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Stock Market Prediction Through Sentiment Analysis Of Social Media And Financial Stock Data Using Machine Learning
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Book Synopsis Stock Market Prediction Through Sentiment Analysis of Social-Media and Financial Stock Data Using Machine Learning by : Mohammad Al Ridhawi
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%.
Book Synopsis Deep Learning Tools for Predicting Stock Market Movements by : Renuka Sharma
Download or read book Deep Learning Tools for Predicting Stock Market Movements written by Renuka Sharma and published by John Wiley & Sons. This book was released on 2024-05-14 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Book Synopsis Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network by : Joish Bosco
Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.
Book Synopsis Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data by : Andreas Holzinger
Download or read book Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data written by Andreas Holzinger and published by Springer. This book was released on 2013-06-26 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third Workshop on Human-Computer Interaction and Knowledge Discovery, HCI-KDD 2013, held in Maribor, Slovenia, in July 2013, at SouthCHI 2013. The 20 revised papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in topical sections on human-computer interaction and knowledge discovery, knowledge discovery and smart homes, smart learning environments, and visualization data analytics.
Book Synopsis Predicting the Stock Market Using News Sentiment Analysis by : Majid Memari
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.
Book Synopsis AI Stock Investing: Dividend Investing with Artificial Intelligence by : DIZZY DAVIDSON
Download or read book AI Stock Investing: Dividend Investing with Artificial Intelligence written by DIZZY DAVIDSON and published by Pure Water Books. This book was released on 2024-08-04 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you struggling to fully understand how AI can revolutionize your stock investing strategy? Do you find it challenging to keep up with the rapid advancements in AI technology and its applications in finance? Look no further! “AI Stock Investing: Dividend Investing with Artificial Intelligence” is your ultimate guide to harnessing the power of AI for smarter, more profitable investments. This book demystifies AI and provides you with practical insights and strategies to leverage AI in your dividend investing journey. Benefits of Reading This Book: Unlock the Potential of AI: Learn how AI algorithms can optimize your trading decisions and maximize your returns. Stay Ahead of the Curve: Understand the latest AI trends and technologies that are shaping the future of stock investing. Personalized Investment Strategies: Discover how AI can tailor investment advice to your unique financial goals and risk tolerance. Enhanced Risk Management: Utilize AI to detect and mitigate risks, ensuring a more secure investment portfolio. Fraud Detection: Protect your investments with AI’s advanced fraud detection capabilities. Why This Book is a Must-Read: This book is packed with actionable insights and real-world examples that make complex AI concepts accessible to everyone. Whether you’re a seasoned investor or just starting, you’ll find valuable information that can transform your approach to stock investing. The clear explanations and step-by-step guides will empower you to confidently apply AI techniques to your investment strategy. Bullet Points: Algorithmic Trading: Execute trades at optimal prices with AI. Sentiment Analysis: Predict stock movements by analyzing market sentiment. Portfolio Optimization: Create and manage investment portfolios with AI. Predictive Analytics: Forecast future stock prices using historical data. Automated Portfolio Building: Leverage robo-advisors for customized investment portfolios. Call to Action: Don’t miss out on the opportunity to revolutionize your investing strategy with AI. Get your copy of “AI Stock Investing: Dividend Investing with Artificial Intelligence” today and unlock the secrets to smarter, more profitable investments. Become knowledgeable about AI and take control of your financial future!
