<div>I examine movements in the closing price that are different than the movements of the high and low prices on a given day. I construct the measure HLDiff which accumulates the differences between the high-low midpoint return and the closing price return over a month. Instances in which the closing price deviates from the movements in the midpoint between the high and low are a strong predictor of future abnormal returns. The predictive power of the HLDiff measure holds across size groups and sub-periods and holds in the presence of other common determinants of stock returns. The predictive power of HLDiff appears to be driven by the existence of market frictions. Specifically, I find that the premium associated with a factor constructed based on HLDiff is consistent with short-selling constraints inhibiting the correction of overpricing. The factor also appears to improve the pricing ability of the single-factor and five-factor models.</div>

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 39-40)

abstract: A firewall is a necessary component for network security and just like any regular equipment it requires maintenance. To keep up with changing cyber security trends and threats, firewall rules are modified frequently. Over time such modifications increase the complexity, size and verbosity of firewall rules. As the rule set grows in size, adding and modifying rule becomes a tedious task. This discourages network administrators to review the work done by previous administrators before and after applying any changes. As a result the quality and efficiency of the firewall goes down. Modification and addition of rules without knowledge of previous rules creates anomalies like shadowing and rule redundancy. Anomalous rule sets not only limit the efficiency of the firewall but in some cases create a hole in the perimeter security. Detection of anomalies has been studied for a long time and some well established procedures have been implemented and tested. But they all have a common problem of visualizing the results. When it comes to visualization of firewall anomalies, the results do not fit in traditional matrix, tree or sunburst representations. This research targets the anomaly detection and visualization problem. It analyzes and represents firewall rule anomalies in innovative ways such as hive plots and dynamic slices. Such graphical representations of rule anomalies are useful in understanding the state of a firewall. It also helps network administrators in finding and fixing the anomalous rules. / Dissertation/Thesis / Masters Thesis Computer Science 2014

Wind tunnel experiments often employ a wide variety and large number of sensor systems. Anomalous measurements occurring without the knowledge of the researcher can be devastating to the success of costly experiments; therefore, anomaly detection is of great interest to the wind tunnel community. Currently, anomaly detection in wind tunnel data is a manual procedure. A researcher will analyze the quality of measurements, such as monitoring for pressure measurements outside of an expected range or additional variability in a time averaged quantity. More commonly, the raw data must be fully processed to obtain near-final results during the experiment for an effective review. Rapid anomaly detection methods are desired to ensure the quality of a measurement and reduce the load on the researcher. While there are many effective methodologies for anomaly detection used throughout the wider engineering research community, they have not been demonstrated in wind tunnel experiments. Wind tunnel experimentation is unique in the sense that many repeat measurements are not typical. Typically, this will only occur if an anomaly has been identified. Since most anomaly detection methodologies rely on well-resolved knowledge of a measurement to uncover the expected uncertainties, they can be difficult to apply in the wind tunnel setting. First, the analysis will focus on pressure measurements around an airfoil and its wake. Principal component analysis (PCA) will be used to build a measurement expectation by linear estimation. A covariance matrix will be constructed from experimental data to be used in the PCA-scheme. This covariance matrix represents both the strong deterministic relations dependent on experimental configuration as well as random uncertainty. Through principles of ideal flow, a method to normalize geometrical changes to improve measurement expectations will be demonstrated. Measurements from a microphone array, another common system employed in aeroacoustic wind tunnels, will be analyzed similarly through evaluation of the cross-spectral matrix of microphone data, with minimal repeat measurements. A spectral projection method will be proposed that identifies unexpected acoustic source distributions. Analysis of good and anomalous measurements show this methodology is effective. Finally, machine learning technique will be investigated for an experimental situation where repeat measurements of a known event are readily available. A convolutional neural network for feature detection will be shown in the context of audio detection. This dissertation presents techniques for anomaly detection in sensor systems commonly used in wind tunnel experiments. The presented work suggests that these anomaly identification techniques can be easily introduced into aeroacoustic experiment methodology, minimizing tunnel down time, and reducing cost. / Doctor of Philosophy / Efficient detection of anomalies in wind tunnel experiments would reduce the cost of experiments and increase their effectiveness. Currently, manual inspection is used to detect anomalies in wind tunnel measurements. A researcher may analyze measurements during experiment, for instance, monitoring for pressure measurements outside of an expected range or additional variability in a time averaged quantity. More commonly, the raw data must be fully processed to obtain near-final results to determine quality. In this dissertation, many methods, which can assist the wind tunnel researcher in reviewing measurements, are developed and tested. First, a method to simultaneously monitor pressure measurements and wind tunnel environment measurements is developed with a popular linear algebra technique called Principal Component Analysis (PCA). The novelty in using PCA is that measurements in wind tunnels are often not repeated. Instead, the proposed method uses a large number of independent measurements acquired in various conditions and fundamental aspects of fluid mechanics to train the detection algorithm. Another wind tunnel system which is considered is a microphone array. A microphone array is a collection of microphones arranged in known locations. Current methods to assess the quality of the output data from this system require extended computation and review time during an experiment. A method parallel to PCA is used to rapidly determine if an anomaly is present in the measurement. This method does not require the extra computation necessary to see what the microphone array has observed and simplifies the quantities assessed for anomalies. While this is not a replacement for complete computation of the results associated with microphone array measurements, this can take most of the effort out of the experiment time and relegate detailed review to a time after the experiment is complete. Finally, an application of machine learning is discussed with an alternate application outside of the wind tunnel. This work explores the usefulness of a convolutional neural network (CNN) for cough detection. This can be similarly applied to detect anomalies in audio data if searching for specific anomalies with known characteristics. CNNs, in general, require much effort to train and operate effectively but are not dependent on the application or data type. These methods could be applied to a wind tunnel experiment. Overall, the work in this dissertation shows many techniques which can be implemented into current wind tunnel operations to improve the efficiency and effectiveness of the data review process.

