The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

MEASURING THE INFORMATION FLOW BETWEEN THE WEB AND STOCK MARKET VOLUMES: A MULTIVARIATE TRANSFER ENTROPY ANALYSIS

The recent discovery of strong analogies between speculative markets and some well known physical phenomena and concepts, such as spin systems, universality, criticality and complexity has led to a growing interest of physicists in the dynamics of financial markets. Together with the development of these analogies between the statistical mechanics branch of physics and economics, the new field of study called econophysics is born. This field of research profits from the immense amount of data that nowadays are provided by different sources: from social networks to search engines. Following the procedure of recent studies, in this thesis we investigate the interplay between finance-related news and tweets and financial markets. In particular, we consider, in a period of 9 years, the Twitter-and-news volume of the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index and, as a first attempt, we explore time-lagged cross-correlations and Granger-causality tests. However, the non-stationary and non-gaussian nature of financial data requires a different tool that can overcome the limits of linear statistics. We found this tool in information theory; allowing us to propose a novel approach based on a multivariate transfer entropy analysis.The recent discovery of strong analogies between speculative markets and some well known physical phenomena and concepts, such as spin systems, universality, criticality and complexity has led to a growing interest of physicists in the dynamics of financial markets. Together with the development of these analogies between the statistical mechanics branch of physics and economics, the new field of study called econophysics is born. This field of research profits from the immense amount of data that nowadays are provided by different sources: from social networks to search engines. Following the procedure of recent studies, in this thesis we investigate the interplay between finance-related news and tweets and financial markets. In particular, we consider, in a period of 9 years, the Twitter-and-news volume of the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index and, as a first attempt, we explore time-lagged cross-correlations and Granger-causality tests. However, the non-stationary and non-gaussian nature of financial data requires a different tool that can overcome the limits of linear statistics. We found this tool in information theory; allowing us to propose a novel approach based on a multivariate transfer entropy analysis

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index.We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Linking microblogging sentiments to stock price movement: An application of GPT-4

This paper investigates the potential improvement of the GPT-4 Language Learning Model (LLM) in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. We recorded daily adjusted closing prices and translated them into up-down movements. Sentiment for each day was extracted from messages on the Stocktwits platform using both LLMs. We develop a novel method to engineer a comprehensive prompt for contextual sentiment analysis which unlocks the true capabilities of modern LLM. This enables us to carefully retrieve sentiments, perceived advantages or disadvantages, and the relevance towards the analyzed company. Logistic regression is used to evaluate whether the extracted message contents reflect stock price movements. As a result, GPT-4 exhibited substantial accuracy, outperforming BERT in five out of six months and substantially exceeding a naive buy-and-hold strategy, reaching a peak accuracy of 71.47 % in May. The study also highlights the importance of prompt engineering in obtaining desired outputs from GPT-4's contextual abilities. However, the costs of deploying GPT-4 and the need for fine-tuning prompts highlight some practical considerations for its use

The role of textual data in finance: methodological issues and empirical evidence

This thesis investigates the role of textual data in the financial field. Textual data fall into the more extensive category of alternative data. These types of data, such as reviews, blog post, tweet, are constantly growing, and this reinforces the importance in several domains. The thesis explores different applications of textual data in finance to highlight how it is possible to use this type of data and how this implementation can add value to financial analysis. The first application concerns the use of a lexicon-based approach in the credit scoring model. The second application proposes a causality detection between financial and sentiment data using an information-theoretic measure, the transfer entropy. The last application concerns the use of sentiment analysis in a network model, called BGVAR, to analyze the financial impact of the Covid-19 Pandemic. Overall, this thesis shows that combining textual data with traditional financial data can lead to a more insightful knowledge and, therefore, to a more in-depth analysis, allowing for a broader understanding of economic events and financial relationships among economic entities of any kind

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

There is a general consensus of the good sensing and novelty character- istics of Twitter as an information media for the complex fi nancial market. This paper investigates the permeability of Twitter sphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to fi nancial-specifi c events and establishes Twitter as a relevant feeder for taking decisions regarding the fi nancial market and event fraudulent activities in that market. However, the provenance of contributions, their diferent levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specifi c financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK's Leading Food Business. The experiment attempts to answer two research questions which aim to characterize the features of Twitter permeability to the fi nancial market. The experimental results con rm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specifi c fi nancial forum, it is permeable to financial events

American Enterprise in the European Common Market: A Legal Profile. Volume 2.

