Download or read book Improved Volatility Prediction and Trading Using StockTwits Sentiment Data written by Shradha Berry and published by . This book was released on 2020 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility prediction plays an important role in the financial domain. The GARCH family of prediction models is very popular and efficient in using past returns to forecast volatility. It has also been observed that news, scheduled and unscheduled, have an impact on return volatility of assets. An enhanced GARCH model, called News Augmented GARCH (NAGARCH) includes an additional component for news sentiment. With the rise in popularity of the world wide web and social media, it has become a rich source for opinions and sentiments. Twitter is one such platform. It is a micro-blogging site and a popular source for public view on different topics. StockTwits is a social media platform that started as an application built using Twitter's API. It has since grown into an independent financial social media platform for news and sentiment. StockTwits is a rich source of opinions from subject experts and analysts. This data provides first systematic exploration of social media. It reflects raw sentiments of traders, investors, media, public companies, and investment professionals as opposed to sentiments from curated news wires. This research attempts to determine if the sentiment on stocks from StockTwits micro-blogs can improve volatility prediction. The experiment is performed on 9 NASDAQ100 stocks. The GARCH model with stock returns, and the NA-GARCH model with stock returns and micro-blog sentiment are tuned and their prediction results are evaluated. NA-GARCH, with the sentiment data from StockTwits performed better than the GARCH model in 7 out of the 9 cases.
Download or read book Handbook of Alternative Data in Finance Volume I written by Gautam Mitra and published by CRC Press. This book was released on 2023-07-12 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Volatility Forecast Using GARCH News Sentiment and Implied Volatility written by Jamie Atkinson and published by . This book was released on 2019 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to its significance, forecasting asset volatility has been an active area of research in recent decades. In this whitepaper we aim to take into account the stylised facts of volatility to improve predictive power of a simple GARCH model. We investigate the power of three GARCH models (GARCH, EGARCH, GJR- GARCH) using implied volatility and news sentiment data as external regressors in order to enhance forecasts of stock return volatility. We also explore the impact of the use of fat-tailed and skewed distributions. Analysis is conducted on 5 constituents of the S&P500. In terms of in-sample performance, the findings suggest that a GJR-GARCH(1,1) model incorporating a student-t distribution, implied volatility and news sentiment data consistently out-performs a simple GARCH(1,1) with a normal distribution. When comparing out-of-sample forecast performance, the enhanced models were able to improve volatility predictions for four out of five stocks.
Download or read book The Impact of Sentiment and Attention Measures on Stock Market Volatility written by Francesco Audrino and published by . This book was released on 2018 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: We analyze the impact of sentiment and attention variables on volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. Applying a state-of-the-art sentiment classification technique, we investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify investors' attention, as measured by the number of Google searches on financial keywords (e.g. "financial market" and "stock market"), and the daily volume of company-specific short messages posted on StockTwits to be the most relevant variables. In addition, our study shows that attention and sentiment variables are able to significantly improve volatility forecasts, although the improvements are of relatively small magnitude from an economic point of view.
Download or read book Volatility Forecast with GARCH Model and News Analytics written by Andrea Cantamessa and published by . This book was released on 2019 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study we investigate how the prediction of future volatility is improved by using news (meta)data. We use three input time series, namely: (i) market data, (ii) news sentiment impact scores, as explained by Yu (2014), and (iii) the news volume. We compare the results of predicting volatility by using a “vanilla” GARCH model, which uses market data only, and the news enhanced GARCH, as described above. Finally, the forecasted volatility is compared with the realized volatility, allowing an assessment of the robustness and precision of the model. RavenPack and Thomson Reuters provided news data and market data, respectively. The main findings are that the inclusion of scheduled news and the inclusion of news volume characterized by negative sentiment improve the forecasted volatility. The added value of scheduled news to volatility predictions is in line with Li and Engle (1998).
Download or read book Prediction of Stock Market Prices Using Prediction Algorithm and Sentiment Analysis written by Aryan Shaikh and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market has fascinated thousands of investors' hearts from entire globe. Prediction of the stock exchange data is main monetary subject which has involvement of supposition that, there is some predictive relation between past and future stock returns. An every indusial should know that stock market is one of the vital things with respect to economy of country. People who are keen towards computers and trading communal are interested in forecasting of stocks. Forecasting can be done using past data values as well as understanding the bulletin and data in Digital Media. Unobtainability or erroneous predictions because of varying patterns. Predictions of Stocks are interesting not only for the trading community only but also for a computer enthusiastic public. When we think about prediction, it can happen in two ways: we can predict using previous data values and the other way is to look and understand the news and data in the Digital Media. In the previous case there is a problem with the unavailability of the data or some data which is available but we might get inaccurate predictions because of changing patterns. Our system predicts the stock prices for the next trading day and for the specific date. Moving average technique is used to get improved prediction from the model. The Moving Average is the best widespread techniques procedure amongst every marker. The point of this investigation is done to give an appearance whether the moving normal pointer is unconditionally valuing the case by financial supporters and examiners. The theme of this tool is to provide opportunities and recognize whether future security worth developing. The contextual mining of text with recognizing and deriving subjective information from the source material is known as Sentiment analysis. Opinion about the particular company can be identified by user by Real time sentiment analysis of the stock prices. Users can easily identify the opinion of people whether tweets are positive, neutral or negative by using graphs by our carried-out sentiment analysis of tweets. The prediction accuracy is assessed and gives a percentage of accurate result. The accuracy and the prediction is combined to give user to acknowledge them the trend of the target stock with known accuracy. Also, the future price of each company can be checked by user.