Previous work has shown that the strongest concentrations of lunar crustal magnetic anomalies are located antipodal to four large, similarly aged impact basins (Orientale, Serenitatis, Imbrium, and Crisium). Here, we report results of a correlation study between magnetic anomaly clusters and geology in areas antipodal to Imbrium, Orientale, and Crisium. Unusual geologic terranes, interpreted to be of seismic or ejecta origin associated with the antipodal basins, have been mapped antipodal to both Orientale and Imbrium. All three antipode regions have many high-albedo swirl markings. Results indicate that both of the unusual antipode terranes and Mare Ingenii (antipodal to Imbrium) show a correlation with high-magnitude crustal magnetic anomalies. A statistical correlation between all geologic units and regions of medium to high magnetization when high-albedo features are present (antipodal to Orientale) may suggest a deep, possibly seismic origin to the anomalies. However, previous studies have provided strong evidence that basin ejecta units are the most likely sources of lunar crustal anomalies, and there is currently insufficient evidence to differentiate between an ejecta or seismic origin for the antipodal anomalies. Results indicate a strong correlation between the high-albedo markings and regions of high magnetization for the Imbrium, Orientale, and Crisium antipodes. Combined with growing evidence for an Imbrian age to the magnetic anomalies, this supports a solar wind deflection origin for the lunar swirls.

Sudden phase anomalies (SPA) of low-frequency radio waves reflected from the D-region of the lower ionosphere exclusively occur during the daylight hours when rapid changes in the ionospheric reflection height take place. They lead to abrupt changes in the linear superposition of the ground wave and the sky wave and consequently in the total field strength of the received signal. Such sudden rapid reflection height changes are usually connected with shortperiod (minutes to hours) enhancements of electron density in the lower ionosphere following solar flares, which sometimes are associated with a dramatically increase of solar X-ray radiation. This additional wave radiation can penetrate into the lower ionosphere and intensify the D region ionisation. The mean level and the number of solar X-ray bursts varies with the 11-year sunspot cycle, so that statistically investigations of number and intensity of observed SPAs can give insight into solar-terrestrial connections concerning the upper atmosphere. At Collm Observatory, SPAs are recorded since several decades. These records are combined to an index characterising the monthly mean disturbance state of the ionosphere 1983-2002. / Plötzliche Phasenanomalien (engl. sudden phase anomalies, SPA) von Langwellen, die in den Tageslichtstunden von der ionosphärischen D- Region reflektiert werden, treten auf, wenn schnelle Änderungen in der Reflexionshöhe stattfinden. Sie führen zu einer abrupten Änderung der linearen Superposition von Raum- und Bodenwelle am Beobachtungspunkt und in der Folge im Feldstärkebetrag der empfangenen Signale. Solche plötzlichen schnellen Reflexionshöhenänderungen sind gewöhnlich mit einer kurzen (Minuten bis Stunden) Zunahme der Elektronendichte in der unteren Ionosphäre verbunden und nach Sonneneruptionseffekten zu beobachten, die mit einer erheblichen Erhöhung der emittierten kurzwelligen Röntgenstrahlung einhergehen. Das mittlere Strahlungsniveau und die Anzahl von Bursts variiert mit dem 11-jährigen Sonnenfleckenzyklus, so dass statistische Untersuchungen von Anzahl und Intensität der SPA- Effekte spezielle Hinweise auf solar-terrestrische, die obere Atmosphäre betreffende Verbindungen geben können. An der Außenstelle Observatorium Collm der Universität Leipzig werden SPAs seit mehreren Jahrzehnten registriert. Sie stellen eine Datenbasis für die Jahre 1983-2002 zur Untersuchung solar-terrestrischer Beziehungen dar.

Semi-supervised learning (SSL) is the most practical approach for classification among machine learning algorithms. It is similar to the humans way of learning and thus has great applications in text/image classification, bioinformatics, artificial intelligence, robotics etc. Labeled data is hard to obtain in real life experiments and may need human experts with experimental equipments to mark the labels, which can be slow and expensive. But unlabeled data is easily available in terms of web pages, data logs, images, audio, video les and DNA/RNA sequences. SSL uses large unlabeled and few labeled data to build better classifying functions which acquires higher accuracy and needs lesser human efforts. Thus it is of great empirical and theoretical interest. We contribute two SSL algorithms (i) adaptive anomaly detection (AAD) (ii) hybrid anomaly detection (HAD), which are self evolving and very efficient to detect anomalies in a large scale and complex data distributions. Our algorithms are capable of modifying an existing classier by both retiring old data and adding new data. This characteristic enables the proposed algorithms to handle massive and streaming datasets where other existing algorithms fail and run out of memory. As an application to semi-supervised anomaly detection and for experimental illustration, we have implemented a prototype of the AAD and HAD systems and conducted experiments in an on-campus cloud computing environment. Experimental results show that the detection accuracy of both algorithms improves as they evolves and can achieve 92.1% detection sensitivity and 83.8% detection specificity, which makes it well suitable for anomaly detection in large and streaming datasets. We compared our algorithms with two popular SSL methods (i) subspace regularization (ii) ensemble of Bayesian sub-models and decision tree classifiers. Our contributed algorithms are easy to implement, significantly better in terms of space, time complexity and accuracy than these two methods for semi-supervised anomaly detection mechanism.

Thesis (M.Com. (Finance))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2016 / This study aims to investigate the presence of the low market risk (beta) anomaly in the Johannesburg Stock Market (JSE). Finance theory suggests that with higher return comes higher risk. However, several studies have reported evidence of low risk anomaly in global markets where portfolios containing low beta shares delivers superior risk adjusted returns compared to market index and high beta shares' portfolio. This study will explore various risk return relationships on the JSE and test a variety of potential explanations of the anomalous behaviour of the low beta premium. Three explanations have been identified as potential factors that contribute to the persistence of the Low Beta Anomaly. These include; Net International Equity flows (NIEF), Idiosyncratic Risk and Market Concentration. The results are consistent with international literature indicating a persistent Low Beta Anomaly on the JSE. However, the results also indicate that in periods of turmoil, high beta shares outperform low beta shares i.e. during the Global Financial Crisis. Although some significant relationships are found between the low minus high beta differential and NIEF. NIEF is unable to suitably explain the anomaly. Idiosyncratic risk results are mixed depending on the model used to calculate the idiosyncratic risk estimates. Despite being a significant issue on the JSE, Market concentration does not explain the Low Beta Anomaly. As the superior performance of the low beta portfolios remains once the portfolios returns have been adjusted for the different variables however magnitude ofthe outperformance ofthe low beta portfolio was to a lesser degree. / AC2016

