Thesis on "business plan" is focused on processing of current business plan of company DI-ELCOM Ltd.. Thesis offers a comprehensive evaluation of its activities. The main result of this work is set the strategy, that would drive the company to eliminate the negative factors, which could be internal or external origin. It is also evaluated existing key contract for the company.

This thesis aims to analyze the business environment and the conditions for international trade in Afghanistan. The main focus is on the analysis of the risks that emerge from these conditions.

L’analyse des risques liés aux objets rocheux (effondrement ou affaissement de terrain, écroulement de falaise, etc.) s’effectue généralement dans un contexte lourd en incertitudes. Malheureusement, les outils dont dispose aujourd’hui l’expert pour réaliser ses études souffrent de ne pouvoir apporter de solutions suffisamment précises pour réellement saisir ce contexte d’incertitude. Ce mémoire effectue d’abord un retour sur la notion d’incertitude dans les études de risques liés au sol et au sous-sol. Une définition et une typologie détaillée en sont proposées et des outils, issus de la littérature ou développés dans le cadre de la thèse, sont fournis pour permettre un traitement pratique de chacune des classes introduites. L’utilisation des probabilités comme outil d’aide à la prise en compte des incertitudes est ensuite examinée. Une discussion, confrontant les approches fréquentiste et épistémique, est proposée pour évaluer la possibilité et les limites d’une interprétation opérationnelle de résultats probabilistes dans une analyse de risque. Ce travail est principalement destiné à l’expert du terrain réalisant une étude de risque. Ainsi, plusieurs exemples réels (analyse de la stabilité d’une mine de charbon, étude de l’aléa fontis au droit d’une carrière souterraine de gypse, réalisation d’un Plan de Prévention des Risques Miniers) sont proposés pour éclairer les propos, illustrer l’adaptabilité des outils introduits aux méthodologies actuelles, et présenter les avantages concrets que le traitement des incertitudes et le calcul probabiliste peuvent apporter en terme d’aide à la démarche d’expertise et à la communication entre les acteurs de la gestion du risque / Analyses of risks related to ground and underground issues (surface collapses, subsidence, rockfalls, etc.) are generally undertaken in a strong context of uncertainty. However, tools that are available for geotechnical expert to carry out his study suffer today from not being able to really seize this context of uncertainty. This work takes firstly stock of the notion of uncertainty in risk analyses. It provides a definition and a typology of uncertainty that can be concretely used by the expert. For each of the defined classes, methods allowing an operational treatment of uncertainties are presented, either extracted from a literature survey or developed in the framework of the Thesis. The use of probabilities as an expertise-help tool is secondly examined. A discussion, which distinguishes between frequentist and epistemic interpretations of probabilities, is proposed to evaluate the benefits and implications that probabilities can have towards the practical elaboration of a risk analysis. This study is mainly dedicated to the field expert in charge of the analysis. Numerous concrete examples (analysis of surface stability above an underground coal mine, sinkhole development analysis over an underground gypsum mine, elaboration of a Mining Risk Prevention Plan) are therefore provided both to present the main results of the work and to illustrate the adaptability of the tools being introduced to the current methodologies of analysis. They also aim at highlighting the fact that the treatment of uncertainties and the use of probabilities in risk analyses can facilitate the process of expertise and allow a better communication between the various actors of risk management

