SENN: Stock Ensemble-based Neural Network for Stock Market Prediction using Historical Stock Data and Sentiment Analysis

Owen, Louis; Oktariani, Finny

doi:10.1109/icodsa50139.2020.9212982

Cited by 6 publications

(2 citation statements)

References 16 publications

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“… Owen & Oktariani (2020) assess the idea of utilizing past data and sentiment evaluations derived from microblog language data to improve stock market prediction accuracy. The sentiment score is extracted using an ensemble-based approach that leverages the capability of CNN, MLP, and LSTM.…”

Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

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Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.

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Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

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show abstract

“…Market sentiment derived from news can reflect investor reactions to various events and the latest information. Previous studies have shown that integrating sentiment data with historical data can significantly improve the accuracy of stock price predictions [1].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Diffusion Variational Autoencoder for Stock Price Prediction with the Integration of Historical and Market Sentiment Data

Ardisurya,

Rizkinia

2024

IJECBE

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This study aims to predict stock prices using a Diffusion Variational Autoencoder (D-VAE) model that integrates technical data and market sentiment. Technical data is obtained from historical stock prices and trading volume, while sentiment data is derived from financial news analyzed using the IndoBERT model for sentiment classification. The research findings indicate that the integration of sentiment data in the D-VAE model enhances the accuracy of stock price predictions compared to a model that uses only technical data. Model evaluation is conducted using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²). The model with sentiment data integration has an MSE of 2753.204, MAE of 42.751, and R² of 0.94489, which are better than the model without sentiment data integration. This study demonstrates that the use of sentiment analysis can significantly contribute to improving stock price prediction performance using machine learning technology.

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Non‐Intrusive Load Monitoring Using A Parallel Bidirectional Long Short‐Term Memory Model

Andrean

Lian

2022

Intelligent Data Mining and Analysis in Power and Energy Systems

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SENN: Stock Ensemble-based Neural Network for Stock Market Prediction using Historical Stock Data and Sentiment Analysis

Cited by 6 publications

References 16 publications

A systematic literature survey on recent trends in stock market prediction

A systematic literature survey on recent trends in stock market prediction

Implementation of Diffusion Variational Autoencoder for Stock Price Prediction with the Integration of Historical and Market Sentiment Data

Non‐Intrusive Load Monitoring Using A Parallel Bidirectional Long Short‐Term Memory Model

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