Short term persistence in mutual fund market timing and stock selection abilities

Using daily return data from 448 actively managed mutual funds over a recent 9-year period, we look for persistence, over two consecutive quarters, in the ability of funds to select individual stocks and time the market. That is, we decompose overall fund performance into excess returns resulting from stock selection and timing abilities and we separately test for persistence in each ability. We find persistence in the ability to time the market only among well performing funds and in the ability to select stocks only among the very best and worst performers. The existing literature patterns appear only when funds are ranked by their overall performance, which includes stock selection, market timing and fees. With respect to overall performance, there is persistence among most poorly performing and only the top well performing funds. Furthermore, the profitability of a winner-picking strategy depends on the rebalancing frequency and potentially the size of the investment. Small investors cannot profit, whereas large investors can take advantage of the class-A share fee structure and realize positive abnormal returns by annually rebalancing their portfolios

Patriotic name bias and stock returns

WOS:000322683400007 (Nº de Acesso Web of Science)Companies whose names contain the words "America(n)" or "USA" earn positive abnormal returns of about 6% per annum during World War II, the Korean War, and the War on Terrorism. These abnormal returns are not realized immediately upon the outbreak of each of the wars but are accumulated gradually during wartime. Given that no such effect is observed for the Vietnam War, we hypothesize that major, victorious wars arouse investors' patriotic feelings and cause them to gradually and perhaps subconsciously gravitate toward stocks whose name has a patriotic flavor

On the decision rules of cost-effective treatment for patients with diabetic foot syndrome

John E Goulionis1, Athanassios Vozikis2, VK Benos1, D Nikolakis11Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece; 2Department of Economic Science, University of Piraeus, Piraeus, GreeceObjective: To assess the cost-effectiveness of two treatments (medical treatment and ­amputation) in patients with diabetic foot syndrome, one of the most disabling diabetic complications. Diabetes mellitus is a massive health care problem worldwide with a current prevalence of 150 millions diabetic cases, estimated to increase to 300 million cases in 2025.Methods: Integrating medical knowledge and advances into the clinical setting is often difficult due to the complexity of the algorithms and protocols. Clinical decision support systems assist the clinician in applying new information to patient care through the analysis of patient-specific clinical variables. We require strategic decision support to analyze the cost-effectiveness of these programs compared to the status quo. We provide a simple partially observable Markov model to investigate that issue, and we propose an heuristic algorithm to find the best policy of intervention.Results: This study assesses the potential cost-effectiveness of two alternative treatment interventions in patients with diabetic foot syndrome. The implementation of the heuristic algorithm solution will assist doctors in clinical decision making, and health care organizations in evaluating medication choices for effective treatment. Finally, our study reveals that treatment programs are highly cost-effective for patients at high risk of diabetic foot ulcers and lower extremity amputations.Keywords: partially observable Markov decision model, diabetic foot syndrome, cost-­effectiveness metho

DNA Familial Binding Profiles Made Easy: Comparison of Various Motif Alignment and Clustering Strategies

Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP–chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies

Centralized trading, transparency and interest rate swap market liquidity: evidence from the implementation of the Dodd-Frank Act

We use proprietary transaction data on interest rate swaps to assess the impact of centralized trading, as mandated by Dodd-Frank, on market quality. We show that it has led to activity increasing and liquidity improving, with the largest improvements for contracts most affected by the mandate. Associated reductions in execution costs are economically significant eg daily end-user costs of trading USD relative to EUR mandated contracts drop by $3 million–$4 million. We show that requiring centralized trading in the United States caused swap markets to fragment geographically and give evidence which suggests that fragmentation is due to dealers trying to maintain market power

An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs

Background: Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty. Results: We propose SCintuit, a new scoring method for measuring sequence-motif affinity based on IFS theory. Unlike existing methods that consider dependencies between positions, SCintuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. We used SCintuit to identify known TFBSs in DNA sequences. Our method provides excellent results when dealing with both synthetic and real data, outperforming the sensitivity and the specificity of two existing methods in all the experiments we performed. Conclusions: The results show that SCintuit improves the prediction quality for TFs of the existing approaches without compromising sensitivity. In addition, we show how SCintuit can be successfully applied to real research problems. In this study the reliability of the IFS theory for motif discovery tasks is proven

Reviewing the review:a qualitative assessment of the peer review process in surgical journals

Abstract Background Despite rapid growth of the scientific literature, no consensus guidelines have emerged to define the optimal criteria for editors to grade submitted manuscripts. The purpose of this project was to assess the peer reviewer metrics currently used in the surgical literature to evaluate original manuscript submissions. Methods Manuscript grading forms for 14 of the highest circulation general surgery-related journals were evaluated for content, including the type and number of quantitative and qualitative questions asked of peer reviewers. Reviewer grading forms for the seven surgical journals with the higher impact factors were compared to the seven surgical journals with lower impact factors using Fisher’s exact tests. Results Impact factors of the studied journals ranged from 1.73 to 8.57, with a median impact factor of 4.26 in the higher group and 2.81 in the lower group. The content of the grading forms was found to vary considerably. Relatively few journals asked reviewers to grade specific components of a manuscript. Higher impact factor journal manuscript grading forms more frequently addressed statistical analysis, ethical considerations, and conflict of interest. In contrast, lower impact factor journals more commonly requested reviewers to make qualitative assessments of novelty/originality, scientific validity, and scientific importance. Conclusion Substantial variation exists in the grading criteria used to evaluate original manuscripts submitted to the surgical literature for peer review, with differential emphasis placed on certain criteria correlated to journal impact factors

Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data

Background: MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. It is generally believed that intragenic miRNAs (located in introns or exons of protein coding genes) are co-transcribed with their host genes and most intergenic miRNAs transcribed from their own RNA polymerase II (Pol II) promoter. However, the length of the primary transcripts and promoter organization is currently unknown. Methodology: We performed Pol II chromatin immunoprecipitation (ChIP)-chip using a custom array surrounding regions of known miRNA genes. To identify the true core transcription start sites of the miRNA genes we developed a new tool (CPPP). We showed that miRNA genes can be transcribed from promoters located several kilobases away and that their promoters share the same general features as those of protein coding genes. Finally, we found evidence that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters. Conclusion: miRNA promoters have similar features to those of protein coding genes, but miRNA transcript organization is more complex. © 2009 Corcoran et al

Inferring Binding Energies from Selected Binding Sites

We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms