Download or read book Machine Learning Sentiment Analysis Covid 19 News and Stock Market Reactions written by Michele Costola and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the "Natural Language Toolkit" that uses machine learning models to extract the news' sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.

Download or read book Stock Prediction Using Natural Language Processing Sentiment Analysis on News Headlines During COVID 19 written by Mina Ibrahim and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Stock prediction based on NLP sentiment analysis is one of the most researched topics due to the revenues they generate for investors. Researchers have used various tools to achieve this, especially fundamental and technical analysis based on historical data helped to achieve this target. Due to the technological advancement and abundance of data, the introduction of machine learning tools accelerated that approach. However, as the public mood affects the stock market, the need for another analysis emerged. Natural language processing sentiment analysis on data from various sources was able to capture public events and moods. NLP is one of the most effective tools since covering the public moods, and capturing the sentiment is the main driver for stock markets. In this research, NLP sentiment analysis shall be applied to news to predict United States technology stock companies and indices during COVID-19 using a natural language toolkit. The contribution of this is the research is creating a model for predicting the technology companies listed in the United States market during the crisis. The model is achieving over 61% accuracy and could be highly improved by adding other resources of news.

Download or read book Coronavirus News Markets and AI written by Pankaj Sharma and published by Taylor & Francis. This book was released on 2020-12-27 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Coronavirus News, Markets and AI explores the analysis of unstructured data from coronavirus-related news and the underlying sentiment during its real-time impact on the world and on global financial markets, in particular. In an age where information - both real and fake - travels in the blink of an eye and significantly alters market sentiment daily, this book is a blow by blow account of economic impact of the COVID-19 pandemic. The volume: Details how AI driven machines capture, analyse and score relevant on-ground news sentiment to analyse the dynamics of market sentiment, how markets react to good or bad news across ‘short term’ and ‘long term’; Investigates what have been the most prevalent news sentiment during the pandemic, and its linkages to crude oil prices, high profile cases, impact of local news, and even the impact of Trump’s policies; Discusses the impact on what people think and discuss, how the COVID-19 crisis differs from the Global Financial Crisis of 2008, the unprecedented disruptions in supply chains and our daily lives; Showcases how easy accessibility to big data methods, cloud computing, and computational methods and the universal applicability of these tool to any topic can help analyse extract the related news sentiment in allied fields. Accessible, nuanced and insightful, this book will be invaluable for business professionals, bankers, media professionals, traders, investors, and investment consultants. It will also be of great interest to scholars and researchers of economics, commerce, science and technology studies, computer science, media and culture studies, public policy and digital humanities.

Download or read book Stock Market Prediction Through Sentiment Analysis of Social Media and Financial Stock Data Using Machine Learning written by Mohammad Al Ridhawi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Given the volatility of the stock market and the multitude of financial variables at play, forecasting the value of stocks can be a challenging task. Nonetheless, such prediction task presents a fascinating problem to solve using machine learning. The stock market can be affected by news events, social media posts, political changes, investor emotions, and the general economy among other factors. Predicting the stock value of a company by simply using financial stock data of its price may be insufficient to give an accurate prediction. Investors often openly express their attitudes towards various stocks on social medial platforms. Hence, combining sentiment analysis from social media and the financial stock value of a company may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models. We use an LSTM model for the financial stock prediction. The models are trained on the AAPL, CSCO, IBM, and MSFT stocks, utilizing a combination of the financial stock data and sentiment extracted from social media posts on Twitter between the years 2015-2019. Our experimental results show that the combination of the financial and sentiment information can improve the stock market prediction performance. The proposed solution has achieved a prediction performance of 74.3%.

Download or read book Computational Modeling and Data Analysis in COVID 19 Research written by Chhabi Rani Panigrahi and published by CRC Press. This book was released on 2021-05-09 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers recent research on the COVID-19 pandemic. It includes the analysis, implementation, usage, and proposed ideas and models with architecture to handle the COVID-19 outbreak. Using advanced technologies such as artificial intelligence (AI) and machine learning (ML), techniques for data analysis, this book will be helpful to mitigate exposure and ensure public health. We know prevention is better than cure, so by using several ML techniques, researchers can try to predict the disease in its early stage and develop more effective medications and treatments. Computational technologies in areas like AI, ML, Internet of Things (IoT), and drone technologies underlie a range of applications that can be developed and utilized for this purpose. Because in most cases there is no one solution to stop the spreading of pandemic diseases, and the integration of several tools and tactics are needed. Many successful applications of AI, ML, IoT, and drone technologies already exist, including systems that analyze past data to predict and conclude some useful information for controlling the spread of COVID-19 infections using minimum resources. The AI and ML approach can be helpful to design different models to give a predictive solution for mitigating infection and preventing larger outbreaks. This book: Examines the use of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and drone technologies as a helpful predictive solution for controlling infection of COVID-19 Covers recent research related to the COVID-19 pandemic and includes the analysis, implementation, usage, and proposed ideas and models with architecture to handle a pandemic outbreak Examines the performance, implementation, architecture, and techniques of different analytical and statistical models related to COVID-19 Includes different case studies on COVID-19 Dr. Chhabi Rani Panigrahi is Assistant Professor in the Department of Computer Science at Rama Devi Women’s University, Bhubaneswar, India. Dr. Bibudhendu Pati is Associate Professor and Head of the Department of Computer Science at Rama Devi Women’s University, Bhubaneswar, India. Dr. Mamata Rath is Assistant Professor in the School of Management (Information Technology) at Birla Global University, Bhubaneswar, India. Prof. Rajkumar Buyya is a Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia.

