Stock price reaction to earnings announcement: the case of an emerging market

In an efficient stock market stock prices instantaneously and accurately adjust to new information. This paper conducts an event study analysis on an emerging market namely the Karachi Stock Exchange (KSE) by investigating the stock price reaction to public announcement of quarterly after tax profit by listed firms. By employing 5 year data on stock prices from January 2004 to August 2008 for 114 non financial firms we found that there is no abnormal return post earnings announcement. Moreover the study provides evidence that there is a bigger element of surprise in bad news than in good news as the market reaction to bad news is stronger.Event Study, Earnings Announcement, Emerging Markets

Compressed Sensing Performance Analysis via Replica Method using Bayesian framework

Compressive sensing (CS) is a new methodology to capture signals at lower rate than the Nyquist sampling rate when the signals are sparse or sparse in some domain. The performance of CS estimators is analyzed in this paper using tools from statistical mechanics, especially called replica method. This method has been used to analyze communication systems like Code Division Multiple Access (CDMA) and multiple input multi- ple output (MIMO) systems with large size. Replica analysis, now days rigorously proved, is an efficient tool to analyze large systems in general. Specifically, we analyze the performance of some of the estimators used in CS like LASSO (the Least Absolute Shrinkage and Selection Operator) estimator and Zero-Norm regularizing estimator as a special case of maximum a posteriori (MAP) estimator by using Bayesian framework to connect the CS estimators and replica method. We use both replica symmetric (RS) ansatz and one-step replica symmetry breaking (1RSB) ansatz, clamming the latter is efficient when the problem is not convex. This work is more analytical in its form. It is deferred for next step to focus on the numerical results.Comment: The analytical work and results were presented at the 2012 IEEE European School of Information Theory in Antalya, Turkey between the 16th and the 20th of Apri

Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them

Job Crafting dan Kebosanan Kerja Karyawan

Tujuan penelitian ini ingin mengetahui hubungan pengaruh antara  pelatihan job crafting dan penurunan tingkat kebosanan kerja. Hipotesis yang diajukan adalah terdapat perbedaan tingkat kebosanan kerja kelompok eksperimen-kontrol. Rancangan penelitian  menggunakan desain kelompok kontrol prates-pascates.  Konsep kebosanan kerja diukur menggunakan alat tes Dutch Boredom Scale (DUBS) diadaptasi dari Reijseger, et al.  Pengukuran job crafting menggunakan Job Crafting Scale (JCS) diadaptasi dari Tims, et.al. Analisis data menggunakan Mann Whitney U.  Hasil analisis menyebutkan nilai Z= -2,067 dan p = 0,039 (p < 0,05). Hasil ini menunjukkan adanya perbedaan skor secara signifikan tingkat kebosanan kerja kelompok eksperimen dan kontrol, setelah diberi pelatihan. Artinya, pelatihan job crafting merupakan alternatif penting dalam mengatasi kebosanan kerja