Thesis advisor: Robert Taggart / My dissertation is comprised of three chapters. The first chapter is motivated by many lowfrequency sources of systemic risk in the economy. We propose a two-stage learning procedure to construct a high-frequency (i.e., daily) systemic risk factor from a cross-section of low-frequency (i.e., monthly) risk sources. In the first stage, we use a Kalman-Filter approach to synthesize the information about systemic risk contained in 19 different proxies for systemic risk. The low frequency (i.e., monthly) Bayesian factor can predict the cross-section of stock returns out of sample. In particular, a strategy that goes long the quintile portfolio with the highest exposure to the Bayesian factor and short the quintile portfolio with the lowest exposure to the Bayesian factor yields a Fama–French–Carhart alpha of 1.7% per month (20.4% annualized). The second stage is to convert this low frequency Bayesian factor into a high-frequency factor. We use textual analysis Word2Vec that reads the headlines and abstracts of all daily articles from the business section of the New York Times from 1980 to 2016 to collect distributional information on a per word basis and store it in high-dimensional vectors. These vectors are then used in a LASSO model to predict the Bayesian factor. The result is a series of coefficients that can then be used to produce a high-frequency estimate of the Bayesian factor of systemic risk. This high-frequency indicator is validated in several ways including by showing how well it captures the 2008 crisis. We also find that the high frequency factor is priced in the cross-section of stock returns and able to predict large swings in the VIX using a quantile regression approach, which sheds some light on the puzzling relation between the macro-economy and stock market volatility. The second chapter of my dissertation provides a basic quantitative description of a compendium of macro economic variables based on their ability to predict bond returns and stock returns . We use three methods( asymptotic PCA, LASSO and Support Vector Machine) to construct factors out of 133 monthly time series of economic activity spanning a period from 1996:1 to 2015:12 and classify these factors into two groups: bond demand factors and bond supply factors. In PCA regression, we find both demand factors and supply factors are unspanned by bond yields and have stronger predictability power for future bond excess returns than CP factors. This predictability finding is confirmed and enhanced by machine learning technique LASSO and Support Vector Machine. More interestingly, LASSO can be used to identify 15 most important economic variables and give direct economic explanations of predictors for bond returns. Regarding to stock predictability, we find both demand and supply PC factors are priced by the cross-section of stock returns. In particular, portfolios with highest exposure to aggregate supply factor outperform portfolios with lowest exposure to aggregate supply factor 1.8% per month while portfolios with lowest exposure to aggregate demand factor outperform portfolios with highest exposure to aggregate demand factor 2.1% per month. The finding is consistent with ”fly to safety” explanation. Furthermore, variance decomposition from VAR shows that demand factors are much more important than supply factors in explaining asset returns. Finally, we incorporate demand factors and supply factors into macrofinance affine term structure (MTSMs) to estimate market price of risk of factors and find that demand factors affect level risk and supply factors affect slope risk. Moreover, MTSMs enable us to decompose bond yields into expectation component and yield risk premium component and we find MTSMs without macro factors under-estimate yield risk premium. The third chapter,coauthored with Dmitriy Muravyev and Aurelio Vasquez, is motived from the fact that a typical stock has hundreds of listed options. We use principal component analysis (PCA) to preserve their rich information content while reducing dimensionality. Applying PCA to implied volatility surfaces across all US stocks, we find that the first five components capture most of the variation. The aggregate PC factor that combines only the first three components predicts future stock returns up to six months with a monthly alpha of about 1%; results are similar out-of-sample. In joint regressions, the aggregate PC factor drives out all of the popular option-based predictors of stock returns. Perhaps, the aggregate factor better aggregates option price information. However, shorting costs in the underlying drive out the aggregate factor’s predictive ability. This result is consistent with the hypothesis that option prices predict future stock returns primarily because they reflect short sale constraints. / Thesis (PhD) — Boston College, 2018. / Submitted to: Boston College. Carroll School of Management. / Discipline: Finance.

Historically, performance has been the most important feature when optimizing computer hardware. Modern processors are so highly optimized that every cycle of computation time matters. However, this practice of optimizing for performance at all costs has been called into question by new microarchitectural attacks, e.g. Meltdown and Spectre. Microarchitectural attacks exploit the effects of microarchitectural components or optimizations in order to leak data to an attacker. These attacks have caused processor manufacturers to introduce performance impacting mitigations in both software and silicon. To investigate the performance impact of the various mitigations, a test suite of forty-seven different tests was created. This suite was run on a series of virtual machines that tested both Ubuntu 16 and Ubuntu 18. These tests investigated the performance change across version updates and the performance impact of CPU core number vs. default microarchitectural mitigations. The testing proved that the performance impact of the microarchitectural mitigations is non-trivial, as the percent difference in performance can be as high as 200%.

The diploma thesis introduces processing of an analysis that is realized within an engineering company that had decided to transfer its production from Nordic countries such as Norway and Sweden to the Czech Republic. The company decided to deal with the transfer without any use of project management and planning according to project management and its techniques. Therefore the diploma thesis is supposed to present how the project could look like if it was done by project methodology. The main parts of project are analysis of time, sources, costs and risk analysis. These are preceded by situation analysis which is resulted into SWOT. Factors mentioned in SWOT are used as inputs into risk analysis. Subsequently the risks are identified, evaluated and the proposals for reducing the risks are presented.

The diploma thesis titled Risk of dropshipping and B2C sale deals with the analysis and evaluation risks in proces of executing an order at e-shop Zahradavpohode.cz. This businnes subject in this proces uses two business models – dropshipping and B2C sale. The theoretical part is serving to gain information about problmatics. In the analytical part this knowledge is used for analysis of currnet staus, proces, identification and evaluation risks. The design part is making conclusions from the analyzes, proposing and looking fot a available use of company businness models view finances and risks.

The diploma thesis deals with the issue of risk management in project management. It focuses on a project that describes a change in the holiday process and introduces a motivation system for employees. The thesis is divided into three parts. The first part describes the theoretical basis based on professional literature. The analytical part describes the current state of the process, the company and identifies the risks. The draft section proposes measures to reduce the risks in the project, and recommendations are made to increase the likelihood of adopting the proposed changes.

The Diploma thesis is focused on evaluation of company’s investment plan to build both production and packaging line, to pursue production of syrups. Main part of the Diplo-ma thesis assess effectivnes of the investment by correctly chosen indicators of dynamic methods. Monte Carlo method was used to compare results of effectivnes of the simula-tion with traditional methods. Reccommendations of project’s feasibility are concluded in the last part of the thesis.

This diploma thesis solves the proposal for personal data protection according to the Regulation (EU) 2016/679 of 27 April 2016 (generally known under the abbreviation GDPR) in a chosen company. The thesis begins with a theoretical background followed by the part devoted to the analysis of the company. These results are further confronted with the own solution which is presenting the proposals and recommendations to bring the selected company into line with the GDPR.