The rapid expansion of international trade during the past fifteen years has confronted the American business counselor with a great variety of new problems. Solutions to these problems were not expounded to him in his pre-war legal education, nor are they to be found in the rich proliferation of advance sheets, digests, and loose-leaf services with which the modern American lawyer is blessed. When he turns to foreign counsel, he finds that a lack of common legal background makes meaningful professional communication difficult. This book has been prepared with the primary purpose of helping those American lawyers who, because of their clients\u27 expanding activities, confront for the first time the problems of trading with and trading in the European Common Market. It is designed to give them an over-all picture of the new legal framework of the Market itself and of the laws of business organization, labor relations, industrial property, competition, and taxation which prevail there. With this background American lawyers should be better able to select and use the services of the European experts on whom they must, of course, depend for definitive counsel.https://repository.law.umich.edu/michigan_legal_studies/1003/thumbnail.jp

The effects of media on financial stability

My thesis investigates the topic regarding media effects and financial stability, which makes valuable contributions to the current literature and financial industry. Through the study of this thesis, it reveals that the media can indeed manipulate financial stability via controlling different aspects of media, including the type, concept, the post information, the sentiment etc. I believe the findings of thesis would be benefit for not only the policy makers but the individuals who desire to invest in financial market. Specifically, the findings of my thesis would help the policy makers aware of the media power to adjust their monitor level of media industry. At the same time, individual investors may seek opportunities via media platforms and information posted on Twitter. For those who intend to explore links between media and financial world, this thesis would help them establish a better view of current literature and research outcomes related to media and financial aspects. For indicating different aspects of media effects, this thesis focuses on three research questions, which are presented as separate chapters 1-3. In chapter 1, I focus on the effects of two media formats (traditional and modern media) and three media concepts (media freedom, concentration and ownership) on financial stability. For defining financial stability, I distinguish it into two specific aspects, which are banking stability and financial market stability. Multi types of regression models (OLS, 2SLS and GMM) are applied to analyse the relationship based on data analysis of OECD countries from 2002 to 2016. After the empirical analysis, I find that TV and the Internet both have significant negative effects on financial stability. For other media formats and factors such as Radio, Newspaper, and media freedom, the influence behaves differently depending on the financial environment (banking or financial market). Chapter 2 is related to social media and financial stability, which investigates how the volume of Tweets and sentiment of Tweets could affect financial stability, established by the banking stock market stability and trading volume. After using a sample including 73 listed banks selected from the NYSE and FTSE 100 index, the primary results indicate that both Tweets volume and Tweets sentiment have significant effects on banking stock market stability and stock trading volume., The novel finding is from the Tweets sentiment, as the number of sentiment Tweets show opposite effects on stock market stability compared with the number of Tweets. Specially, the Tweets volume show a significant positive effect on stock market stability, yet a significant negative effect of sentimental Tweets on stability. The results of this chapter indicate the importance of sentimental information and thus, we should pay attention to information with strong sentiment on a social media platform. As we know, in 2020, the global pandemic of COVID-19 came across as an unpredictable, unexpected and rare event to the world. The governments around the world announce and carry out strict regulations to control the spread of the COVID-19. For supporting the firms especially during the quarantine, the governments also inject funds into businesses and increase interest rates. Based on that, in Chapter 3, my purpose is to investigate and compare the media coverage and real factor effects of COVID-19 on financial stability from the bank liquidity and bank stability aspects, viewing from three different time waves based on weekly data from six countries with over 30,000 observations from 1st March 2020 to 1st March 2022. The results indicate that both media coverage and real factors of COVID-19 significantly affect bank liquidity and bank stability, and most of the real factors could be harmful to bank liquidity and stability, yet some evidence shows a benefit could exist such as the number of vaccinations. Based on the results, we should continue following the government’s suggestions and getting vaccination