Winning by Following the Winners: Mining the Behaviour of Stock Market Experts in Social Media

2014 ◽  
pp. 103-110 ◽  
Author(s):  
Wenhui Liao ◽  
Sameena Shah ◽  
Masoud Makrehchi
Keyword(s):  
Social Media ◽  
Stock Market

How Social Media Impacts Mass Media News Viewership in the Stock Market

2019 ◽  
Author(s):  
Jie Ren ◽  
Hang Dong ◽  
Gaurav Sabnis ◽  
Jeffrey V. Nickerson
Keyword(s):  
Social Media ◽  
Mass Media ◽  
Stock Market

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Social Media, News Media and the Stock Market

2016 ◽  
Author(s):  
Peiran Jiao ◽  
Ansgar Walther
Keyword(s):  
Social Media ◽  
Stock Market ◽  
News Media

scholarly journals Predicting Stock Market Movements with Social Media and Machine Learning

2021 ◽  
Author(s):  
Paraskevas Koukaras ◽  
Vasiliki Tsichli ◽  
Christos Tjortjis
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Stock Market

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%.

Beyond negative and positive: Exploring the effects of emotions in social media during the stock market crash

2020 ◽  
Vol 57 (4) ◽  
pp. 102218 ◽  
Author(s):  
Yidi Ge ◽  
Jiangnan Qiu ◽  
Zhiyong Liu ◽  
Wenjing Gu ◽  
Liwei Xu
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Stock Market Crash

Follow the money: A study of cashtags on Twitter

First Monday ◽  
2014 ◽  
Author(s):  
Martin Hentschel ◽  
Omar Alonso
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Market Performance ◽  
Exploratory Analysis ◽  
Financial Information ◽  
Stock Market Performance ◽  
Trending Topics ◽  
Ticker Symbols

The popularity of Twitter goes beyond trending topics, world events, memes, and popular hashtags. Recently a new way of sharing financial information is taking place in social media under the name of cashtags, stock ticker symbols that are prefixed with a dollar sign. In this paper we present an exploratory analysis of cashtags on Twitter. Specifically, we investigate how widespread cashtags are, what stock symbols are tweeted more often, and which users tweet about cashtags in general. We analyze relationships among cashtags and study hashtags in the context of cashtags. Finally, we compare tweet performance to stock market performance. We conclude that cashtags, in particular in combination with other cashtags or hashtags, can be very useful for analyzing financial information and provide new insights into stocks and companies.

Influence of Social Media over the Stock Market

2016 ◽  
Vol 34 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Juan Piñeiro-Chousa ◽  
Marcos Vizcaíno-González ◽  
Ada María Pérez-Pico
Keyword(s):  
Social Media ◽  
Stock Market
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