Download or read book Natural Language Processing and Financial Markets written by Carlos Moreno Pérez and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the reactions of US financial markets to press news from January 2019 to 1 May 2020. To this end, we deduce the content and sentiment of the news by developing apposite indices from the headlines and snippets of The New York Times, using unsupervised machine learning techniques. In particular, we use Latent Dirichlet Allocation to infer the content (topics) of the articles, and Word Embedding (implemented with the Skip-gram model) and K-Means to measure their sentiment (uncertainty). In this way, we arrive at the definition of a set of daily topic-specific uncertainty indices. These indices are then used to find explanations for the behaviour of the US financial markets by implementing a batch of EGARCH models. In substance, we find that two topic-specific uncertainty indices, one related to COVID-19 news and the other to trade war news, explain the bulk of the movements in the financial markets from the beginning of 2019 to end-April 2020. Moreover, we find that the topic-specific uncertainty index related to the economy and the Federal Reserve is positively related to the financial markets, meaning that our index is able to capture actions of the Federal Reserve during periods of uncertainty.

Download or read book A Sentiment Analysis of Spanish and Italian News Articles about COVID 19 written by Jordany Werzner Regalado and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Artificial Intelligence Applications and Innovations written by Ilias Maglogiannis and published by Springer Nature. This book was released on with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Comparison of Machine Learning and Deep Learning Models for Stock Sentiment Analysis Using News Headlines written by Rohan Gaikwad and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of its importance in the economy, the stock market prediction has always been a hot topic. In order to avoid investment risks, there is always an urgent need to uncover the stock market's future behavior. Before making a trading decision to buy or sell a stock, stock traders must be able to predict its behavior trends. The more accurately they predict the behavior of a stock, the more profit they make. However, determining stock market trends is a difficult task due to factors such as industry performance, company news, company performance, investor sentiment, economic variables, and, in particular, social media sentiment. As a result, reading the market's stock sentiment has become critical to making a sound investment. The purpose of this study is to examine previously used Machine Learning and Deep Learning models, explore new models, optimize them with better techniques, and compare them to determine which models are the most effective at predicting stock market sentiment. The SVM classifier performed best in my experiments, with a classification accuracy score of 91%, followed by the Passive-Aggressive classifier and the LSTM model, both of which achieved 90% accuracy, and then comes the Naive Bayes model, which achieved 89% accuracy.

Download or read book Stock Markets Reaction to Covid 19 written by Badar Nadeem Ashraf and published by . This book was released on 2020 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we examine the stock markets' response to the COVID-19 pandemic. Using daily COVID-19 confirmed cases and deaths and stock market returns data from 64 countries over the period January 22, 2020 to April 17, 2020, we find that stock markets responded negatively to the growth in COVID-19 confirmed cases. That is, stock market returns declined as the number of confirmed cases increased. We further find that stock markets reacted more proactively to the growth in number of confirmed cases as compared to the growth in number of deaths. Our analysis also suggests negative market reaction was strong during early days of confirmed cases and then between 40 to 60 days after the initial confirmed cases. Overall, our results suggest that stock markets quickly respond to COVID-19 pandemic and this response varies over time depending on the stage of outbreak.

