Post Earnings Announcement Drift (PEAD) is a market anomaly that challenge the “Efficient Market Hypothesis” (EMH). It was first discovered in 1968 by Ball and Brown. When firms on the stock market have their earnings announcement the stock price will be affected and tend to drift up or down in price for days, weeks or months. Based on the limited research studies available there is acceptance that PEAD exists in the Stockholm stock exchange but depending on how measured the effect can strongly differ. In this master thesis we will study PEAD anomaly in the Swedish stock market and how pronounced it is on the stock’s sector, beta and trading volume. This study is an event and quantitative study which analyses firms on the Stockholm exchange market during the period between January 2007 to December 2022. A price measurement methodology has been used where the benchmark for abnormal (or excess) returns is the index of the list. Evidence shows that PEAD is present in the Stockholm Stock Exchange but that the effect is limited. The fact that the event abnormal returns are significant regarding of the returns up to after 60 trading days (although on a very small effect) provides insight and understanding of the effect. This study has also provided insight that beta and sector is a relevant PEAD parameter, maybe as important as the abnormal returns in the event itself. Trading volume have not provided any insight on PEAD in this study.

Time series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep learning algorithms. The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminating features and time-series temporal nature. However, their performance is affected by the speed at which the time series arrives, the use of a fixed threshold, and the assumption of Gaussian distribution on the prediction error to identify anomalous values. An exact parametric distribution is often not directly relevant in many applications and it’s often difficult to select an appropriate threshold that will differentiate anomalies with noise. Thus, implementations need the Prediction Interval (PI) that quantifies the level of uncertainty associated with the Deep Neural Network (DNN) point forecasts, which helps in making a better-informed decision and mitigates against false anomaly alerts. To achieve this, a new anomaly detection method is proposed that computes the uncertainty in estimates using quantile regression and used the quantile interval to identify anomalies. Similarly, to handle the speed at which the data arrives, an online anomaly detection method is proposed where a model is trained incrementally to adapt to the concept drift that improves prediction. This is implemented using a window-based strategy, in which a time series is broken into sliding windows of sub-sequences as input to the model. To adapt to concept drift, the model is updated when changes occur in the new arrival instances. This is achieved by using anomaly likelihood which is computed using the Q-function to define the abnormal degree of the current data point based on the previous data points. Specifically, when concept drift occurs, the proposed method will mark the current data point as anomalous. However, when the abnormal behavior continues for a longer period of time, the abnormal degree of the current data point will be low compared to the previous data points using the likelihood. As such, the current data point is added to the previous data to retrain the model which will allow the model to learn the new characteristics of the data and hence adapt to the concept changes thereby redefining the abnormal behavior. The proposed method also incorporates feature extraction to capture structural patterns in the time series. This is especially significant for multivariate time-series data, for which there is a need to capture the complex temporal dependencies that may exist between the variables. In summary, this thesis contributes to the theory, design, and development of algorithms and models for the detection of anomalies in both static and evolving time series data. Several experiments were conducted, and the results obtained indicate the significance of this research on offline and online anomaly detection in both static and evolving time-series data. In chapter 3, the newly proposed method (Deep Quantile Regression Anomaly Detection Method) is evaluated and compared with six other prediction-based anomaly detection methods that assume a normal distribution of prediction or reconstruction error for the identification of anomalies. Results in the first part of the experiment indicate that DQR-AD obtained relatively better precision than all other methods which demonstrates the capability of the method in detecting a higher number of anomalous points with low false positive rates. Also, the results show that DQR-AD is approximately 2 – 3 times better than the DeepAnT which performs better than all the remaining methods on all domains in the NAB dataset. In the second part of the experiment, sMAP dataset is used with 4-dimensional features to demonstrate the method on multivariate time-series data. Experimental result shows DQR-AD have 10% better performance than AE on three datasets (SMAP1, SMAP3, and SMAP5) and equal performance on the remaining two datasets. In chapter 5, two levels of experiments were conducted basis of false-positive rate and concept drift adaptation. In the first level of the experiment, the result shows that online DQR-AD is 18% better than both DQR-AD and VAE-LSTM on five NAB datasets. Similarly, results in the second level of the experiment show that the online DQR-AD method has better performance than five counterpart methods with a relatively 10% margin on six out of the seven NAB datasets. This result demonstrates how concept drift adaptation strategies adopted in the proposed online DQR-AD improve the performance of anomaly detection in time series. / Petroleum Technology Development Fund (PTDF)

