Automated news reading: Stock price prediction based on financial news using context-capturing features

2013 ◽  
Vol 55 (3) ◽  
pp. 685-697 ◽  
Author(s):  
Michael Hagenau ◽  
Michael Liebmann ◽  
Dirk Neumann
Keyword(s):  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News

scholarly journals Applying LSTM for Stock Price Prediction with Sentiment Analysis

2021 ◽  
Author(s):  
Alexandre Heiden ◽  
Rafael Stubs Parpinelli
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Market Volatility ◽  
Analysis Model ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News ◽  
Social Media Platforms

Financial news has been proven to be valuable source of information for the evaluation of stock market volatility. Most of the attention has been given to social media platforms, while news from vehicles such as newspapers are not as widely explored. Newspapers provide, although in a smaller volume, more reliable information than social media platforms. In this context, this research aims to examine the influence of financial news within the stock price prediction problem, by using the VADER sentiment analysis model to process the news and feed the sentiments as a feature into a LSTM-based stock price prediction model, along with the historical data of the assets. Experiments indicate that the model has better results when the news’ sentiments are considered, and the model demonstrates potential to accurately predict stock prices up to around 60 days into the future.

scholarly journals A SURVEY ON STOCK PRICE PREDICTION USING MACHINE LEARNING

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Manavi Mishra ◽  
Manjushree Patil ◽  
Geetanjali Raut ◽  
Tushar Chaudhari
Keyword(s):  
Machine Learning ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
Machine Learning Techniques ◽  
Annual Income ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News ◽  
Stock Investments

Stock returns are very fluctuating in nature. They rely upon various factors like previous stock prices, current market trends, financial news, etc. To feature their annual income, people have now started watching stock investments as a remunerative option. There are many tools available to investors using technical analysis to form decisions. With expert guidance and intelligent planning, we will almost double our annual income through stock returns. These days, social media has become a mirror. It reflects people’s thoughts and opinions on any particular event or news. Sentiments of the general public associated with an organization can have an upshot on its stock prices. This paper surveys various machine learning techniques and algorithms employed to boost the accuracy of stock price prediction.

scholarly journals A Discrete Stock Price Prediction Engine Based on Financial News

Computer ◽  
2010 ◽  
Vol 43 (1) ◽  
pp. 51-56 ◽  
Author(s):  
Robert P. Schumaker ◽  
Hsinchun Chen
Keyword(s):  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News

Using SVM to Predict Stock Price Changes from Online Financial News

2012 ◽  
Vol 157-158 ◽  
pp. 1586-1590
Author(s):  
Shu Yan Dai ◽  
Ning Li
Keyword(s):  
Text Analysis ◽  
Stock Price ◽  
Online News ◽  
Support Vector ◽  
Prediction Problem ◽  
Price Changes ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News ◽  
Financial Indices

Many technical analysis use financial indices to predict stock price changes. In this paper, we present a different approach for prediction stock price fluctuations using financial news. Our method approaches the stock price prediction problem from an information retrieval perspective. We apply both text analysis and pattern classification techniques to search for important online news that are relevant for stock price changes. First, the online financial news and the corresponding stocks are extracted. Then we apply Support Vector Machine (SVM) to construct a model that predicts the price changes for the stocks. Finally, the stock changes prediction model is used to classify and extract upcoming important financial news. The experimental results demonstrate our method is effective for seeking the important financial news for stock price changes.

scholarly journals Neural Network on Stock Prediction using the Stock Prices Feature and Indonesian Financial News Titles

2021 ◽  
Vol 7 (1) ◽  
pp. 54-63
Author(s):  
Nur Ghaniaviyanto Ramadhan ◽  
Imelda Atastina
Keyword(s):  
Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Multi Layer Perceptron ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News ◽  
Machine Learning Model ◽  
The Right ◽  
Bahasa Indonesia

Stocks are the most popular investments among entrepreneurs or other investors. When investing in stocks these investors tend to learn how to invest stocks correctly and when is the right time. For the problem of how to invest shares correctly can be used a variety of basic theories that already exist, but for the problem when the right time needs further learning. In this paper will purpose about stock price prediction using stock data indicators and financial headline data in Bahasa Indonesia. The machine learning model used is a multi-layer perceptron neural network (MLP-NN) with the highest accuracy produced by 80%.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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