681 research outputs found
Gold Sponsor Talk: Author Kris Dinnison
No full textWe are very pleased to announce that Kris Dinnison will be appearing at PNC/MLA this year! Kris Dinnison is a local author whose works include the YA novel You and Me and Him. Find out more about Kris at www.krisdinnison.net
The Gaussian rank correlation estimator: Robustness properties.
Get PDFThe Gaussian rank correlation equals the usual correlation coefficient computed from the normal scores of the data. Although its influence function is unbounded, it still has attractive robustness properties. In particular, its breakdown point is above 12%. Moreover, the estimator is consistent and asymptotically efficient at the normal distribution. The correlation matrix based on the Gaussian rank correlation is always positive semidefinite, and very easy to compute, also in high dimensions. A simulation study confirms the good efficiency and robustness properties of the proposed estimator with respect to the popular Kendall and Spearman correlation measures. In the empirical application, we show how it can be used for multivariate outlier detection based on robust principal component analysis.Breakdown; Correlation; Efficiency; Robustness; Van der Waerden;
Hedge fund portfolio selection with modified expected shortfall
Get PDFModified Value-at-Risk (VaR) and Expected Shortfall (ES) are recently introduced downside risk estimators based on the Cornish-Fisher expansion for assets such as hedge funds whose returns are non-normally distributed. Modified VaR has been widely implemented as a portfolio selection criterion. We are the first to investigate hedge fund portfolio selection using modified ES as optimality criterion. We show that for the EDHEC hedge fund style indices, the optimal portfolios based on modified ES outperform out-of-sample the EDHEC Fund of Funds index and have better risk characteristics than the equal-weighted and Fund of Funds portfolios.portfolio optimization, modified expected shortfall, non-normal returns
Robust estimation of intraweek periodicity in volatility and jump detection.
Get PDFOpening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We propose a non-parametric weighted standard deviation and parametric truncated maximum likelihood estimation procedure for the periodic component in volatility and show that they are robust to price jumps. We also show that robust periodicity estimates can be used to increase the accuracy of jump detection methods. We compare the classical and robust methods for the 5-minute EUR/USD returns. The robust intraweek periodicity estimates are lower than the classical ones on Tuesday-Friday 8:30-8:35 EST and Monday-Friday 10:00-10:05 EST. The higher values for the non-robust estimates are likely to be due to jumps. Accounting for the periodicity in the volatility of high-frequency returns is especially important to detect the relatively small jumps occurring at times for which volatility is periodically low and to reduce the number of spurious jump detections at times of periodically high volatility.High-frequency data; Jump detection; Periodicity; Robust statistics;
Stainless steel in Sweden : antidumping attacks, good international citizenship
Get PDFThis report analyzes the economics, legal, and business logic of the United States, Sweden, and the European Community regarding the stainless steel industry. Trade policies and legal cases are analyzed and presented to support the author's conclusion that good economics, international competitiveness, private ownership, and limited support from a government that demonstrates good international citizenship are not enough to defend an industry against the application of antidumping or other import-restricting policy.Water and Industry,Roads&Highways,Primary Metals,Banks&Banking Reform,Mining&Extractive Industry (Non-Energy)
Questioning the news about economic growth : sparse forecasting using thousands of news-based sentiment values
Get PDFThe modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and the weighting of thousands of sentiment values. These values are obtained by pooling the textual sentiment values across publication venues, article topics, sentiment construction methods, and time. We apply the framework to the investigation of the value added by textual analysis-based sentiment indices for forecasting economic growth in the US. We find that the additional use of optimized news-based sentiment values yields significant accuracy gains for forecasting the nine-month and annual growth rates of the US industrial production, compared to the use of high-dimensional forecasting techniques based on only economic and financial indicators. (C) 2018 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters
Contributions to the analysis of corporate information: Robustness, sustainability and textual analysis.
No full textInvestors are in the business of acquiring information and using that information to manage a portfolio of assets. Information asymmetry, however, plays a central role in investors' information acquisition and occurs when one group of participants has better or more timely information than other groups. Typically, the source of the information asymmetry is the superior knowledge that managers have about the firm's prospects, while the investors in the firm comprise the uninformed group. With examples such as the accounting scandals of Enron, Lernout & Hauspie, Worldcom, it is obvious today that information on capital markets is asymmetrically distributed and that information differential between the management and investors can lead to a suboptimal allocation of resources within the firm. Clearly, managers, investors and regulators concerned with financial supervision are in need of methods to manage information asymmetry problems.This Ph.D. dissertation contributes to the development of new methods to mitigate information asymmetry on capital markets and focuses on three specific sources of information, each of them corresponding to one part of the dissertation. In Part I, I define a prediction model that helps investors reduce information asymmetry by predicting financial analysts' forecast error. In Part II, I focus on the value of corporate social responsibility (CSR) information, provided by the Kinder, Lyndenberg and Domini Research and Analytics database (KLD). In Part III, the objective is to decrease information asymmetry by defining more accurate measures of tone (or sentiment) in the narrative sections of a firm's voluntary disclosures, such as earnings press releases and CEO letters to shareholders. Overall, we show that investors, managers and regulators can manage and reduce information asymmetries on capital markets by either using advanced econometric methods, new databases on a firms stakeholder activities or the textual content of a firm's financial disclosures.status: Publishe
Ajax for web application developers / Kris Hadlock.
No full textIncludes index."Reusable components and patterns for Ajax-driven applications"--Cover.Book fair 2012.271 pages
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