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Revisionskvaliteten har blivit kritiserad efter skandaler som exempelvis Enron och Wirecard. Skandalerna gjorde att förtroendet för revisionsbranschen försvagades. För att stärka förtroendet föreslogs utökade krav och begränsningar. En av begränsningarna varrevisorsrotation där det infördes utökade krav hur länge revisorn kan behålla ett uppdrag med samma kund. Kraven kom bara att gälla publika aktiebolag, därav valdes privata aktiebolag med revisor där tidigare forskning varit begränsad. Forskningen inom publika aktiebolag har varit oense huruvida revisorns uppdragslängd påverkar revisionskvaliteten negativt eller positivt. Syftet med studien är att bidra med ökad kunskap kring om det finns ett samband mellan längden på revisionsuppdraget och revisionskvaliteten hos svenska privata aktiebolag. Utifrån resultatet, ämnar studien erhålla en indikation huruvida obligatorisk revisorsrotation kan vara ett sätt att stärka revisionskvaliteten även för privata aktiebolag. Forskningsfrågan formulerades därför som följande “finns det ett samband mellan längden på revisionsuppdraget och revisionskvaliteten hos svenska privata aktiebolag?”. För att mäta revisionskvaliteten användes oväntade periodiseringar genom en modifierad Jonesmodell. Träffsäkerheten av fortsatt drift kommentarer undersöktes även genom typ-II fel som ett tillläggstest.  Data är insamlad för 50 422 svenska privata aktiebolag med revisor och bokslut under 2022. Resultatet visar att det finns ett positivt samband mellan revisorns uppdragslängd och revisionskvalitet mätt som resultatmanipulation genom oväntade periodiseringar.Kopplat till revisionskvalitet sker en förbättring vid längre uppdrag. Uppdragslängden delades upp i tre kategorier där studien fann att korta uppdrag hade signifikant mer oväntade periodiseringar jämfört med medel och långa. Det fanns ingen signifikant skillnad mellan medel och långa uppdrag, vilket innebär att det inte observerats någon minskning av revisionskvalitet till följd av längre uppdrag. Tilläggstestet genomfördes på 256 svenska privata aktiebolag med revisor. Bolagen hade alla bokslut under 2022 och sedan gått i konkurs. Resultatet visar en indikation att träffsäkerheten av fortsatt drift kommentarer minskar när revisorns uppdragslängd ökar. Det finns däremot inga signifikanta resultat vilket gör att inget uttalande kan göras gällande sambandet.  Studien bidrar med bredare kunskaper om revisionskvalitet inom privata aktiebolag. Slutsatserna blir således att det inte finns några indikationer på att revisorsrotation bör införas även för privata aktiebolag. För revisionsbyråerna har inget uppmärksammats som tyder på att rutiner ska ses över gällande revisorrotation för att stärka revisonskvaliteten.

The presented research investigated the viability of a double-braided synthetic fiber rope for providing improved performance of steel moment frames subjected to earthquake-induced ground motions. A series of experimental tests, including a 1:3-scale dynamic test and 1:6-scale shaking table tests, was conducted using Northridge ground-motion input. A series of nonlinear dynamic analytical studies, using DRAIN-2DX, was conducted to develop the experimental tests. Throughout experimental testing, the ropes exhibited a hyper-elastic loading response and a reduced-stiffness unloading response. A conditioning cycle was defined as a loading cycle induced in the rope above the highest load expected to be experienced by the rope, and was determined to be requisite for ropes intended to be used for the stated objectives of the research program. After experiencing a conditioning cycle, the rope response returned to initial conditions without permanent deformation, demonstrating repeatability of response through several loading cycles below the conditioning load. In the 1:6-scale shaking-table experiments, the ropes drastically improved the performance of the steel moment frames. Maximum and residual drift were reduced significantly, with a corresponding minimal increase to the maximum base shear. Base shear was reduced at several peaks subsequent to the initial pulse of the Northridge ground-motion input. The analytical model developed was excellent for predicting elastic response of the 1:6-scale shaking table experiments and adequate for the purpose of planning shaking table studies. Correlation of peak rope forces between the analytical model and experimental results was poor, and was attributed to limitations of the pre-defined elements used to represent the rope devices in the software program. The inability of the elements to capture the complex unloading response of the rope was specifically noted. / Ph. D.

