Implications for Enhanced Portfolio Performance Based on the Information Content of Short Interest

2007 ◽  
Author(s):  
Glen A. Larsen ◽  
Steven L. Jones
2019 ◽  
Vol 45 (7) ◽  
pp. 827-841
Author(s):  
Anthony Thomas Garcia ◽  
Anthony Loviscek ◽  
Kangzhen Xie

Purpose Does Fortune magazine’s list of the 100 Fastest-Growing Companies have information content; that is, is the list a source for market-beating performance? The paper aims to discuss this issue. Design/methodology/approach Using data for 26 annual periods, 1991–2016, the paper examines the top 5, 10, 25, 50 and all 100 stocks on a return-risk basis, including an application of Modern Portfolio Theory. To generate portfolio performance metrics, the study uses conventional mean-variance analysis, which includes the estimation of returns and risks, where risk will be measured by standard deviation and β. To arrive at the performance metrics and to determine whether information content is embedded in the list, the study reviews a series of tests. Because Fortune ranks the companies from 1 to 100, the data can be used to test if information content is displayed in sub-groups, such as in the first five to ten companies, even if it does not exist in the 100-stock portfolios. Findings The study finds that the returns are not high enough nor are the risks low enough statistically to conclude the existence of significant information content. Research limitations/implications As part of the authors’ efforts to move to the population of 2,600 firms as closely as possible, the authors use “delisting” returns from CRSP on 120 firms to account for missing observations, with a final sample size of 2,594 firms. Practical implications The evidence indicates that investors drawn to Fortune’s “100 Fastest-Growing Companies” should view them skeptically as a source for an effective stock selection strategy. Originality/value On the basis of the results of this study, readers will conclude that subscribers drawn to Fortune’s “100 Fastest-Growing Companies” should view them skeptically for investment recommendations. From a portfolio perspective, the study is unable to uncover information content that could lead to a market-beating performance, suggesting that the published criteria Fortune uses to select the Fastest-Growing Companies is embedded in the prices of the stocks even before Fortune publishes its list. The study notes that the selection criteria used by Fortune do involve some judgments on the part of the editorial staff (e.g. whether an announced restatement of previously reported financial data appears to have a significant impact), which means that someone who wished to anticipate the publication of the next list of the “Fastest-Growing Companies” would not only have to gather information but would also have to correctly anticipate these judgment calls.


2005 ◽  
Vol 78 (4) ◽  
pp. 1307-1336 ◽  
Author(s):  
Tom Arnold ◽  
Alexander W. Butler ◽  
Timothy Falcon Crack ◽  
Yan Zhang

Author(s):  
Tom Arnold ◽  
Alexander W. Butler ◽  
Timothy Falcon Crack ◽  
Yan NMI1 Zhang

2011 ◽  
Vol 40 (2) ◽  
pp. 249-283 ◽  
Author(s):  
Honghui Chen ◽  
David H. Downs ◽  
Gary A. Patterson

Author(s):  
T. L. Hayes

Biomedical applications of the scanning electron microscope (SEM) have increased in number quite rapidly over the last several years. Studies have been made of cells, whole mount tissue, sectioned tissue, particles, human chromosomes, microorganisms, dental enamel and skeletal material. Many of the advantages of using this instrument for such investigations come from its ability to produce images that are high in information content. Information about the chemical make-up of the specimen, its electrical properties and its three dimensional architecture all may be represented in such images. Since the biological system is distinctive in its chemistry and often spatially scaled to the resolving power of the SEM, these images are particularly useful in biomedical research.In any form of microscopy there are two parameters that together determine the usefulness of the image. One parameter is the size of the volume being studied or resolving power of the instrument and the other is the amount of information about this volume that is displayed in the image. Both parameters are important in describing the performance of a microscope. The light microscope image, for example, is rich in information content (chemical, spatial, living specimen, etc.) but is very limited in resolving power.


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