Dissertations / Theses on the topic 'The market methods'

We design, implement and evaluate statistical methods for managing uncertainty when consuming and provisioning resources in a federated computational market. To enable efficient allocation of resources in this environment, providers need to know consumers' risk preferences, and the expected future demand. The guarantee levels to offer thus depend on techniques to forecast future usage and to accurately capture and model uncertainties. Our main contribution in this thesis is threefold; first, we evaluate a set of techniques to forecast demand in computational markets; second, we design a scalable method which captures a succinct summary of usage statistics and allows consumers to express risk preferences; and finally we propose a method for providers to set resource prices and determine guarantee levels to offer. The methods employed are based on fundamental concepts in probability theory, and are thus easy to implement, as well as to analyze and evaluate. The key component of our solution is a predictor that dynamically constructs approximations of the price probability density and quantile functions for arbitrary resources in a computational market. Because highly fluctuating and skewed demand is common in these markets, it is difficult to accurately and automatically construct representations of arbitrary demand distributions. We discovered that a technique based on the Chebyshev inequality and empirical prediction bounds, which estimates worst case bounds on deviations from the mean given a variance, provided the most reliable forecasts for a set of representative high performance and shared cluster workload traces. We further show how these forecasts can help the consumers determine how much to spend given a risk preference and how providers can offer admission control services with different guarantee levels given a recent history of resource prices.
QC 20100909

Thesis (M.S.)--West Virginia University, 2007.
Title from document title page. Document formatted into pages; contains vi, 104 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 100-104).

This dissertation presents an exploration of the use of nonparametric statistical methods based on ranks for use in financial market research. Applications to event study methodology and the estimation of security systematic risk are analyzed using a simulation methodology with actual daily security return data. The results indicate that procedures based on ranks are more efficient than normal theory procedures currently in common use.

Deep learning methods have the ability to accurately predict and interpret what data represents. However, the decision making of a deep learning model is not comprehensible for humans. This is a problem for sectors like market surveillance which needs clarity in the decision making of the used algorithms. This thesis aimed to investigate how a deep learning model can be constructed to make the decision making of the model humanly comprehensible, and to investigate the potential impact on classification performance. A literature study was performed and publicly available explanation methods were collected. The explanation methods LIME, SHAP, model distillation and SHAP TreeExplainer were implemented and evaluated on a ResNet trained on three different time-series datasets. A decision tree was used as the student model for model distillation, where it was trained with both soft and hard labels. A survey was conducted to evaluate if the explanation method could increase comprehensibility. The results were that all methods could improve comprehensibility for people with experience in machine learning. However, none of the methods could provide full comprehensibility and clarity of the decision making. The model distillation reduced the performance compared to the ResNet model and did not improve the performance of the student model.
Djupinlärningsmetoder har egenskapen att förutspå och tolka betydelsen av data. Däremot så är djupinlärningsmetoders beslut inte förståeliga för människor. Det är ett problem för sektorer som marknadsövervakning som behöver klarhet i beslutsprocessen för använda algoritmer. Målet för den här uppsatsen är att undersöka hur en djupinlärningsmodell kan bli konstruerad för att göra den begriplig för en människa, och att undersöka eventuella påverkan av klassificeringsprestandan. En litteraturstudie genomfördes och publikt tillgängliga förklaringsmetoder samlades. Förklaringsmetoderna LIME, SHAP, modelldestillering och SHAP TreeExplainer blev implementerade och utvärderade med en ResNet modell tränad med tre olika dataset. Ett beslutsträd användes som studentmodell för modelldestillering och den blev tränad på båda mjuka och hårda etiketter. En undersökning genomfördes för att utvärdera om förklaringsmodellerna kan förbättra förståelsen av modellens beslut. Resultatet var att alla metoder kan förbättra förståelsen för personer med förkunskaper inom maskininlärning. Däremot så kunde ingen av metoderna ge full förståelse och insyn på hur beslutsprocessen fungerade. Modelldestilleringen minskade prestandan jämfört med ResNet modellen och förbättrade inte prestandan för studentmodellen.

