Using Social Media And News Sentiment Data To Construct A Momentum Strategy
DOWNLOAD
Download Using Social Media And News Sentiment Data To Construct A Momentum Strategy PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Using Social Media And News Sentiment Data To Construct A Momentum Strategy book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Using Social Media And News Sentiment Data To Construct A Momentum Strategy
DOWNLOAD
Author : Yuke Shi
language : en
Publisher:
Release Date : 2019
Using Social Media And News Sentiment Data To Construct A Momentum Strategy written by Yuke Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Momentum strategy is one of the most popular strategies that market participants use to make investment decisions. In the past two decades, many researchers have shown that momentum strategy beats the market, and provides attractive portfolio returns. In this study we investigate Dow Jones Industry Average (DJIA) index and include news data and social media sentiment data to improve the performance of momentum strategy. Particularly, we select StockTwits as the social media source. Four weekly momentum strategies are built and compared over a five-year back-testing period. This research starts with using market data to calculate 5-day Relative Strength Indicator (RSI) that captures the momentum of price. A momentum strategy is constructed based on the overbought/oversold (70/30) signals of RSI proposed by Wilder (1978). Furthermore, the news and social media sentiment data are applied separately to enhance the RSI selections of momentum strategy. News impact scores are used to give more precise evaluations toward news sentiment. Finally, news and social media sentiment data are applied as a double filter to enhance the momentum strategy. The results show that news sentiment and social media data improves the performance of the momentum strategy.
Trading On Sentiment
DOWNLOAD
Author : Richard L. Peterson
language : en
Publisher: John Wiley & Sons
Release Date : 2016-03-21
Trading On Sentiment written by Richard L. Peterson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-21 with Business & Economics categories.
In his debut book on trading psychology, Inside the Investor’s Brain, Richard Peterson demonstrated how managing emotions helps top investors outperform. Now, in Trading on Sentiment, he takes you inside the science of crowd psychology and demonstrates that not only do price patterns exist, but the most predictable ones are rooted in our shared human nature. Peterson’s team developed text analysis engines to mine data - topics, beliefs, and emotions - from social media. Based on that data, they put together a market-neutral social media-based hedge fund that beat the S&P 500 by more than twenty-four percent—through the 2008 financial crisis. In this groundbreaking guide, he shows you how they did it and why it worked. Applying algorithms to social media data opened up an unprecedented world of insight into the elusive patterns of investor sentiment driving repeating market moves. Inside, you gain a privileged look at the media content that moves investors, along with time-tested techniques to make the smart moves—even when it doesn’t feel right. This book digs underneath technicals and fundamentals to explain the primary mover of market prices - the global information flow and how investors react to it. It provides the expert guidance you need to develop a competitive edge, manage risk, and overcome our sometimes-flawed human nature. Learn how traders are using sentiment analysis and statistical tools to extract value from media data in order to: Foresee important price moves using an understanding of how investors process news. Make more profitable investment decisions by identifying when prices are trending, when trends are turning, and when sharp market moves are likely to reverse. Use media sentiment to improve value and momentum investing returns. Avoid the pitfalls of unique price patterns found in commodities, currencies, and during speculative bubbles Trading on Sentiment deepens your understanding of markets and supplies you with the tools and techniques to beat global markets— whether they’re going up, down, or sideways.
Relationship Discovery Of Price Movements Between Sentiment Analysis On Social Media Data And Stock Market
DOWNLOAD
Author : Mohammed Moosa Naqvi
language : en
Publisher:
Release Date : 2019
Relationship Discovery Of Price Movements Between Sentiment Analysis On Social Media Data And Stock Market written by Mohammed Moosa Naqvi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
A desire to make a profit on investment has been a prominent motivational factor in financial investments. The idea of growing with a blue chip firm or an emerging start-up has allured both individual investor(s) and large investing firms alike. One of the financial market areas that gives such opportunity to become part of something bigger is the stock market. Across the globe, stock exchanges become the medium through which billions of stocks are traded on daily basis. Nevertheless, stock market volatility always challenges a seasoned investor to find new ways to invest into stocks that will be profitable in near future. These challenges are equally important for financial firms that are building algorithms for creating profitable stock portfolio. With the advent of social media and similar resonance in digital news media, we have witnessed huge data explosion and this has also opened new opportunities to harvest these data into information for profitable stock trading. In this research, I have performed analysis of more than 8.5 million news article and twitter messages to determine relationship between stock price and media sentiments. Using novel data visualization and Natural Language Processing techniques, I have implemented novel data visualizations such as frequency of news items and other related events affecting the company share price.
Handbook Of Alternative Data In Finance Volume I
DOWNLOAD
Author : Gautam Mitra
language : en
Publisher: CRC Press
Release Date : 2023-07-12
Handbook Of Alternative Data In Finance Volume I written by Gautam Mitra and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-12 with Business & Economics categories.