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 Forecasting Stock Volatility written by Xingyi Li and published by . This book was released on 2018 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as compared with that delivered by the models that use daily data. Exactly how much better is still unknown. The present paper fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important directions. First, we employ an extensive set of intraday data on 31 individual stocks over a sample period of 19 years. Second, we use forecast horizons ranging from 1 day to 6 months. Third, we evaluate the precision of volatility forecast provided by various competing models. Fourth, we conduct several robustness checks to assess the sensitivity of our results to various alternative choices. The major finding of our empirical study is that the gains from using intraday data are rather significant and persist over longer forecast horizons. Depending on the forecast horizon, the improvement in forecast precision varies from 30 to 50 percent. We demonstrate that our main results on the forecast accuracy gains are robust to the choice of intraday data frequency and the choice of measure of realized daily volatility.
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.
Download or read book Improving Prediction of Stock Market Indices by Analyzing the Psychological States of Twitter Users written by Alexander Porshnev and published by . This book was released on 2015 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: In our paper, we analyze the possibility of improving the prediction of stock market indicators by conducting a sentiment analysis of Twitter posts. We use a dictionary-based approach for sentiment analysis, which allows us to distinguish eight basic emotions in the tweets of users. We compare the results of applying the Support Vector Machine algorithm trained on three sets of data: historical data, historical and “Worry”, “Fear”, “Hope” words count data, historical data and data on the present eight categories of emotions. Our results suggest that the Twitter sentiment analysis data provides additional information and improves prediction as compared to a model based solely on information on previous shifts in stock indicators.
Download or read book Using Text Sentiment of Company Filings to Forecast Volatility written by Kevin Tikvic and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent times, large scale textual analysis was started to be deployed in finance and accounting analysis. Among others, an established use case happens to be the application of natural language processing for volatility forecasting. In this thesis, I extend existing dictionary-based methodologies in the field of textual sentiment analysis by introducing a term-weighting scheme based on the impact of words on past volatility. Using a sample of 46,483 corporate 10-K filings, the learned term weights from past observations are applied to out-of-sample term counts of sentiment words from diverse categories. Subsequently, the relevance of language is tested within an augmented Mincer-Zarnowitz regression framework. It is found that - after controlling for the powerful forecasting ability of conventional time series models from the GARCH-family as well as other common predictors of volatility (e.g., trading volume, size, or leverage) - textual contents in most model specifications fall short in providing value added in predicting realized volatility in the week after the filing was submitted. While negative and positive tone embedded in the 10-K to some extent do help in explaining post-filing volatility, other textual aspects such as assertiveness/uncertainty in the management's writing style, focus on financial topics, or document readability appear to have less significant importance for the purpose of forecasting stock return volatility.
Download or read book Liquidity and Asset Prices written by Yakov Amihud and published by Now Publishers Inc. This book was released on 2006 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Liquidity and Asset Prices reviews the literature that studies the relationship between liquidity and asset prices. The authors review the theoretical literature that predicts how liquidity affects a security's required return and discuss the empirical connection between the two. Liquidity and Asset Prices surveys the theory of liquidity-based asset pricing followed by the empirical evidence. The theory section proceeds from basic models with exogenous holding periods to those that incorporate additional elements of risk and endogenous holding periods. The empirical section reviews the evidence on the liquidity premium for stocks, bonds, and other financial assets.
Download or read book Mining Data for Financial Applications written by Valerio Bitetta and published by Springer Nature. This book was released on 2021-01-14 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.* The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain. *The workshop was held virtually due to the COVID-19 pandemic. “Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets” and “Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Download or read book Advanced Data Mining and Applications written by Xiaochun Yang and published by Springer Nature. This book was released on 2023-12-06 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Download or read book How can I get started Investing in the Stock Market written by Lokesh Badolia and published by Educreation Publishing. This book was released on 2016-10-27 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is well-researched by the author, in which he has shared the experience and knowledge of some very much experienced and renowned entities from stock market. We want that everybody should have the knowledge regarding the different aspects of stock market, which would encourage people to invest and earn without any fear. This book is just a step forward toward the knowledge of market.
Download or read book E Business written by Robert M.X. Wu and published by BoD – Books on Demand. This book was released on 2021-05-19 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the latest viewpoints of scientific research in the field of e-business. It is organized into three sections: “Higher Education and Digital Economy Development”, “Artificial Intelligence in E-Business”, and “Business Intelligence Applications”. Chapters focus on China’s higher education in e-commerce, digital economy development, natural language processing applications in business, Information Technology Governance, Risk and Compliance (IT GRC), business intelligence, and more.