La Tesis Doctoral estudia la observaci'on en tiempo real de la concentraci'on en el colector de escape de 'oxidos de nitr'ogeno (NOx) y del dosado en motores diesel sobrealimentados (¿ '1 ). Para ello se combinan dos fuentes de informaci'on diferentes: ¿ Sensores capaces de proporcionar una media de dichas variables, ¿ y modelos orientados a control que estiman estas variables a partir de otras medidas del motor. El trabajo parte de la evaluaci'on de la precisi'on de los sensores, realizada mediante la comparaci'on de su medida con la proporcionada por equipos anal'¿ticos de alta precisi'on, que son usados como est'andares de calibraci'on est'atica. Tambi'en se desarrollan en la Tesis m'etodos para la calibraci'on de la din'amica del sensor; dichos m'etodos permiten identi¿car un modelo de comportamiento del sensor y revelar su velocidad de respuesta. En general, estos sensores demuestran ser precisos pero relativamente lentos. Por otra parte, se proponen modelos r'apidos para la estimaci'on de NOx y ¿ '1 . Estos m'etodos, basados en relaciones f'¿sicas, tablas de par'ametros y una serie de correcciones, emplean las medidas proporcionadas por otros sensores con el ¿n de proporcionar una estimaci'on de las variables de inter'es. Los modelos permiten una estimaci'on muy r'apida, pero resultan afectados por efectos de deriva que comprometen su precisi'on. Con el ¿n de aprovechar las caracter'¿sticas din'amicas del modelo y mantener la precisi'on en estado estacionario del sensor, se proponen t'ecnicas de fusi'on de la informaci'on basadas en la aplicaci'on de ¿ltros de Kalman (KF). En primer lugar, se dise¿na un KF capaz de combinar ambas fuentes de informaci'on y corregir en tiempo real el sesgo entre las dos se¿nales. Posteriormente, se estudia la adaptaci'on en tiempo real de los par'ametros del modelo con el ¿n de corregir de forma autom'atica los problemas de deriva asociados al uso de modelos. Todos los m'etodos y procedimientos desarrollados a lo largo de la presente Tesis Doctoral se han aplicado de forma experimental a la estimaci'on de NOx y ¿ '1 . De forma adicional, la Tesis Doctoral desarrolla aspectos relativos a la transferencia de estos m'etodos a los motores de serie. / The dissertation covers the problem of the online estimation of diesel engine exhaust concentrations of NOx and '1. Two information sources are utilised: ¿ on-board sensors for measuring NOx and '1, and ¿ control oriented models (COM) in order to predict NOx and '1. The evaluation of the static accuracy of these sensors is made by comparing the outputs with a gas analyser, while the dynamics are identified on-board by perform- ing step-like transitions on NOx and '1 after modifying ECU actuation variables. Different methods for identifying the dynamic output of the sensors are developed in this work; these methods allow to identify the time response and delay of the sensors if a sufficient data set is available. In general, these sensors are accurate but present slow responses. Afterwards, control oriented models for estimating NOx and '1 are proposed. Regarding '1 prediction, the computation is based on the relative fuel-to-air ratio, where fuel comes from an ECU model and air mass flow is measured by a sensor. For the case of NOx, a set-point relative model based on look-up tables is fitted for representing nominal engine emissions with an exponential correction based on the intake oxygen variation. Different corrections factor for modeling other effects such as the thermal loading of the engine are also proposed. The model is able to predict NOx fast with a low error and a simple structure. Despite of using models or sensors, model drift and sensor dynamic deficiencies affect the final estimation. In order to solve these problems, data fusion strategies are proposed by combining the steady-state accuracy of the sensor and the fast estimation of the models by means of applying Kalman filters (KF). In a first approach, a drift correction model tracks the bias between the model and the sensor but keeping the fast response of the model. In a second approach, the updating of look-up tables by using observers is coped with different versions based on the extended Kalman filter (EKF). Particularly, a simplified KF allows to observe the parameters with a low computational effort. Finally, the methods and algorithms developed in this work are combined and applied to the estimation of NOx and '1. Additionally, the dissertation covers aspects relative to the implementation of the methods in series engines. / Blanco Rodríguez, D. (2013). Modelling and observation of exhaust gas concentrations for diesel engine control [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/32666 / Premios Extraordinarios de tesis doctorales