What happens around earning announcements? An investigation of information asymmetry and trading activity in the Saudi market

This paper examines stock returns and trading activities around earnings announcements for listed companies in the Saudi stock market (SSM). Specifically, we examine the levels of stock liquidity, trading activity, volatility, bid-ask spread, asymmetric information and investor trading behaviour around earnings announcements for all firms in the market for the period 2002-2009. Abnormal price and volume reactions around earnings announcements suggest that these announcements produce highly informative contents. The magnitude of the cumulative abnormal returns around earnings announcement is induced by trading activity in the two weeks before the release date. We also show evidence of an increased adverse selection cost around earnings announcement, which is then gradually reduced in the post-announcement period, indicating that earnings announcements reduce uncertainty in the market. We also examine trading behaviour among small and large investors in the market through constructing order imbalance measures. In general, large investors are more sophisticated and show higher informed trading before earnings announcements whereas smaller investors show stronger reaction to news. Moreover, small investors show a buying pattern which is consistent with times-series based earnings surprise. They are net-buyers for good news and net-sellers for bad news portfolios

The potential of wiki technology as an e-learning tool in science and education; perspectives of undergraduate students in Al-Baha university, Saudi Arabia

This paper examines the potential of wiki technology as an e-learning tool in Al-Baha University, Saudi Arabia with a random sample in two colleges: science and education. 24 male students participated in this survey. The data is collected through interviewer-administered questionnaires with 16 questions divided into four axes. The data is analysed to reveal the students’ perceptions of using wiki technology in learning. The results indicate that, students prefer to learn collaboratively with positive perceptions of wiki. These results lead us to determine the possible potential of wiki technology as an e-learning tool for undergraduate students in similar context

How markets react to earnings announcements in the absence of analysts and institutions evidence from the Saudi market

How stock markets react to news is an established area of research. We examine the behaviour of the Saudi Stock market in response to earnings announcements where there are no analysts’ forecasts, with the aim of examining the efficiency of the market. The SSM seems to underreact to positive news for the first five days and then reactions tend to strengthen in the following weeks, indicating the presence of a post–earnings announcement drift, or PEAD. At the same time, the SSM overreacts to negative news in the first five days and then reverses its direction and reports an upward post-earnings announcement drift. The individually dominated market combined with the absence of analysts’ forecasts is the main explanation for this underreaction to positive news and overreaction to negative news

Can market frictions really explain the price impact asymmetry of block trades? Evidence from the Saudi stock market

We empirically examine the price impact of block trades, in the Saudi Stock Market over the time period of 2005-2008. Using a unique dataset of intraday data consisting of 2.3 million block buys and 1.9 million block sales, we find an asymmetry in the price impact of block purchases and sales. The asymmetry persists even when we account for the bidask bias in block trades, which is contrary to the previous literature. Overall, our findings suggest that in an emerging market where institutional trading is relatively scarce, market microstructure cannot explain the asymmetry in the price impact of large trades

From Temporal Models to Property-Based Testing

This paper presents a framework to apply property-based testing (PBT) on top of temporal formal models. The aim of this work is to help software engineers to understand temporal models that are presented formally and to make use of the advantages of formal methods: the core time-based constructs of a formal method are schematically translated to the BeSpaceD extension of the Scala programming language. This allows us to have an executable Scala code that corresponds to the formal model, as well as to perform PBT of the models functionality. To model temporal properties of the systems, in the current work we focus on two formal languages, TLA+ and FocusST.Comment: Preprint. Accepted to the 12th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2017). Final version published by SCITEPRESS, http://www.scitepress.or

Large Margin Image Set Representation and Classification

In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation -maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency

Design and evaluation of synthetic silica-based monolithic materials in shrinkable tube for efficient protein extraction

Sample pretreatment is a required step in proteomics in order to remove interferences and preconcentrate the samples. Much research in recent years has focused on porous monolithic materials since they are highly permeable to liquid flow and show high mass transport compared with more common packed beds. These features are due to the micro-structure within the monolithic silica column which contains both macropores that reduce the back pressure, and mesopores that give good interaction with analytes. The aim of this work was to fabricate a continuous porous silica monolithic rod inside a heat shrinkable tube and to compare this with the same material whose surface has been modified with a C(18) phase, in order to use them for preconcentration/extraction of proteins. The performance of the silica-based monolithic rod was evaluated using eight proteins; insulin, cytochrome C, lysozyme, myoglobin, β-lactoglobulin, ovalbumin, hemoglobin, and bovine serum albumin at a concentration of 60 μM. The results show that recovery of the proteins was achieved by both columns with variable yields; however, the C(18) modified silica monolith gave higher recoveries (92.7 to 109.7%) than the non-modified silica monolith (25.5 to 97.9%). Both silica monoliths can be used with very low back pressure indicating a promising approach for future fabrication of the silica monolith inside a microfluidic device for the extraction of proteins from biological media