Book Synopsis Predicting Stock Price Using Sentiment Analysis Combining Twitter, Search Engine and Investor Intelligence Data by : Rui Wu
Download or read book Predicting Stock Price Using Sentiment Analysis Combining Twitter, Search Engine and Investor Intelligence Data written by Rui Wu and published by . This book was released on 2014 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: The stock markets in the recent years have become an integral part of the global economy, any fluctuation in this market influences our personal and corporate financial lives. A good prediction model for stock market forecasting is always highly desirable and would of wider interest. Recent research suggests that very early indicators can be extracted from online social media (blogs, Twitter feeds, etc.) to predict changes in various economic and commercial indicators. In this project, daily sentiment features are generated from a Twitter dataset to build up a high accuracy prediction model for stock price movement. Google Search Queries and Investor Intelligence provide additional features to improve performance on weekly based models. Five sentiment features (Mt-Positive, Mt-Negative, Bullishness, Message Volume, Agreement) are extracted from Twitter using sentiment analysis. Tweets that can express opinion upon stocks or indices are filtered out and classified from a Twitter dataset, which holds more than 400 million records from July 31 to December 31 2009. Four finance features (Return, Close, Trade Volume, Volatility) are generated for 2 Market Indices NASDAQ-100, Dow Jones Average Indices and 13 leading technological companies. Second step, correlations on each finance features with all other features are calculated to verify their statistically relationships. Results show high correlations (up to 0.93 for DJIA with Close) with stock prices and twitter sentiment. Twitter Sentiment may have time delay on stock prices movement, so time lag by weeks are also included in this experiments. Furthermore, with confidence from the correlations, several Machine Learning algorithms like Gaussian Process, Neural Network and Decision Stump are applied on the feature set. Results show reliable models are built with strong correlations and low Root Mean Square Error (R: 0.94, RMSE: 0.065). Finally, a real time prediction system is built with an additional component of Twitter Streaming API collecting real time Twitter data. Overall, the experimental results show that this prediction system is working with satisfiable efficiency and accuracy.
Book Synopsis Sentiment and Technical Analyses for Stock Market Forecasting Through Machine Learning by : Joshua Licudo
Download or read book Sentiment and Technical Analyses for Stock Market Forecasting Through Machine Learning written by Joshua Licudo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proliferation of social media combined with advancements in machine learning allows for researchers to easily capture a userbase's opinions on a given topic, and stock market sentiment is no exception to this. Although discussions on social media are typically informal and written by users who are not necessarily market experts, public platforms are still a reflection of public opinion. Acting contrary to the public, in line with the well-known adage "be greedy when others are fearful, and fearful when others are greedy," is nonetheless a proven strategy. This project explores existing tools and libraries for both sentiment and time series analyses, integrating both to apply a contrarian approach to stock trading. At first, a general approach analyzing stocks measured by the Dow Jones Industrial Average and discussions pertaining to them on Reddit was taken, but numerous flaws with this made the project pivot over to margin trading with popular stocks discussed on the subreddit r/wallstreetbets, especially given Reddit's recent notoriety after the 2021 GameStop short squeeze. Since tens of thousands of posts are submitted to r/wallstreetbets every day, optimal performance of all project components became a critical metric for practical viability. Two simulations were constructed using one day of one-minute interval data and five days of five-minute interval data to capture performance benchmarks for a sentiment analysis model using VADER, and two time series analysis models, one using ARIMA/GARCH and the other using LSTM, were compared for performance and accuracy. While the best overall results were observed using the ARIMA/GARCH model, poor performance scaling was observed in the sentiment analyzer, making it infeasible to execute simulations across timeframes longer than five days. Future work should focus on exploration of additional sentiment and time series analysis models and optimization of the sentiment analyzer.
Book Synopsis The Predictive Edge by : Alejandro Lopez-Lira
Download or read book The Predictive Edge written by Alejandro Lopez-Lira and published by John Wiley & Sons. This book was released on 2024-07-11 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use ChatGPT to improve your analysis of stock markets and securities In The Predictive Edge: Outsmart the Market Using Generative AI and ChatGPT in Financial Forecasting, renowned AI and finance researcher Dr. Alejandro Lopez-Lira delivers an engaging and insightful new take on how to use large language models (LLMs) like ChatGPT to find new investment opportunities and make better trading decisions. In the book, you’ll learn how to interpret the outputs of LLMs to craft sounder trading strategies and incorporate market sentiment into your analyses of individual securities. In addition to a complete and accessible explanation of how ChatGPT and other LLMs work, you’ll find: Discussions of future trends in artificial intelligence and finance Strategies for implementing new and soon-to-come AI tools into your investing strategies and processes Techniques for analyzing market sentiment using ChatGPT and other AI tools A can’t-miss playbook for taking advantage of the full potential of the latest AI advancements, The Predictive Edge is a fully to-date and exciting exploration of the intersection of tech and finance. It will earn a place on the bookshelves of individual and professional investors everywhere.
