1,720,962 research outputs found
Functional coefficient quantile regression model with time-varying loadings
Full text linkThis paper proposes a functional coefficient quantile regression model with heterogeneous and time-varying regression coefficients and factor loadings. Estimation of the model coefficients is done in two stages. First, we estimate the unobserved common factors from a linear factor model with exogenous covariates. Second, we plug-in an affine transformation of the estimated common factors to obtain the functional coefficient quantile regression model. The quantile parameter estimators are consistent and asymptotically normal. The application of this model to the quantile process of a cross-section of U.S. firms’ excess returns confirms the predictive ability of firm-specific covariates and the good performance of the local estimator of the heterogeneous and time-varying quantile coefficients
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
Essays in econometrics
No full textPhDThis thesis consists of two main parts. The first part deals with
an analysis of realized volatility and its relationship with market microstructure problem. The second part of the thesis presents a time
trend analysis in a panel data framework, with a semiparametric approach.
Chapter 1 introduces the topics that I embark upon the thesis. In
particular, I motivate the interest in realized volatility and market microstructure problem in the first part of the thesis, with a factor model
approach. Then, in the second part, the motivation is on the estimation
of time varying coefficient trend functions in a panel data case, using
nonparametric estimation methods.
Chapter 2 proposes a literature review on realized volatility and
factor models, while focusing on the seminal papers and models that the
theoretical literature suggests and also provides the empirical evidence
observed in financial markets.
Chapter 3 develops a theoretical model to forecast the realized
volatility consistently and efficiently for large dimensional datasets and
also addresses the solution for noise problem coming out of volatility
estimation in the presence of market microstructure effects.
Chapter 4 provides the empirical analysis and results on a sample
of S&P 500 stocks following the methodology and models suggested in
Chapter 3.
Chapter 5 focuses on developing a semiparametric panel model to
explain the time trend function. Profile likelihood estimators (PLE)
are proposed and their statistical properties are studied. We apply our methods to the UK regional temperatures. Finally, forecasting based
on the proposed model is studied.
Chapter 6 concludes, summarizing the main results and contributions of the thesis
Financial Sentiment Index with Natural Language Processing
No full textIn this paper, we aim to create a financial sentiment index by investigating the company’s voluntary information disclosures using 10-K reports. We extract relevant financial information for sentiment analysis through Natural Language Processing (NLP). We measure strategy-related disclosures, and their cross-sectional variation and classify report content into generic sections using synonym lists divided into four main categories according to their liquidity risk profile, risk positions, intra-annual information, and their exposure to risk. We create an adequate analytical tool and a financial dictionary to depict the importance of granular financial disclosure for investors to identify correctly the risk-taking behaviour and hence make the aggregated effects traceable. </p
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
Exploring the sentiment in Borsa Istanbul with deep learning
No full textSentiment analysis holds immense importance in finance and economics, addressing crucial issues such as principal–agent dynamics and information imbalances. The rise of natural language processing signifies a groundbreaking era in sentiment analysis, enabling the effective extraction of insights from textual data. Our research investigates the impact of qualitative financial data on firm valuation, utilizing sentiment extracted from annual financial disclosures, focusing on companies listed on the Borsa Istanbul Stock Exchange from 1998 to 2022. Employing a pre-trained transformer model, we develop sentiment indices and integrate textual data using a system-generalized method of moments. Our study aims to uncover how sentiment expressed in financial disclosures aids in mitigating challenges related to asymmetric information
Reflexivity Analysis of Digital Currencies with a Semiparametric Hawkes Process
No full text The self-excitability and price clustering properties of the cryptocurrency market are studied to investigate the main sources of volatility, in particular, the reflexivity or the endogeneity issues. We apply our kernel estimation of the spectrum localized both in time and frequency to data sets of transaction times, revealing pertinent features in the data that had not been made visible by classical non-localized approaches based on models with constant fertility functions over time. We apply the empirical analysis to the three largest crypto assets, i.e. Bitcoin - Ethereum - Ripple, and provide a comparison with other financial assets such as SP500, Gold, and the volatility index VIX observed from January 2018 to December 2020. The results show high levels of endogeneity in the basket of cryptocurrencies under investigation, underlining the evidence of a significant role of endogenous feedback mechanisms in the price formation process. We also demonstrate that the level of the endogeneity of markets, quantified by the branching ratio of the Hawkes process, is overestimated if the time variation is not considered.</p
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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