Este trabalho apresenta um procedimento de inversão magnética de três etapas no qual quantidades invariantes em relação à fonte magnética são sequencialmente invertidas para recuperar i) a geometria da fonte no substrato, ii) sua intensidade de magnetização e iii) a inclinação da magnetização da fonte. A primeira quantidade invertida (chamada função geométrica) é obtida pela razão entre a intensidade do gradiente da anomalia magnética e a intensidade do campo magnético anômalo. Para fontes homogêneas, a função geométrica depende apenas da geometria da fonte, o que permite a reconstrução da forma do corpo usando valores arbitrários para a magnetização. Na segunda etapa, a forma da fonte é fixa e a intensidade de magnetização é estimada ajustando o módulo do gradiente da anomalia magnética, uma quantidade invariante com a direção da magnetização e equivalente à amplitude do sinal analítico. Na última etapa, a forma da fonte e a intensidade da magnetização são fixas e a inclinação da magnetização é determinada ajustando a anomalia magnética. Além de recuperar a forma e a magnetização de fontes homogêneas, esta técnica permite, em alguns casos, verificar se as fontes magnéticas são homogêneas. Isto é possível pois a função geométrica de fontes heterogêneas pode ser ajustada por um modelo homogêneo, mas o modelo assim obtido não permite o ajuste da amplitude do sinal analítico nem da anomalia magnética. Esse é um critério que parece efetivo no reconhecimento de fontes fortemente heterogêneas. O método de inversão por etapas é testado em experimentos numéricos de computador e utilizado para interpretar uma anomalia magnética gerada por rochas básicas intrusivas da Bacia do Paraná. / This work presents a three step magnetic inversion procedure in which invariant quantities related to source parameters are sequentially inverted to provide i) cross-section of two-dimensional sources; ii)intensity of source magnetization, and iii) inclination of source magnetization. The first inverted quantity (called geometrical function) is obtained by rationing intensity gradient of total field anomaly and intensity of vector anomalous field. For homogenous sources, geometrical function depends only on source geometry thus allowing shape reconstruction by using arbitrary values for source magnetization. In the second step, source shape is fixed and magnetization intensity is estimated by fitting intensity gradient of total field anomaly, an invariant quantity with magnetization direction and equivalent to amplitude of the analytical signal. In the last step, source shape and magnetization intensity are fixed and magnetization inclination is determined by fitting magnetic anomaly. Besides furnishing shape and magnetization of homogeneous two-dimensional sources, this technique allows to check in some cases if causative sources are homogeneous. It is possible because geometrical function from inhomogeneous sources can be fitted by a homogeneous model but a model thus obtained does not fit the amplitude of analytical signal nor magnetic anomaly itself. This is a criterion that seems efective in recognizing strongly inhomogeneous sources. The proposed technique is tested with numerical experiments, and used to model a magnetic anomaly from intrusive basic rocks of Paraná Basin, Brazil.

"Visual analytics is an emerging field which uses visual techniques to interact with users in the analytical reasoning process. Users can choose the most appropriate representation that conveys the important content of their data by acting upon different visual displays. The data itself has many features of interest, including clusters, trends (commonalities) and anomalies. Most visualization techniques currently focus on the discovery of trends and other relations, where uncommon phenomena are treated as outliers and are either removed from the datasets or de-emphasized on the visual displays. Much less work has been done on the visual analysis of outliers, or anomalies. In this thesis, I will introduce a method to identify the different levels of “outlierness” by using interactive selection and other approaches to process outliers after detection. In one approach, the values of these outliers will be estimated from the values of their k-Nearest Neighbors and replaced to increase the consistency of the whole dataset. Other approaches will leave users with the choice of removing the outliers from the graphs or highlighting the unusual patterns on the graphs if points of interest lie in these anomalous regions. I will develop and test these anomaly handling methods within the XMDV Tool."