1,720,958 research outputs found
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
Tail Risk Protection via reproducible data-adaptive strategies
Full text linkDie Dissertation untersucht das Potenzial von Machine-Learning-Methoden zur Verwaltung von Schwanzrisiken in nicht-stationären und hochdimensionalen Umgebungen. Dazu vergleichen wir auf robuste Weise datenabhängige Ansätze aus parametrischer oder nicht-parametrischer Statistik mit datenadaptiven Methoden. Da datengetriebene Methoden reproduzierbar sein müssen, um Vertrauen und Transparenz zu gewährleisten, schlagen wir zunächst eine neue Plattform namens Quantinar vor, die einen neuen Standard für wissenschaftliche Veröffentlichungen setzen soll. Im zweiten Kapitel werden parametrische, lokale parametrische und nicht-parametrische Methoden verglichen, um eine dynamische Handelsstrategie für den Schutz vor Schwanzrisiken in Bitcoin zu entwickeln. Das dritte Kapitel präsentiert die Portfolio-Allokationsmethode NMFRB, die durch eine Dimensionsreduktionstechnik hohe Dimensionen bewältigt. Im Vergleich zu klassischen Machine-Learning-Methoden zeigt NMFRB in zwei Universen überlegene risikobereinigte Renditen. Das letzte Kapitel kombiniert bisherige Ansätze zu einer Schwanzrisikoschutzstrategie für Portfolios. Die erweiterte NMFRB berücksichtigt Schwanzrisikomaße, behandelt nicht-lineare Beziehungen zwischen Vermögenswerten während Schwanzereignissen und entwickelt eine dynamische Schwanzrisikoschutzstrategie unter Berücksichtigung der Nicht-Stationarität der Vermögensrenditen. Die vorgestellte Strategie reduziert erfolgreich große Drawdowns und übertrifft andere moderne Schwanzrisikoschutzstrategien wie die Value-at-Risk-Spread-Strategie. Die Ergebnisse werden durch verschiedene Data-Snooping-Tests überprüft.This dissertation shows the potential of machine learning methods for managing tail risk in a non-stationary and high-dimensional setting. For this, we compare in a robust manner data-dependent approaches from parametric or non-parametric statistics with data-adaptive methods. As these methods need to be reproducible to ensure trust and transparency, we start by proposing a new platform called Quantinar, which aims to set a new standard for academic publications. In the second chapter, we dive into the core subject of this thesis which compares various parametric, local parametric, and non-parametric methods to create a dynamic trading strategy that protects against tail risk in Bitcoin cryptocurrency. In the third chapter, we propose a new portfolio allocation method, called NMFRB, that deals with high dimensions thanks to a dimension reduction technique, convex Non-negative Matrix Factorization. This technique allows us to find latent interpretable portfolios that are diversified out-of-sample. We show in two universes that the proposed method outperforms other classical machine learning-based methods such as Hierarchical Risk Parity (HRP) concerning risk-adjusted returns. We also test the robustness of our results via Monte Carlo simulation. Finally, the last chapter combines our previous approaches to develop a tail-risk protection strategy for portfolios: we extend the NMFRB to tail-risk measures, we address the non-linear relationships between assets during tail events by developing a specific non-linear latent factor model, finally, we develop a dynamic tail risk protection strategy that deals with the non-stationarity of asset returns using classical econometrics models. We show that our strategy is successful at reducing large drawdowns and outperforms other modern tail-risk protection strategies such as the Value-at-Risk-spread strategy. We verify our findings by performing various data snooping tests
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
Deep Neural Networks for Cryptocurrencies Price prediction
Full text linkDie Preisvorhersage ist eine der größten Herausforderungen der quantitativen Finanzierung. Diese Thesis stellt ein Neuronales Netz-Framework vor, das eine tiefgreifende maschinelle Lernlösung für das Preisvorhersageproblem bietet. Das Framework wird in drei Zeitpunkten mit einem Multilayer Perzeptron (MLP), einem einfachen Recurrent Neural Network (RNN) und einem Long Short Term Memory (LSTM) realisiert, die lange Abhängigkeiten lernen können. Wir beschreiben die Theorie der neuronalen Netze und des Deep Learning, um eine reproduzierbare Methode für unsere Anwendungen auf dem Kryptowährungsmarkt zu erstellen. Da die Preisvorhersage verwendet wird, um finanzielle Entscheidungen wie Handelssignale zu treffen, vergleichen wir verschiedene Ansätze des Vorhersageproblems, indem wir überwachte Lernmethoden in Klassifikationsaufgaben untersuchen. Wir untersuchen diese Modelle, um Preisrichtungen von wichtitgen Kryptowährungen außerhalb der Stichprobe mit einer rolling window regression Methode vorherzusagen. Für dieses Ziel erstellen wir ein Klassifikationsproblem, das voraussagt, ob der Preis jeder Kryptowährung als Grundlage für dreimonatige Handelsstrategien erheblich zu- oder abnimmt. Wir bauen verschiedene Handelsstrategien auf, basierend auf Long- oder Long- / Short-Positionen, die auf unseren Prognosen aufbauen, und vergleichen ihre Performance mit einer passiven Index-Investition auf dem Cryptowährungsmarkt, die CRIX (Trimborn and Härdle, 2016) folgt. Cryptocurrencies, Bitcoins sind die bekanntesten, basieren auf elektronischem Geld
auf Blockchain-Technologie, die als eine dezentrale Alternative zu Währungen verwendet werden kann. Dank ihrer zahlreichen Anwendungen hat der Markt für Kryptowährung im Jahr 2017 ein exponentielles Wachstum erfahren. Wir vergleichen verschiedene gewichtete Portfolios, um zu testen, wie ein Anleger von fundamentalen Indikatoren wie der Marktkapitalisierung profitieren kann. Wir finden, dass LST die beste Genauigkeit für die Vorhersage von Richtungsbewegungen für die wichtigsten Kryptowährungen von CRIX hat und dass ein gleich gewichtetes Portfolio CRIX in den ersten Quartalen 2017 schlägt.Price prediction is one of the main challenge of quantitative finance. This paper presents a Neural Network framework to provide a deep learning solution to the price prediction problem. The framework is realized in three instants with a Multilayer Perceptron (MLP), a simple Recurrent Neural Network (RNN) and a Long Short-Term Memory (LSTM), which can learn long dependencies. We describe the theory of neural networks and deep learning in order to be able to build a reproducible method for our applications on the cryptocurrency market. Since price prediction is used in order to make fi nancial decisions such as trading signals, we compare different approaches of the prediction problem by exploring supervised learning methods in classication tasks. We study these models to predict out-of-sample price directions of height major cryptocurrencies with a rolling window regression method. For that goal, we build a classi cation problem that predicts if the price of each cryptocurrency will increase or decrease considerably, as a basis for three-months trading strategies. We build di erent trading strategies, based on long or long/short positions build on our predictions and compare their performance with a passive index investment on the cryptocurrency market that follows CRIX (Trimborn and Härdle, 2016). Cryptocurrencies, Bitcoin being the most famous, are electronic money based on Blockchain technology that can be used as a decentralized alternative to at currencies. Thanks to their numerous applications, the cryptocurrency market has experienced an exponential growth during 2017. We compare di fferent weighted portfolios to test how an investor can benefi t from fundamental indicators such as market capitalization. We fi nd that LSTM has the best accuracy for predicting directional movements for the most important cryptocurrencies of CRIX and that an equally weighted portfolio beats CRIX on the fi rst quarters of 2017
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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