Download or read book New Opportunities for Sentiment Analysis and Information Processing written by Aakanksha Sharaff and published by . This book was released on 2021 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a unique contribution to the various interdisciplinary fields of information retrieval and sentiment analysis, which are fueling the revolutionary growth of digital marketing and changes in the market game but also presents new opportunities for skilled professional skilled and expertise"--

Download or read book Artificial Intelligence and Speech Technology written by Amita Dev and published by Springer Nature. This book was released on 2022-01-28 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes selected papers presented at the Third International Conference on Artificial Intelligence and Speech Technology, AIST 2021, held in Delhi, India, in November 2021. The 36 full papers and 18 short papers presented were thoroughly reviewed and selected from the 178 submissions. They provide a discussion on application of Artificial Intelligence tools in speech analysis, representation and models, spoken language recognition and understanding, affective speech recognition, interpretation and synthesis, speech interface design and human factors engineering, speech emotion recognition technologies, audio-visual speech processing and several others.

Download or read book Stock Market Prediction Using Reinforcement Learning with Sentiment Analysis written by and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work creates a new Deep Q-learning model with augmented sentiment analysis and stock trend labelling (DQS model). It incorporates stock market trend label and sentiment analysis score label as input features to improve model accuracy and performance. The first part of this work proves that machine learning models can predict stock price trends instead of just accurate stock prices. It studies the performance difference between neural networks and other maching learning algorithm performance for stock price trend prediction. It shows that neural networks can accurately predict stock trends when stock price data are preprocessed and transformed into category data. Subsequently, this work utilizes Valence Aware Dictionary for Sentiment Reasoning (VADER) to predict the sentiment score of new titles. A correlation study shows that there is a strong correlation between stock price and and market daily sentiment. Lastly, a new neural network customized for this application has been utilized in the DQS model to map state features to action for trading decision making.

Download or read book Quantifying High frequency Market Reactions to Real time News Sentiment Announcements written by Axel Groß-Klußmann and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predicting the Stock Market Using News Sentiment Analysis written by Majid Memari and published by . This book was released on 2018 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. GDELT is the largest, most comprehensive, and highest resolution open database ever created. It is a platform that monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day that stretches all the way back to January 1st, 1979, and updates daily. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. On the other hand, other studies show that it is predictable. The stock market prediction has been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news. The main objective of this research is to investigate the accuracy of predicting the unseen prices of the Dow Jones Industrial Average using information derived from GDELT database. Dow Jones Industrial Average (DJIA) is a stock market index, and one of several indices created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. This research is based on data sets of events from GDELT database and daily prices of the DJI from Yahoo Finance, all from March 2015 to October 2017. First, multiple different classification machine learning models are applied to the generated datasets and then also applied to multiple different Ensemble methods. In statistics and machine learning, Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Afterwards, performances are evaluated for each model using the optimized parameters. Finally, experimental results show that using Ensemble methods has a significant (positive) impact on improving the prediction accuracy.

Download or read book Blockchain Applications for Smart Contract Technologies written by Derbali, Abdelkader Mohamed Sghaier and published by IGI Global. This book was released on 2024-04-09 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Blockchain technology has garnered much attention in recent years from both academic and business spheres. At its core, this technology enables the implementation of smart contracts, automated software applications that execute agreements on a secure and distributed blockchain ledger. This ledger, known for its transparency, facilitates trustless transactions, eliminating the need for centralized authority. Smart contracts, stored on the blockchain, automate processes such as goods sales, contract execution, and currency exchange, making them accessible to all users. Blockchain Applications for Smart Contract Technologies aims to present an exhaustive compilation of academic and industrial endeavors that advocate for the integration of blockchain and smart contracts in various sectors. Beyond offering a comprehensive understanding of blockchain and smart-contract fundamentals, the book seeks to spotlight specific research themes within these domains. With dedicated sections focused on applications in healthcare, finance, e-government, the Internet of Things (IoT), energy, identity, telecommunications, Metaverse, non-fungible tokens (NFTs), and notary services, the book becomes a valuable guiding resource for scholars and professionals alike. This book caters to scholars, researchers, and industry professionals that want to apply blockchain and smart-contract technologies in their fields.

Download or read book An Application of Sentiment Analysis with Transformer Models on Online News Articles Covering the Covid 19 Pandemic written by Prakul Asthana and published by . This book was released on 2021 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Covid-19 pandemic has had a devastating impact on lives across the world, with tremendous human socio-economic costs, while exposing and exacerbating several fault lines in our society. It has also caused a rapid rise in misinformation and erosion of trust in established news outlets amid allegations of political bias and censorship. In this paper we use the processes of sentiment analysis to study the coverage of the Covid-19 pandemic in news outlets. By comparing the coverage from news sources with opposing political leanings, we quantitatively establish political bias. We also repeat this process on news articles mentioning specific topics like Masks, Social Distancing etc., to check for any bias present in the sentiment towards them. Lastly, we compare sentiment in Covid-19 news coverage in the United States, the United Kingdom and Australia to contrast the political bias in news articles on the pandemic in these three countries.