Exploring propagation factors of social media moods for stock prices prediction

2020 ◽  
Vol 18 (3) ◽  
pp. 191-204
Author(s):  
Hongxun Jiang ◽  
Xiaotong Wang ◽  
Mengjun Zhu
Keyword(s):  
Social Media ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Release Time ◽  
Lag Phase ◽  
Integrated Framework ◽  
Financial Prediction ◽  
Dissemination Process ◽  
Simple Neural Network

Weibo, the most widely-used social media in China, makes researchers highly regard its profound impact in public and gather moods for social computing and analysis, such as financial prediction. Most existing literatures concern excessively on text semantic or sentiment mining techniques, but neglect the procedure of moods dissemination and its factors. This paper proposes an integrated framework of social media moods mining, which creatively focuses on information transmission and propagating factors analysis, to predict stock prices more accurately. For the part of propagating factors on social media, several essential factors are distinguished in the dissemination process, such as emotional absorption of forwarding, influence of content and poster, user categories, release time, etc. to optimize the fitting effect of original model. And the count of forwarding also matters on predicting stock prices. Searching a given finance-related keyword, from Weibo we collected over 500,000 micro-blogs and their user information. Then we adopt the proposed integrated framework to predict stock price fluctuation, as well as the simple neural network method. Experiments demonstrate that the former outperformed the latter. The results also show that user categories and the count of forwarding differ on the lag phase of influence. And more, this paper studies the fitting effect of prediction models for different periods of the stock curve. The results indicate that the model works the best in the rising periods of stock prices curves, relatively well in the declining and the worst in the random fluctuating.

scholarly journals Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic

2021 ◽  
Vol 25 ◽  
pp. 567-582
Author(s):  
Muhammad Ramadhani Kesuma ◽  
Felisitas Defung ◽  
Anisa Kusumawardani
Keyword(s):  
Prediction Model ◽  
Strong Evidence ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Business Sector ◽  
The World ◽  
Hotel Sector

As COVID-19 pandemic hit the world since early 2020, one business sector in many countries that struggling to survive is tourism and its derivatives, such as restaurants and hotels.  This study aims to examine the accuracy of the Springate and Grover models in predicting bankruptcy, as well as the effect on stock prices of tourism, restaurant, and hotel sector in Indonesia. The results show that all sample tourism, restaurant, and hotel companies are bankrupt under the Springate model, whilst according to Grover's model the findings are varied during the study period. Furthermore, the Grover model is implied to be more accurate than the Springate model. The effect of both prediction models on stock price appears dissimilar. Springate's prediction model suggests a positive and significant effect on stock prices, whereas there is no strong evidence about the effect of Grover’s prediction model.

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 Do Blockchain Stocks Exhibit More Herding Behaviour than Traditional Stocks?

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Prakhar Goel ◽  
Abhishek Dev
Keyword(s):  
Social Media ◽  
Stock Returns ◽  
Automotive Industry ◽  
Stock Prices ◽  
Stock Price ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Price Movements ◽  
The Impact ◽  
Stock Price Movements

While the volatile behaviour of cryptocurrency is extensively studied, the stock market’s blockchain sector, which has not been given much attention in the academic world, operates very differently from traditional stock industries. The paper hypothesizes that blockchain stocks exhibit more herding behaviour than traditional stocks and uses quantitative data analysis techniques to study it. The automotive industry is taken as a representative of traditional stocks. Cross-Sectional Absolute Deviation, the academic standard for herding behaviour, is used as the primary comparative measure between blockchain and automotive stocks. It reveals that blockchain industry has significant herding, while rational pricing mechanisms prevail in the automotive industry. Supporting this conclusion, a correlation matrix of stock prices of small market capitalisation firms in each industry is constructed, analysing how closely stock price movements in an industry are related. The correlation coefficient for blockchain stocks is 20% higher than the coefficient for automotive stocks. This indicates that blockchain stocks likely exhibit higher levels of herding. The impact of social media on stock price movements in the two industries is analysed by conducting a correlation study between Google Trends data for industry-related keywords and individual stock returns. The blockchain industry saw a significantly higher correlation, likely suggesting that social media has a stronger influence on blockchain stock price movements. Finally, the paper provides possible explanations for why herding behaviour is more prominent in the blockchain stocks compared to traditional stocks. These include absence of traditional stock valuation metrics, lack of financial knowledge and role of social media.

scholarly journals A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

2019 ◽  
pp. 477-484
Author(s):  
Yahui Chen ◽  
Zhan Wen ◽  
Qi Li ◽  
Yuwen Pan ◽  
Xia Zu ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Quantitative Data ◽  
Prediction Models ◽  
Investor Behavior ◽  
Prediction Approach ◽  
The Impact

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price ,the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.

scholarly journals Applying of a Company’s Stock Price Prediction Using Data Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 2847-2850
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Mining Machine ◽  
Support Vector ◽  
Product Reviews ◽  
Stock Price Prediction

