1,720,963 research outputs found
Directional derivatives in non-Hausdorff TVS: topological filter techniques without metric structures
No full textIn this work, we introduce and give some results about directional derivatives in non-Hausdorff Topological Vector Space over general Topological Division Ring. Through the paper, we use some topological filter techniques that are needful for the development of the theory because of the total lack of a metric structure of the spaces, but most of all for the non-uniqueness of the limits due to the absence of the T2 bond
AlBERTino for stock price prediction: A gibbs sampling approach
No full textBERT (Bidirectional Encoder Representations from Transformers) is one of the most popular models in Natural Language Processing (NLP) for Sentiment Analysis. The main goal is to classify sentences (or entire texts) and to obtain a score in relation to their polarity: positive, negative or neutral. Recently, a Transformer-based architecture, the fine-tuned AlBERTo [1], has been introduced to determine a sentiment score in the financial sector through a specialized corpus of sentences. In this paper, we use the sentiment (polarity) score to improve the stocks forecasting. We apply the BERT model to determine the score associated to various events (both positive and negative) that have affected some stocks in the market. The sentences used to determine the scores are newspaper articles published on MilanoFinanza. We compute both the average sentiment score and the polarity, and we use a Monte Carlo method to generate (starting from the day the article was released) a series of possible paths for the next trading days, exploiting the Bayesian inference to determine a new series of bounded drift and volatility values on the basis of the score; thus, returning an exact “directed” price as a result
BERT’s sentiment score for portfolio optimization: a fine-tuned view in Black and Litterman model
Get PDFIn financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) with their polarity (positive, negative, or neutral) and derive a sentiment score. In this paper, we use this sentiment (polarity) score to improve the forecasting of stocks and use it as a new “view” in the Black and Litterman model. This score is related to various events (both positive and negative) that have affected some stocks. The sentences used to determine the scores are taken from articles published in Financial Times (an international financial newspaper). To improve the forecast using this average sentiment score, we use a Monte Carlo method to generate a series of possible paths for several trading hours after the article was published to discretize (or approximate) the Wiener measure, which is applied to the paths and returning an exact price as results. Finally, we use the price determined in this way to calculate a yield to be used as views in a new type of “dynamic” portfolio optimization, based on hourly prices. We compare the results by applying the views obtained, disregarding the sentiment and leaving the initial portfolio unchanged
MCMC Approach for Stock Price Forecasting Using an Italian-BERT Model
No full textSentiment Analysis is a task of Natural Language Processing (NLP) whose main goal is to classify sentences (or entire texts) to obtain a score about their polarity: positive, negative, or neutral. Recently, a Transformer-based architecture, AlBERTino [5], has been introduced to determine a sentiment score in the financial sector through a specialized corpus of sentences. Here, the AlBERTino model can be used to improve stock forecasting, determining the sentiment score associated with events in the market and using a Markov Chain Monte Carlo (MCMC) method to determine a new series of bounded drift and volatility values based on this score. With these new values obtained through Bayesian inference, generating a series of paths through a Monte Carlo method to predict a polarity-driven future price is possible
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
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