing linguistic data and predicting market trends.
  • The methodology involves forecasting market sentiment using news headlines related to the Dow Jones Industrial Average from 2008 to 2020.

Key Insights:

  • The paper details the construction of DL models for sentiment analysis and their application in developing an algorithmic trading strategy.
  • The predictive capability of DL models is tested through two specific case studies over five time-steps in real-world scenarios.

Outlook:

  • Future research could explore broader market indices and diverse news sources to enhance the robustness of DL-driven trading strategies.
  • The findings suggest a promising avenue for integrating advanced machine learning techniques into quantitative finance for improved trading performance.