1,720,965 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
The Effects of Social Media Sentiment on Financial Markets: A High-Frequency Study
No full textSocial media has become one of the main communication channels over the last decade. It has reformed how investors acquire and exchange news and become their leading information source in the digital era. Social media platforms allow for the rapid dissemination of news, opinions, and sentiment to a vast number of market participants in real time. In line with the landscape change, recent studies have emphasised the significance of social media sentiment effects on financial markets. However, the majority of research focuses on the predictability of the sentiment derived from social media or assesses its impact on a specific type of financial asset. The mechanisms by which social media affects prices and investors are still not clear. In addition, whether social media sentiment captures information or noise is debatable. In this thesis, I examine the role of social media sentiment in financial markets from a market microstructure perspective and study its influence on various aspects of market dynamics.
I produce a social media sentiment index from millions of real-time Twitter (now called X) messages through textual analytics, and I demonstrate the price impact of social media sentiment and its effect on market informational efficiency at a high-frequency level. Furthermore, I explore the spillover effects between social media sentiment and market volatility across various financial assets by employing Refinitiv MarketPsych analytics sentiment indices. The analysis at intraday and daily granularity captures the nuances of real-time social media sentiment impacts on market dynamics, demonstrating the mechanism of social media sentiment influencing financial markets. Hence, this thesis aims to contribute to the extant literature on how social media sentiment affects and interacts with financial markets in a high-frequency context.
The first study of the thesis examines the mechanism by which social media sentiment affects stock prices. I assess the impact of Twitter posts on stock returns at the minute level. I find that social media sentiment can affect stock prices via trades. Specifically, an increase in buyer- (seller-) initiated trades has a significantly positive (negative) price impact. The impact is stronger with an increase in the number of tweets and sentiment, and persists even after controlling for volatility, liquidity shock, and limit-order activity. Both bullish and bearish tweets amplify the impact of trades on returns. It shows that the effect of social media sentiment is transmitted to stock prices through trades. The impact of Twitter sentiment on prices causes a permanent price movement at intraday, indicating that Twitter sentiment contains information.
The second study investigates the impact of social media sentiment on the informational efficiency of financial markets. I examine the relationship between the aggregated tone of Twitter posts, i.e., the sentiment index used in the first study, and two commonly used market efficiency measures in empirical studies: return autocorrelation and variance ratio. The findings reveal that higher social media sentiment leads to higher intraday return autocorrelation and variance ratio the following day, indicating a decrease in market informational efficiency. I account for various influential factors, employ different sentiment analysis approaches, and consider different intervals for sentiment construction, all of which consistently support this relationship. Moreover, I demonstrate that social media sentiment impacts informational efficiency through the occurrence of herding behaviours among traders, with higher sentiment leading to heightened herding activity the following day. This study supports the notion that social media sentiment contributes to a decline in the quality of the information environment, resulting in informationally inefficient equity prices the following day.
The third study delves into the dynamics of spillover effects between social media sentiments and market-implied volatilities among stock, bond, foreign exchange, and commodity markets. I find that informational spillover comes mainly from volatility indices to sentiment indices, with stock market volatility (VIX) being the most significant net generator. Within each asset class, there is a stronger spillover from volatility to the sentiment, but a marginal effect for the opposite direction. The connectedness between sentiment and volatility increases in turbulent economic periods, such as the Global Financial Crisis, Brexit, the US-China trade war, and the COVID-19 pandemic. Moreover, sentiment indices can switch from being a net receiver to a net generator of shocks during turbulent periods. This study shows that social media repeats existing news media signals, but some investors interpret repeated signals as genuinely new information.
Overall, this thesis sheds light on the interplay between social media sentiment and financial market dynamics. It shows the mechanisms underlying the influence of social media sentiment on financial markets within the context of high-frequency analysis, contributing to the fast-growing research on the impact of social media on financial markets. Hence, the above findings have important implications for investors and market officials seeking to understand and better regulate social media as an information dissemination channel in the fast-changing environment. It provides insights for investors on utilising social media sentiment in real-time investment strategy. This thesis also emphasises the importance of regulatory frameworks when it comes to social media activity for market quality and stability
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
A Microstructure Perspective on the Effect of Uncertainty and Information Opacity on Equity Market Quality
No full textAs Paulos (2003, para. 1) notes, “uncertainty is the only certainty there is, and knowing how to live with insecurity is the only security.” This statement cannot be more pertinent in the context of equity markets. Uncertainty makes it difficult to interpret information and can arguably hinder the efficient functioning of equity markets. The prevalence of uncertainty in equity markets emphasizes the need to thoroughly understand its impact on various aspects of equity markets. A good understanding of the impact of uncertainty is important not only for investors to fine-tune portfolio strategies but also for market regulators to maintain market quality. In that respect, this thesis is devoted to understanding the effect of information uncertainty on equity market quality. The empirical chapters of this thesis focus on the US equity market.
Chapter 3 studies the effect of equity market uncertainty (EMUNC) on the informational efficiency of US equity prices. We consider the US equity market as a whole by focusing on exchange-traded funds (ETFs) and find that EMUNC significantly reduces ETFs’ price efficiency. This result indicates that uncertainty reduces the quality of the information environment and makes value-relevant signals noisy, which hinders the process of price discovery.
Chapter 4 focuses on uncertainty about the Federal Open Market Committee (FOMC) announcement. These announcements represent informational shocks in the US equity market. Since investors anticipate these news announcements, they compete for trading profits using their private information about the forthcoming news. Thus, possessing accurate predictions about these news events should matter. Using analyst forecast dispersion to measure uncertainty about the impending FOMC news, we find that uncertainty significantly affects equity market quality during the FOMC announcement. In particular, uncertainty increases pre-announcement information asymmetry and reduces liquidity surrounding announcement times. Despite a reduction in liquidity, uncertainty leads to higher trading volume both before and after the announcement. Finally, we find that informational efficiency during the FOMC announcement deteriorates with higher analyst forecast disagreement. We also show that the effect of uncertainty is independent of and incremental to the effect of the FOMC announcement itself.
Chapter 5 extends the first empirical chapter. In the first chapter, we find that EMUNC reduces the informational efficiency of ETF prices. In this chapter, we explore whether such an effect is cross-sectionally heterogeneous. We hypothesize and test two plausible channels that facilitate this cross-sectional heterogeneity: limits-to-arbitrage and uncertainty exposure channels. Using a sample of S&P 500 constituent stocks, we show that EMUNC has a stronger negative impact on stocks that are harder to arbitrage or have a higher past uncertainty exposure.
Overall, this thesis enhances our understanding of uncertainty and its impact on equity market quality. The findings in empirical chapters are also potentially helpful for investors and market regulators for better investments and policy-making
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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