Time Series for Forecasting Stock Market Prices Based on Sentiment Analysis of Social Media

2021 ◽  
pp. 353-367
Author(s):  
Dakshinamoorthy Karthikeyan ◽  
Babu Aravind Sivamani ◽  
Pavan Kalyan Tummala ◽  
Chamundeswari Arumugam
Keyword(s):  
Social Media ◽  
Time Series ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Market Prices

scholarly journals Fractional differencing in stock market price and online presence of global tourist corporations

2019 ◽  
Vol 24 (48) ◽  
pp. 194-204 ◽  
Author(s):  
Francisco Flores-Muñoz ◽  
Alberto Javier Báez-García ◽  
Josué Gutiérrez-Barroso
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Market Price ◽  
Google Trends ◽  
Data Sets ◽  
Market Prices ◽  
Content Type ◽  
Search Results ◽  
Online Presence ◽  
Fractional Differencing

Purpose This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends. Design/methodology/approach The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations. Findings Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown. Originality/value This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

Predicting the French Stock Market Using Social Media Analysis

2015 ◽  
Vol 7 (2) ◽  
pp. 70-84
Author(s):  
Vincent Martin ◽  
Emmanuel Bruno ◽  
Elisabeth Murisasco
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Percentage Error ◽  
Social Media Analysis ◽  
Single Output ◽  
The Neural Network ◽  
Simple Neural Network

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.

scholarly journals Role of Social Sentiment Analysis in Stock Trends Forecasting

2019 ◽  
Vol 8 (2S5) ◽  
pp. 144-147
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Product Reviews ◽  
Stock Market Prediction ◽  
The People ◽  
Market Trends ◽  
Election Results ◽  
Analysis Of Social Networks

Social media like Face book, Twitter have attracted attention from various sectors of study in recent years. Most of the people share ideas, opinions on various topics such as Stock Market Prediction, Digital marketing, Movie review, Election Results Prediction and Product reviews etc,. Forecasting Financial Market is considered to be one of the significant applications of Sentiment Analysis on Social Data like Face book, Twitter. It is essential to accurately predict the movements in stock trends, as the stock market trends are volatile. In the past few years several researches have been carried out for predicting the future trends of stock market through sentiment analysis on social media comments. This paper gives the survey on the various techniques, tools and methodologies adopted by several researchers on Stock Market Prediction based on sentiment analysis of Social networks

scholarly journals Learning-Based Stock Market Trending Analysis by Incorporating Social Media Sentiment Analysis

2021 ◽  
Author(s):  
Zhaoxia Wang ◽  
Zhenda HU ◽  
Fang LI ◽  
Seng-Beng HO
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Prediction Performance ◽  
Support Vector ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Extreme Gradient Boosting ◽  
Media Sentiment

Abstract Stock market trending analysis is one of the key research topics in financial analysis. Various theories once highlighted the non-viability of stock market prediction. With the advent of machine learning and Artificial Intelligence (AI), more and more efforts have been devoted to this research area, and predicting the stock market has been demonstrated to be possible. Learning-based methods have been popularly studied for stock price prediction. However, due to the dynamic nature of the stock market and its non-linearity, stock market prediction is still one of the most dificult tasks. With the rise of social networks, huge amount of data is being generated every day and there is a gaining in popularity of incorporating these data into prediction model in the effort to enhance the prediction performance. Therefore, this paper explores the possibilities of the viability of learning-based stock market trending prediction by incorporating social media sentiment analysis. Six machine learning methods including Multi-Layer Perception, Support Vector Machine, Naïve Bayes, Random Forest, Logistic Regression and Extreme Gradient Boosting are selected as the baseline model. The result indicates the possibilities of successful stock market trending prediction and the performance of different learning-based methods is discussed. It is discovered that the distribution of the value of stocks may affect the prediction performance of the methods involved. This research not only demonstrates the merits and weaknesses of different learning-based methods, but also points out that incorporating social opinion is a right direction for improving the performance of stock market trending prediction.

scholarly journals Information Theoretic Causality Detection between Financial and Sentiment Data

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 621
Author(s):  
Roberta Scaramozzino ◽  
Paola Cerchiello ◽  
Tomaso Aste
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Stock Price ◽  
Transfer Entropy ◽  
Market Prices ◽  
Causal Information ◽  
Information Theoretic ◽  
P Index ◽  
Media Sentiment

The interaction between the flow of sentiment expressed on blogs and media and the dynamics of the stock market prices are analyzed through an information-theoretic measure, the transfer entropy, to quantify causality relations. We analyzed daily stock price and daily social media sentiment for the top 50 companies in the Standard & Poor (S&P) index during the period from November 2018 to November 2020. We also analyzed news mentioning these companies during the same period. We found that there is a causal flux of information that links those companies. The largest fraction of significant causal links is between prices and between sentiments, but there is also significant causal information which goes both ways from sentiment to prices and from prices to sentiment. We observe that the strongest causal signal between sentiment and prices is associated with the Tech sector.

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