1,720,981 research outputs found
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
Optimal Execution Strategy with Time-varying Intraday Patterns of Liquidity Parameters
No full textABSTRACTThis paper suggests an optimal execution strategy to minimize expectedcost of a large size order within a fixed time period. Based on [42]’s price impactmodel, I include time varying bid-ask spread, a measure of market width as aparameter into the problem, and let not only width, but also depth (order booksize) and resiliency time dependent in a trading day. In addition, I utilize meanreversion regression models to estimate mean resiliency ratio as a parameter inthe execution strategy, with S&P 500 stock data in year 2012. U-shaped intradaypatterns of resiliency are presented when measured by bid-ask spreads, whileCotangent-shaped patterns are shown measured by market depths. Resiliencymovement is then predicted using machine learning techniques. In the end, Iconduct empirical experiments with all three time dependent liquidity parametersand obtain same conclusions with numeric examples. I find out higher expectednet cost savings comparing to costs from model with constant liquidity parameters.Market depth is the primary parameter to the strategy while width and resiliencyare not ignorable. When resiliency is low, cost saving is substantial
VOLATILITY FORECASTING USING A DECISION-BASED ATTRIBUTION FRAMEWORK
No full textThis research develops a portfolio volatility forecasting method for absolute return equity strategies with consideration of managers’ investment skills. Besides the portfolio holdings and prices that are commonly used by existing volatility forecast methodologies, the method proposed in this research takes account of investment skills and their volatility attribution. Investment skills are indicated by decisions of constructing portfolio overtime. Portfolio volatility is attributed to investment decisions through use of decision-based performance attribution model. It is shown that tracking the information contained in the time series of investment decision attribution leads to better volatility forecasts than commonly used forecasting methods which directly use returns and holdings. The forecasting method proposed has advantage of explaining risk forecast in terms of actual investment decisions, and changes to those decisions in real time.Ph.D. in Management Science, May 201
Integrating Deep Learning And Innovative Feature Selection For Improved Short-Term Price Prediction In Futures Markets
No full textThis study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely LSTM, CNN_LSTM, and GRU_LSTM. By incorporating cophenetic correlation in feature preparation, the study addresses the challenges posed by sudden fluctuations and price spikes while maintaining diversification and utilizing a limited number of variables derived from daily public data. However, the effectiveness of adding features relies on appropriate feature selection, even when employing powerful deep-learning models. To overcome this limitation, an innovative feature selection method is proposed, which combines cophenetic correlation-based hierarchical linkage clustering with the XGBoost importance listing function. This method efficiently identifies and integrates the most relevant features, significantly improving price prediction accuracy. The empirical findings contribute valuable insights into price prediction accuracy and the potential integration of algorithmic and intuitive approaches in futures markets. Moreover, the developed feature preparation method enhances the performance of all deep learning models, including LSTM, CNN_LSTM, and GRU_LSTM. This study contributes to the advancement of price prediction techniques by demonstrating the potential of integrating deep learning models with innovative feature selection methods. Traders and investors can leverage this approach to enhance their decision-making processes and optimize trading strategies in dynamic and complex futures markets
The Impact of High-Frequency Trading on the U.S. Equity Market
No full textThis dissertation studies the impact of high-frequency trading (HFT) on the U.S equity market. I investigate the trading behavior of high-frequency traders (HFTs) using a massive dataset that contains the NASDAQ ITCH feed messages of all S&P 500 component stocks in year 2012. I identify clusters of extremely high cancellation activity (Blocher et al., 2016) in the order book and use high cancel clusters as a proxy for high-frequency cancellation activity. I examine the change in liquidity measures from one-half second before each cancel cluster starts until after the cancel cluster closes and find that with the presence of high cancel activity, liquidity measures recover to their pre-cluster level faster than in non-cancel clusters. Furthermore, an analysis of 1-minute time intervals finds various HFT proxies to be positively related to liquidity, especially for large-cap stocks and certain sectors. Using the Li criterion (Li et al. 2018), I differentiate trades placed by HFTs versus low-frequency traders (LFTs) and compares the two types of trades under the VAR/VMA framework (Hasbrouck 1991). Evidence shows that HFT trades contribute more to the price discovery process than LFT trades and HFTs impose adverse selection costs on LFTs. This study disambiguates unreliable liquidity and faster price discovery
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