The European Union has set environmental targets on climate change in three areas: energy efficiency, renewable energy sources, and reduction of emissions. These targets are the main driver for the change in today’s power system. The defined targets do not only affect the production and distribution of electricity but also raise questions on how electricity is being consumed. An essential building block of an efficient power system is often referred to as the smart grid. One of the important components of a smart grid is dynamic market models that facilitate demand response. Residential customers account for a relatively large portion of the total electricity consumption in Europe but relatively little is known about dynamic market models used in the residential sector. Pilot projects concerning dynamic market models have been conducted, but there is a lack of common evaluation methods to assess them.    This report investigates how pilot projects concerning demand response can be evaluated and presents a compilation of 135 international pilot projects and their results. The evaluations methods and findings from the international compilation are then applied to assess the proposed dynamic market model for the Stockholm Royal Seaport. Four evaluation criterions have been identified. The first relates to the demand response resource that is being studied and its impact on the results of the pilot project. Secondly, design principles of the pilot project must be considered. Thirdly, the division of costs and benefits among stakeholders must be calculated. Lastly, the precision of these measures must be taken into consideration. The compilation of international pilot projects reveals that dynamic markets models are often combined with modern technology. Combinations of market models, feedback and technology have an impact on overall electricity conservation and peak reduction. The reductions also depend on what electrical appliances are being used by the household members and their willingness to change their behavior. Customer acceptance is generally large among participants in pilot projects concerning dynamic market models. The hypothesis for the Stockholm Royal Seaport, in which five to fifteen percent of the load can be shifted from peak hours to off-peak hours with the proposed market model for the Stockholm Royal Seaport, is estimated to be reasonable. Load shift would lead to savings in the range between 64 – 266 SEK per year, which accounts for 1 – 4 % in bill savings. If the proposed dynamic market model is compared to fixed one month contracts, which includes retail price and fixed network tariffs, the bill savings were estimated to have been 563-766 SEK in year 2010. This corresponds to bill savings in the range of 8 – 11 %. Keywords: Smart grid, demand response, market model, evaluation methods for pilot projects, Stockholm Royal Seaport

The development of time series techniques associated with non stationary data, such as the testing for unit roots and cointegration has presented the applied worker with new challenges in the applied analysis of economic problems.This thesis uses some of these methods to consider a number of questions in the area of labour and financial markets.In particular the thesis considers the application of these methods to two general questions, the specification of the aggregate wage equation in Australia and the efficiency of the Australian stock market. More specifically the thesis focuses on the time series properties of variables commonly used in specifications of the wage equation and then tests them for cointegration. In the financial economics area the thesis tests for the gains to portfolio diversification from the perspective of an Australian investor and the applicability of the present value model of stock prices to the Australian stock market.

A persistent practice in the retail produce market is the mixed use of per unit and per pound pricing for bulk produce commodities. While per pound pricing explicitly prices the size dimension of the produce, per unit pricing (known in the industry as "by the each" pricing) is a form of average pricing whereby units differing in size and value are sold for the same price. When goods are average priced, opportunities exist for buyers to find units of exceptional value at the going price. Exploiting these opportunities requires buyers to measure and compare the values of individual units. Measurement of this kind often results in costly wealth transfers among buyers and between buyers and sellers. Profit maximization implies that sellers will avoid average pricing and its associated measurement costs whenever alternative pricing methods can be implemented at lower cost. This study examines the implications of measurement costs in the retail produce market, and develops predictions concerning the seller's decision to set an average price (price per each) or a price per pound. Logistic regression analysis is used to test the predictions on retail price data from major retailers in Bozeman, Montana. The results suggest that sellers choose between the two pricing methods in a manner that is consistent with the minimization of pre-sale measurement costs.

