Effects of Investor Sentiment Using Social Media on Corporate Financial Distress

The mainstream quantitative models in the finance literature have been ineffective in detecting possible bankruptcies during the 2007 to 2009 financial crisis. Coinciding with the same period, various researchers suggested that sentiments in social media can predict future events. The purpose of the study was to examine the relationship between investor sentiment within the social media and the financial distress of firms Grounded on the social amplification of risk framework that shows the media as an amplified channel for risk events, the central hypothesis of the study was that investor sentiments in the social media could predict t he level of financial distress of firms. Third quarter 2014 financial data and 66,038 public postings in the social media website Twitter were collected for 5,787 publicly held firms in the United States for this study. The Spearman rank correlation was applied using Altman Z-Score for measuring financial distress levels in corporate firms and Stanford natural language processing algorithm for detecting sentiment levels in the social media. The findings from the study suggested a non-significant relationship between investor sentiments in the social media and corporate financial distress, and, hence, did not support the research hypothesis. However, the model developed in this study for analyzing investor sentiments and corporate distress in firms is both original and extensible for future research and is also accessible as a low-cost solution for financial market sentiment analysis

Religious school enrollment in Pakistan : a look at the data

Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policymakers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. The authors of this paper use published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the past decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when the authors look at school choice, they find that no one explanation fits the data. While most existing theories of madrassa enrollment are based on household attributes (for instance, a preference for religious schooling or the household’s access to other schooling options), the data show that among households with at least one child enrolled in a madrassa, 75 percent send their second (and/or third) child to a public or private school or both. Widely promoted theories simply do not explain this substantial variation within households.Teaching and Learning,Public Health Promotion,Health Monitoring&Evaluation,Primary Education,Education Reform and Management,Primary Education,Teaching and Learning,Education Reform and Management,Health Monitoring&Evaluation,Youth and Governance

Framing Effects in the Coverage of Scientology versus Germany: Some Thoughts on the Role of Press and Scholars

"Scientology might be one weird religion, but the German reaction to it is weirder still - not to mention disturbing." 2 This is how Richard Cohen of the Washington Post describes the controversy of Scientology vs. Germany, and he adds, "[...] the treatment of Scientologists is both inexplicable and troubling." 3 The inexplicable or rather as yet unexplained could usually be expected to raise the attention of scholars, especially if there is a troubling thrill to it. However, German scholars have mostly preferred to remain silent on the issue, and the few who spoke out published in German, but not in English.4 American scholars, on the other hand, quite often seem to have firm opinions on the issue, but what finally gets published are usually rather general evaluations. Although the controversy on the whole has gained widespread media attention, the actual causes of disturbance remain rather murky, especially if one decides to take a closer look

Predicting Startup Success Using Publicly Available Data

Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction. To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential and likelihood of success. This thesis explores whether online data about a company, particularly general company data, previous funding events, published news articles, internet presence, and social media activity can be used to identify fast-growing startups. Data collected from Crunchbase, the Google Search API, and Twitter was used to predict whether a company will raise a round of funding within a fixed time horizon. A total of ten machine learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 at predicting funding 1-5 years into the future. The final objective was to predict whether a startup that had already raised an angel or seed round would raise another investment within a one-year horizon. The CatBoost model with a 0.75 cutoff achieved precision and F0.1 scores of 0.790 and 0.774, beating the results of previous work in this field