Stock market analysis is a common economic activity that has been an attractive topic to research and used in different forms of day-to-day life in order to predict the stock prices. Techniques like major analysis, Statistical investigation, Time arrangement analysis and so on are reliably worthy forecast device. In this paper, Data mining, Machine learning (ML) and Sentiment analysis are techniques used for analyzing public emotions in order predict the future stock prices. The goal of a project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. Sentiment classifiers are designed for social media text like product reviews, blog posts, and email corpus messages. In the company’s communication network, information mining calculation is utilized as to mine email correspondence records and verifiable stock costs. Implementing various Machine learning and Classification models such as Deep Neural network, Random forests, Support Vector Machine, the company can successfully implemented a company-specific model capable of predicting stock price movement with efficient accuracy

scholarly journals When the Going Gets Tough, The Tweets Get Going! An Exploratory Analysis of Tweets Sentiments in the Stock Market

2018 ◽  
Vol 18 (5) ◽  
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Subject Matter ◽  
Stock Prices ◽  
Stock Price ◽  
Asset Prices ◽  
Exploratory Analysis ◽  
Information Networks ◽  
Digital Information ◽  
Stock Performance

Information artifacts in social media can have significant impact on domains and subject matter opinions. Asset prices, stock prices and volumes, and metrics are influenced by turbulence in their information ecosystems. Analyses of digital information networks and social media information events have shown information artifacts to affect stock performance without consistent correlation to fundamentals. Thus it becomes important to dispel ambiguity and gain insights into how the sentiments associated with information artifacts in Twitter (due to its extensive usage in relevant signaling), are associated with equity movements. Using an exploratory analysis to study tweet sentiment, we link stock price variations, and explore associated metrics. Our tweets analytics deploying textual analytics, identifies forms in tweet behavior and sentiment shifts.

scholarly journals Investor Sentiment Combined with Multisource Information to Predict Stock Prices: An Analysis of China’s A-Share Market

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Huang ◽  
Huilin Song
Keyword(s):  
Social Media ◽  
Time Series ◽  
News Media ◽  
Multilayer Perceptron ◽  
Stock Prices ◽  
Stock Price ◽  
Investor Sentiment ◽  
Prediction Method ◽  
Stock Price Prediction ◽  
Price Prediction

Investor sentiment has been widely used in the research of the stock market, and how to accurately measure investor sentiment is still being explored. With the rise of social media, investor sentiment is no longer only influenced by macroeconomic data and news media, but also guided by We-Media and fragmented information. We take the data of China A-shares from January 2020 to December 2020 as the research object and propose a stock price prediction method that combines investor sentiment with multisource information. Firstly, the sentiment of macroeconomic data, brokerage research reports, news, and We-Media is calculated, respectively, and then the investor sentiment vector combining multisource information is obtained by the multilayer perceptron. Finally, the LSTM model is used to represent the stock time series characteristics. The results show that (1) the proposed algorithm is superior to the benchmark algorithm in terms of accuracy and F1-score, (2) investor sentiment vector can effectively measure the investment sentiment of stocks, and (3) compared with vector concatenation, multilayer perceptron can better represent investor sentiment.

scholarly journals Importance of Event Binary Features in Stock Price Prediction

2020 ◽  
Vol 10 (5) ◽  
pp. 1597 ◽  
Author(s):  
Yoojeong Song ◽  
Jongwoo Lee
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Training Data ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Stock Investment ◽  
Binary Features

In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has not been established, and it has not been confirmed that the developed stock prediction model can actually result in a profit. To date, designing a good deep learning model depends on how well the user can extract the features that represent all the characteristics of the training data. Among the various available features for training and test data, we determined that the use of event binary features can make stock price prediction models perform better. An event binary feature refers to a 0 or 1 value describing whether an indicator is satisfied (1) or not (0) for any given day and stock. We proposed and compared a stock price prediction model with three different feature combinations to verify the importance of binary features. As a result, we derived a prediction model that defeated the market (KOSPI and KODAQ (KOSPI (Korea Composite Stock Price Index) and KOSDAQ (Korean Securities Dealers Automated Quotations) is Korean stock indices)). The results suggest that deep learning is suitable for stock price prediction.

Social Media-Based Forecasting

2016 ◽  
Vol 28 (2) ◽  
pp. 74-91 ◽  
Author(s):  
Wu He ◽  
Lin Guo ◽  
Jiancheng Shen ◽  
Vasudeva Akula
Keyword(s):  
Social Media ◽  
Stock Prices ◽  
Financial Services ◽  
Stock Price ◽  
Financial Service ◽  
Social Media Data ◽  
Service Companies ◽  
Positive Sentiment ◽  
Media Data ◽  
Significant Attention

Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors' findings indicate, in the financial services context, negative sentiments predict firms' future stock prices. However, the number of and the positive sentiment of tweets are not correlated with stock prices. The findings of this paper suggest the possible predictive value of social media data on stock prices at the company level.

scholarly journals Applications of neural network based methods on stock market prediction: survey

2018 ◽  
Vol 7 (2.6) ◽  
pp. 71 ◽  
Author(s):  
Avilasa Mohapatra ◽  
Smruti Rekha Das ◽  
Kaberi Das ◽  
Debahuti Mishra
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Optimization Techniques ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Statistical Measures ◽  
Financial Application

Financial forecasting is one of the domineering fields of research, where investor’s money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann’s development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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