1,720,970 research outputs found
Working women’s digital inclusion (exclusion) during Covid-19 pandemic and beyond. A literature review
No full textEnhancing the Efficacy of Financial Information Through Artificial Intelligence
No full textThe use of artificial intelligence and machine learning techniques in finance is gaining more and more traction from practitioners as well as from academia. In fact, corporations nowadays are using these techniques to forecast and assess different financial risks such as liquidity risk, volatility risk, and credit risk by applying ML models. The ML models are trained on historical datasets to make future forecasts on potential financial threats to the financial performance of the company.
Practitioners and institutional investors have been introducing artificial intelligence to assist their work and run different types of analysis based on quantitative and qualitative data. The introduction of qualitative (textual) data in financial market analysis is a relatively recent approach adopted by sophisticated investors to measure the tone, and the sentiment and extract information from corporate annual reports, press releases, and even social media posts. Natural Language Processing and text mining paired with machine learning models are still under trial but have proven to be effective in guiding sophisticated investors and corporate managers.
Meanwhile, finance scholars were reluctant to introduce new methodologies, especially those relying on content and textual analysis for different reasons. Their orthodoxy not only in the way they write research but also in the topics they debate could be one of the reasons that probably makes their knowledge less accessible, sometimes less relevant, and probably not read by practitioners.
Finance-related texts commonly meant to make information available to market
participants, tend to be written in formal and technical language that makes them less intelligible than they should be, complicating the possibility to make sense of them and drive action in the financial environment for the great majority of individuals. This has, for a long time, been a silently accepted limit, though more recently it brought to attention the need for a more effective and transparent spread of financial information, aimed at reducing noise as a source of volatility.
New technology, among them machine learning as a prominent application of Artificial Intelligence, maybe a handy instrument to underpin latent meanings, isolate prevalent emerging topics, and help non-professionals to make sense of financial information. Inaccessible information significantly reduces its real impact on the market’s dynamics, considerably limiting the possibility to enhance efficiency.
In this work we introduce and describe in a very understandable way how machine learning may help improve the comprehension of financial information, we also present the results of our latest research, as a prominent example of how the application of machine learning to different fields may be of great utility both for the activity of scholars and researchers, but also for practitioners and investors
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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