Is media just noise? The link between media factors and stock performance

PURPOSE OF THE STUDY Interest towards media analytics has increased significantly by both practitioners and academia alike. The hot topic is whether or not qualitative texts contain information relevant to stock financials, and if they do, whether the impact can be used to earn abnormal returns. In order to answer this, we study the impact media factors have on financial metrics in a novel specification that combines all the major media factors in a holistic media model. To transform qualitative texts information into a "sentiment score", we develop a new methodology to estimate sentiment more accurately than currently prevailing methods. DATA AND METHODOLOGY Our study focuses on the S&P 100 constituents between the time period of 2006 and 2011. As a source of qualitative texts, we use major news publications and earnings announcements retrieved from LexisNexis -database using a web scraper program developed for the purpose of this study. We retrieve the financials data for our study using Thomson Reuters Datastream -database. In order to estimate investor sentiment, we employ both the customary word count, as well as our novel Linearized Phrase-Structure -methodology. For word count, we test the Harvard Psychological -dictionary and a finance-specific dictionary by Loughran and McDonald (2011). As our data is panel in nature, we analyze the correlations in our error terms in line with Petersen (2009), first without clustering and then clustering by firm and by time. We find time-effect in our error terms, and therefore employ a Fama-Macbeth (1973) methodology with clustering done in quarters. To mitigate a methodological choice driving our results, we run our specifications with a multitude of alternative specifications. RESULTS We find that Linearized Phrase-Structure (LPS) outperforms the predominant naïve word count methodology. Also, we find that if employing word counts, researchers should employ context dependent dictionaries, such as Loughran and McDonald's (2011). In terms of our main variables, we find that the existing media factors are not mutually exclusive, and impact financial metrics in chorus. Alas, we do not find statistically significant relationship between sentiment and abnormal returns. However, we find a relationship between aggregate market news volume and abnormal returns, and also between sentiment and abnormal volatility. We infer that our findings support limited attention -theory, and provide evidence against market efficiency

Blame and the Messengers: Journalism as a Puritan Prism for Cultural Policies in Britain

This study proposes that legacies of Puritanism are reflected in the way journalists cover a range of events and processes. The consequences are ambiguous: sometimes they may be harmful, other times they are laudable. Media coverage of the death of Peter Connelly (Baby P) in 2007 is chosen as an example of the social production of cultures of guilt and blame. In particular, journalists’ productive efforts perform significant and active roles in colouring public responses to events. Thereby journalists may reflect in their secularised ethics the hidden influences of nineteenth-century Evangelical traditions and earlier Calvinist ones. Following the analysis of Weber, the paper argues that media approaches to rationality also reflect an impress of lingering Puritan structures of thought. The argument contrasts journalism with the Bohemian writing traditions, which were perhaps suffocated by more urgent Calvinistic approaches alongside the development of industrial capitalism. The paper concludes that newsroom practices and values amount to implicit or covert cultural policies of their own

Bill McKibben’s Influence on U.S. Climate Change Discourse: Shifting Field-level Debates Through Radical Flank Effects

This paper examines the influence of radical flank actors in shifting field-level debates by increasing the legitimacy of pre-existing but peripheral issues. Using network text analysis, we apply this conceptual model to the climate change debate in the U.S. and the efforts of Bill McKibben and 350.org to pressure major universities to “divest” their fossil fuel assets. What we find is that, as these new actors and issue entered the debate, liberal policy ideas (such as a carbon tax), which had previously been marginalized in the U.S. debate, gained increased attention and legitimacy while the divestment effort itself gained limited traction. This result expands theory on indirect pathways to institutional change through a discursive radical flank mechanism, and suggests that the actual influence of Bill McKibben on the U.S. climate debate goes beyond the precise number of schools that divest to include a shift in the social and political discourse.https://deepblue.lib.umich.edu/bitstream/2027.42/136601/1/1364_Hoffman.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136601/4/1364_Hoffman_Sept2017.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136601/6/1364_Hoffman_Sept2018.pdfDescription of 1364_Hoffman_Sept2017.pdf : Sept 2017 updateDescription of 1364_Hoffman_Sept2018.pdf : Sept 2018 citation updat

An Examination of Accounting Topics Through the Completion of Case Studies

This thesis, completed under the direction of Dr. Victoria Dickinson in Accountancy 420, a year-long thesis course, is composed of eleven case studies and is intended to analyze various areas of accounting and financial reporting. This topic analysis, along with the numerous accounting firm presentations that were offered to expose us to potential future professional opportunities, helped to further enhance our knowledge of the accounting profession while developing our critical thinking processes and related research techniques. That said, the completion of this course, both information-enhancing and skill-building, assisted in setting us up for a future career in professional accounting

Achieving the Potential of Health Care Performance Measures: Timely Analysis of Immediate Health Policy issues