Handbook of Alternative Data in Finance, Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more. The book also offers a rigorous and detailed exploration of process, application and delivery that should be practically useful to researchers and practitioners alike. Features Includes cutting edge applications in machine learning, fintech, and more Suitable for professional quantitative analysts, and as a resource for postgraduates and researchers in financial mathematics Features chapters from many leading researchers and practitioners
The Momentum Of News
DOWNLOAD
Author : Ying Wang
language : en
Publisher:
Release Date : 2018
The Momentum Of News written by Ying Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Relying on a comprehensive data set of news releases, we construct monthly firm-level news sentiment scores during the 2000-2016 period and document a news momentum phenomenon of stocks with more positive news in the past generating more positive news in the future. We propose three hypotheses to explain this phenomenon and find that news momentum is driven by the persistence of firms' fundamentals rather than stale news or firms' strategic disclosure. A trading strategy that combines a long position in a good news quintile portfolio with a short position in a bad news portfolio generates a 7.45% risk-adjusted return annually. This return anomaly appears on both news and non-news days. Overall, these findings suggest that the cross-sectional prediction of news is not fully incorporated into the stock price by investors.
Emergent Sentiment In Financial Markets
DOWNLOAD
Author : Jie Ren
language : en
Publisher:
Release Date : 2020
Emergent Sentiment In Financial Markets written by Jie Ren and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Over the past two decades, substantial research has established the importance of sentiment in financial markets. We know that sentiment impacts investor decisions; because of advanced text mining and sentiment analysis techniques, automated trading strategies based on sentiment are prevalent. However, we know little about how sentiment emerges. The news media, the markets, and social media are all understood to contribute to overall sentiment, but the exact mechanisms at play remain largely unknown. Understanding the mechanism of sentiment emergence is critical to build systems to support investors' decision making and to ensure that markets function well and are resistant to attempts to manipulate sentiment. We studied one piece of the puzzle and examined sentiment flow from social media to mass media. We studied two years (2013 to 2014) of data from Sina Weibo and Sina Finance. After controlling for the direction of influence from mass media to social media and also for stock performance, we found that in the stock market, social media sentiment affects mass media sentiment the next day. Moreover, this impact is stronger for attention-grabbing stocks. The implications of these findings are discussed in relation to sentiment emergence and a broader demand-driven media bias perspective.
Momentum Mean Reversion And Social Media
DOWNLOAD
Author : Shreyash Agrawal
language : en
Publisher:
Release Date : 2019
Momentum Mean Reversion And Social Media written by Shreyash Agrawal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
We analyze the relation between stock market liquidity and real-time measures of sentiment obtained from the social-media platforms StockTwits and Twitter. Linear regression analysis shows that extreme sentiment corresponds to higher demand and lower supply of liquidity, with negative sentiment having a much larger effect on demand and supply than positive sentiment. An intraday event study shows that booms and panics end when bullish and bearish sentiment reach extreme levels, respectively. After extreme sentiment, prices become more mean-reverting and spreads narrow. To quantify the magnitudes of these effects, we conduct a historical simulation of a market-neutral mean-reversion strategy that uses social-media information to determine its portfolio allocations. Our results suggest that the demand and supply of liquidity are influenced by investor sentiment, and that market makers who can keep their transaction costs to a minimum are able to profit by using extreme bullish and bearish emotions in social media as a real-time barometer for the end of momentum and a return to mean reversion.
The Good The Bad And The Trending
DOWNLOAD
Author : Austin Hill-Kleespie
language : en
Publisher:
Release Date : 2019
The Good The Bad And The Trending written by Austin Hill-Kleespie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
I use a unique data set from investor centric social media site StockTwits to test the Hong and Stein (1999) two trader theory of momentum against Daniel, Hirshleifer, and Subrahmanyam (1998) self attribution theory. Tests of individual securities and portfolios produce results which are largely consistent with Hong and Stein (1999). For individual securities I find that trailing measures of sentiment have predictive power over future stock returns. Portfolios generated using StockTwits data are found to have strong explanatory power over the daily momentum factor from Carhart (1997).
Data Science For Economics And Finance
DOWNLOAD
Author : Sergio Consoli
language : en
Publisher: Springer Nature
Release Date : 2021
Data Science For Economics And Finance written by Sergio Consoli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Application software categories.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Sentiment Analysis And Ontology Engineering
DOWNLOAD
Author : Witold Pedrycz
language : en
Publisher: Springer
Release Date : 2016-03-22
Sentiment Analysis And Ontology Engineering written by Witold Pedrycz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-22 with Technology & Engineering categories.
This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology –oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students.