Book Synopsis Deep Learning Tools for Predicting Stock Market Movements by : Renuka Sharma
Download or read book Deep Learning Tools for Predicting Stock Market Movements written by Renuka Sharma and published by John Wiley & Sons. This book was released on 2024-04-10 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Book Synopsis Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications by : D. Jude Hemanth
Download or read book Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications written by D. Jude Hemanth and published by Elsevier. This book was released on 2024-01-25 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment Analysis has become increasingly important in recent years for nearly all online applications. Sentiment Analysis depends heavily on Artificial Intelligence (AI) technology wherein computational intelligence approaches aid in deriving the opinions/emotions of human beings. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas. The applications of Sentiment Analysis are enormous, ranging from business to biomedical and clinical applications. However, the combination of AI methods and Sentiment Analysis is one of the rarest commodities in the literature. The literatures either gives more importance to the application alone or to the AI/CI methodology. Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The authors provide readers with an in-depth look at the challenges and solutions associated with the different types of Sentiment Analysis, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered, which will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. Includes basic concepts, technical explanations, and case studies for in-depth explanation of the Sentiment Analysis Aids computer scientists in developing practical/real-world AI-based Sentiment Analysis systems Provides readers with real-world development applications of AI-based Sentiment Analysis, including transfer learning for opinion mining from pandemic medical data, sarcasm detection using neural networks in human-computer interaction, and emotion detection using the random-forest algorithm
Book Synopsis The Value of Social Media for Predicting Stock Returns by : Michael Nofer
Download or read book The Value of Social Media for Predicting Stock Returns written by Michael Nofer and published by Springer. This book was released on 2015-04-21 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Michael Nofer examines whether and to what extent Social Media can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which largely consist of user generated content. For instance, emotions can be extracted in order to identify the investors' risk appetite and in turn the willingness to invest in stocks. Discussion forums also provide an opportunity to identify opinions on certain companies. Taking Social Media platforms as examples, the author examines the forecasting quality of user generated content on the Internet.
Book Synopsis Data Mining in Social Media for Stock Market Prediction by : Feifei Xu
Download or read book Data Mining in Social Media for Stock Market Prediction written by Feifei Xu and published by . This book was released on 2012 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Complex, Intelligent and Software Intensive Systems by : Leonard Barolli
Download or read book Complex, Intelligent and Software Intensive Systems written by Leonard Barolli and published by Springer Nature. This book was released on with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Next-Gen Technologies in Computational Intelligence by : R. Anandan
Download or read book Next-Gen Technologies in Computational Intelligence written by R. Anandan and published by CRC Press. This book was released on 2024-06-07 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Proceeding includes the research contribution from the International Conference on Next-Gen Technologies in Computational Intelligence (NGTCA 2023) held on March 24th 2023 at Vels Institute of Science, Technology and Advanced Studies. NGCTA 2023 is the flagship conference of the Computer Society of India (Region 7). Computer Society of India (CSI) is the largest association of IT professionals in India. CSI is a non-profit organization established in 1965 and its members are committed to the advancement of theory and practice of Computer Engineering and Technology Systems. The Mission of CSI is to facilitate research, knowledge sharing, learning, and career enhancement for all categories of IT professionals, while simultaneously inspiring and nurturing new entrants into the industry and helping them to integrate into the IT community. At present, CSI has 76chapters across India, over 550 student branches with 1,00,000 plus members. It serves its members through technical events, seminars, workshops, conferences, publications & journals, research projects, competitions, special interest groups, awards & recognitions, etc. Various CSI chapters conduct Research Convention every year.
Book Synopsis Advances in Swarm Intelligence by : Ying Tan
Download or read book Advances in Swarm Intelligence written by Ying Tan and published by Springer Nature. This book was released on with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence and Sustainable Computing by : Manjaree Pandit
Download or read book Artificial Intelligence and Sustainable Computing written by Manjaree Pandit and published by Springer Nature. This book was released on 2023-10-25 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents high-quality research papers presented at 4th International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering and Technology (ICSISCET 2022) held at Madhav Institute of Technology & Science (MITS), Gwalior, India, from November 19 to 20, 2022. The book extensively covers recent research in artificial intelligence (AI) that knit together nature-inspired algorithms, evolutionary computing, fuzzy systems, computational intelligence, machine learning, deep learning, etc., which is very useful while dealing with real problems due to their model-free structure, learning ability, and flexible approach. These techniques mimic human thinking and decision-making abilities to produce systems that are intelligent, efficient, cost-effective, and fast. The book provides a friendly and informative treatment of the topics which makes this book an ideal reference for both beginners and experienced researchers.