The United States is on the cusp of a new era, with greater demand for performance information, greater data availability, and a greater willingness to integrate performance information into public policy. This era has immense promise to deliver a learning health care system that encourages collaborative improvements in systems-based care, improves accountability, helps consumers make important choices, and improves quality at an acceptable cost. However, to curtail the possibility of unintended adverse consequences, it is important that we invest in developing sound measures, understand quality measures' strengths and limitations, study the science of quality measurement, and reduce inaccurate inferences about provider performance

Exploring the value of big data analysis of Twitter tweets and share prices

Over the past decade, the use of social media (SM) such as Facebook, Twitter, Pinterest and Tumblr has dramatically increased. Using SM, millions of users are creating large amounts of data every day. According to some estimates ninety per cent of the content on the Internet is now user generated. Social Media (SM) can be seen as a distributed content creation and sharing platform based on Web 2.0 technologies. SM sites make it very easy for its users to publish text, pictures, links, messages or videos without the need to be able to program. Users post reviews on products and services they bought, write about their interests and intentions or give their opinions and views on political subjects. SM has also been a key factor in mass movements such as the Arab Spring and the Occupy Wall Street protests and is used for human aid and disaster relief (HADR). There is a growing interest in SM analysis from organisations for detecting new trends, getting user opinions on their products and services or finding out about their online reputation. Companies such as Amazon or eBay use SM data for their recommendation engines and to generate more business. TV stations buy data about opinions on their TV programs from Facebook to find out what the popularity of a certain TV show is. Companies such as Topsy, Gnip, DataSift and Zoomph have built their entire business models around SM analysis. The purpose of this thesis is to explore the economic value of Twitter tweets. The economic value is determined by trying to predict the share price of a company. If the share price of a company can be predicted using SM data, it should be possible to deduce a monetary value. There is limited research on determining the economic value of SM data for “nowcasting”, predicting the present, and for forecasting. This study aims to determine the monetary value of Twitter by correlating the daily frequencies of positive and negative Tweets about the Apple company and some of its most popular products with the development of the Apple Inc. share price. If the number of positive tweets about Apple increases and the share price follows this development, the tweets have predictive information about the share price. A literature review has found that there is a growing interest in analysing SM data from different industries. A lot of research is conducted studying SM from various perspectives. Many studies try to determine the impact of online marketing campaigns or try to quantify the value of social capital. Others, in the area of behavioural economics, focus on the influence of SM on decision-making. There are studies trying to predict financial indicators such as the Dow Jones Industrial Average (DJIA). However, the literature review has indicated that there is no study correlating sentiment polarity on products and companies in tweets with the share price of the company. The theoretical framework used in this study is based on Computational Social Science (CSS) and Big Data. Supporting theories of CSS are Social Media Mining (SMM) and sentiment analysis. Supporting theories of Big Data are Data Mining (DM) and Predictive Analysis (PA). Machine learning (ML) techniques have been adopted to analyse and classify the tweets. In the first stage of the study, a body of tweets was collected and pre-processed, and then analysed for their sentiment polarity towards Apple Inc., the iPad and the iPhone. Several datasets were created using different pre-processing and analysis methods. The tweet frequencies were then represented as time series. The time series were analysed against the share price time series using the Granger causality test to determine if one time series has predictive information about the share price time series over the same period of time. For this study, several Predictive Analytics (PA) techniques on tweets were evaluated to predict the Apple share price. To collect and analyse the data, a framework has been developed based on the LingPipe (LingPipe 2015) Natural Language Processing (NLP) tool kit for sentiment analysis, and using R, the functional language and environment for statistical computing, for correlation analysis. Twitter provides an API (Application Programming Interface) to access and collect its data programmatically. Whereas no clear correlation could be determined, at least one dataset was showed to have some predictive information on the development of the Apple share price. The other datasets did not show to have any predictive capabilities. There are many data analysis and PA techniques. The techniques applied in this study did not indicate a direct correlation. However, some results suggest that this is due to noise or asymmetric distributions in the datasets. The study contributes to the literature by providing a quantitative analysis of SM data, for example tweets about Apple and its most popular products, the iPad and iPhone. It shows how SM data can be used for PA. It contributes to the literature on Big Data and SMM by showing how SM data can be collected, analysed and classified and explore if the share price of a company can be determined based on sentiment time series. It may ultimately lead to better decision making, for instance for investments or share buyback