1,159 research outputs found

    Econometrics meets sentiment : an overview of methodology and applications

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    The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

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

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    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

    The Role of News Media Coverage and Sentiment in German Real Estate Markets

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    This dissertation investigates whether the non-numeric information revealed in real estate-related news media can contribute towards increased transparency in real estate markets and can support market participants in making profound investment decisions. This is achieved by extending the common measurement of text-based sentiment in the real estate literature by not only quantifying the tonality but also the reporting intensity of specific topics or asset classes. Paper 1 identifies and analyses the news coverage and sentiment of real estate-related trends in Germany to investigate whether these two indicators underlie cyclicity over a period of 20 years. Almost 170,000 newspaper articles provided by a major German real estate news provider are assigned to six trends through the integration of topic modelling and word embeddings into real estate analysis. Thereafter, a dictionary-based approach is applied using an industry-specific dictionary to examine the level of optimistic or pessimistic language related to the trends. Paper 2 examines the relationship between news coverage or news sentiment and total returns of the three main asset classes. Three sentiment indicators for the residential, office and retail market are generated from almost 137,000 articles originating from two trade and two daily newspapers by means of computational linguistic techniques including word embeddings, seeded topic modelling and dictionary-based sentiment analysis. Paper 3 investigates whether additional information quantified from the news flow, in particular reporting intensity, can help to increase transparency on housing markets. Hence, this paper examines the relationship between five different sentiment measures and residential real estate prices in Germany on a regional level. By means of natural language processing including word embeddings and a dictionary-based approach, almost 130,000 news articles are analysed regarding the seven largest cities in Germany

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

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    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

    Cost overrun causality model in Saudi Arabian public sector construction projects.

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    Construction performance in Saudi public projects has been poor over the years, with 70% of projects considered as failures and costing the country over 1 trillion SAR (over £202 billion) between 2005 and 2015. The project delivery mechanism used to deliver construction projects is one of the main reasons for such poor performance, as all service providers (consultant, designer, and contractor) are chosen based on the lowest price. The contract evaluation shows that contractors hold most of a project's risks and suffer a cost overrun problem, thereby positioning them as the weakest link in the procurement chain. This study aims to develop a contractors' cost overrun causality model in the Saudi public sector. A systematic literature review was performed and revealed three schools of thought regarding the investigation and identification of cost overrun causes. However, none of the three schools has addressed the limitations of exploring the interaction between any causes identified and then linking root causes with a direct cause, nor including the effects of the context and the process that are used to develop construction projects. Exploring the interaction between causes is important, because the construction projects covered within the literature involved different stakeholders at different phases in a project's lifecycle. Moreover, it has been found that the amount and the causes of cost overrun are different based on the project's location. After the systematic literature review, this study aimed to develop a contractors' cost overrun causality model in the Saudi public sector, which would consider the effects of context, practices and processes of developing construction projects. Specifically, the research explored the commercial context of Saudi public construction project procurement under four major portfolios (economy, business, resources and regulation). Additionally, the research explored the processes and practices that are used to develop construction projects in the sector, based on the Porter model (diamond) and institutional theory. This thesis establishes the link between the commercial context and contractors' performance. Based on the systematic literature review and interviews, the causalities of cost overrun in Saudi Arabian construction projects were critically reviewed, established, classified and evaluated. The data created a "causes pool", with over two-hundred causes - these were then filtered through various means, resulting in forty-nine remaining causes. The study explores the relationship between these causes in order to create "causal paths" and, eventually, the overall model. The model-building process and the resultant outputs were reviewed by two industry experts, resulting in further refinement and simplification of the model. The final model contains forty-nine causal chains that have each been thoroughly explained. The nature of the problem investigated required this research to adopt a pragmatic and abductive approach in order to achieve its objectives. The main methodologies used were systematic literature review, case study, interviews, and project documentation. The research emphasises the importance of investigating the context and project-development process. In fact, by comparing Saudi public sector practices to established best practices, the study found that causal chains were triggered and contributed to by weaknesses within the context, process, and practices, which occur in the early stage of a project's lifecycle. However, it is established that only direct causes occur during the construction phase. Moreover, the results confirm that the current environment, regulation, practices, and behaviours of the Saudi public sector increase the risks of projects failing and damaging the construction industry. Therefore, based on the findings of the research, this thesis recommends that the Saudi public agency should: 1) adopt a project delivery approach that reduces the fragmentation in delivering a construction project, and which is tailored to the project context and characteristics; 2) adopt a new method to finance construction projects that is less affected by fluctuations in the oil economy; 3) build a long-term relationship with service providers (designers, consultants, and contractors) that is built on trust, sharing of information, and lesson learning and improvement; 4) adopt a new contract that is based on fair risk appropriation, where the risk transfer is to the most suitable party to effectively manage that risk; 5) generate general regulations and laws that transform the construction industry so as to be less affected by the external environment, more controlled by all the involved parties, and in which it becomes more attractive to invest
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