Since the liberalisation of the energy market in Europe in the early 1990s, much opportunity to trade electricity as a commodity has arisen. One significant consequence of this movement is that market prices have become more volatile instead of its tradition constant rate of supply. Spot price markets have also been introduced, affecting the demand of electricity as companies now have the option to not only produce their own supply but also purchase this commodity from the market. Following the liberalisation of the energy market, hence creating a greater demand for trading of electricity and other types of energy, various types of options related to the sales, storage and transmission of electricity have consequently been introduced. Particularly, swing options are popular in the electricity market. As we know, swing-type derivatives are given in various forms and are mainly traded as over-the-counter (OTC) contracts at energy exchanges. These options offer flexibility with respect to timing and quantity. Traditionally, the Geometric Brownian Motion (GBM) model is a very popular and standard approach for modelling the risk neutral price dynamics of underlyings. However, a limitation of this model is that it has very few degrees of freedom, as it does not capture the complex behaviour of electricity prices. In short the GBM model is inefficient in the pricing of options involving electricity. Other models have subsequently been used to bridge this inadequacy, e.g. spot price models, futures price models, etc. To model risk-neutral commodity prices, there are basically two different methodologies, namely spot and futures or so-called term structure models. As swing options are usually written on spot prices, by which we mean the current price at which a particular commodity can be bought or sold at a specified time and place, it is important for us to examine these models in order to more accurately inculcate their effect on the pricing of swing options. Monte Carlo simulation is also a widely used approach for the pricing of swing options in the electricity market. Theoretically, Monte Carlo valuation relies on risk neutral valuation and the technique used is to simulate as many (random) price paths of the underlying(s) as possible, and then to average the calculated payoff for each path, discounted to today's prices, giving the value of the desired derivative. Monte Carlo methods are particularly useful in the valuation of derivatives with multiple sources of uncertainty or complicated features, like our electricity swing options in question. However, they are generally too slow to be considered a competitive form of valuation, if any analytical techniques of valuation exist. In other words, the Monte Carlo approach is, in a sense, a method of last resort. In this thesis, we aim to examine a numerical method involved in the pricing of swing options in the electricity market. We will consider an existing and widely accepted electricity price process model, use the finite volume method to formulate a numerical scheme in order to calibrate the prices of swing options and make a comparison with numerical solutions obtained using the theta-scheme. Further contributions of this thesis include a comparison of results and also a brief discussion of other possible methods.

Thesis (doctoral)--Swedish University of Agricultural Sciences, 2006.
Thesis documentation sheet inserted. Appendix reprints four papers and manuscripts, two co-authored with others. Includes bibliographical references. Also issued electronically via World Wide Web in PDF format; online version lacks appendix.

Gaussian process regression (GPR) is a kernel-based nonparametric method that has been proved to be effective and powerful in many areas, including time series prediction. In this thesis, we focus on GPR and its extensions and then apply them to financial time series prediction. We first review GPR, followed by a detailed discussion about model structure, mean functions, kernels and hyper-parameter estimations. After that, we study the sensitivity of hyper-parameter and performance of GPR to the prior distribution for the initial values, and find that the initial hyper-parameters’ estimates depend on the choice of the specific kernels, with the priors having little influence on the performance of GPR in terms of predictability. Furthermore, GPR with Student-t process (GPRT) and Student-t process regression (TPR), are introduced. All the above models as well as autoregressive moving average (ARMA) model are applied to predict equity indices. We find that GPR and TPR shows relatively considerable capability of predicting equity indices so that both of them are extended to state-space GPR (SSGPR) and state-space TPR (SSTPR) models, respectively. The overall results are that SSTPR outperforms SSGPR for the equity index prediction. Based on the detailed results, a brief market efficiency analysis confirms that the developed markets are unpredictable on the whole. Finally, we propose and test the multivariate GPR (MV-GPR) and multivariate TPR (MV-TPR) for multi-output prediction, where the model settings, derivations and computations are all directly performed in matrix form, rather than vectorising the matrices involved in the existing method of GPR for multi-output prediction. The effectiveness of the proposed methods is illustrated through a simulated example. The proposed methods are then applied to stock market modelling in which the Buy&Sell strategies generated by our proposed methods are shown to be profitable in the equity investment.

The increasing penetration of proactive agents in distribution systems (DS) has opened new possibilities to make the grid more resilient and to increase participation of responsive loads (RL) and non-conventional generation resources. On the resiliency side, plug-in hybrid electric vehicles (PHEV), energy storage systems (ESS), microgrids (MG), and distributed energy resources (DER), can be leveraged to restore critical load in the system when the utility system is not available for extended periods of time. Critical load restoration is a key factor to achieve a resilient distribution system. On the other hand, existing DERs and responsive loads can be coordinated in a market environment to contribute to efficiency of electricity consumption and fair electricity tariffs, incentivizing proactive agents' participation in the distribution system. Resiliency and market applications for distribution systems are highly complex decision-making problems that can be addressed using modern optimization techniques. Complexities of these problems arise from non-linear relations, integer decision variables, scalability, and asynchronous information. On the resiliency side, existing models include optimization approaches that consider system's available information and neglect asynchrony of data arrival. As a consequence, these models can lead to underutilization of critical resources during system restoration. They can also become computationally intractable for large-scale systems. In the market design problem, existing approaches are based on centralized or computational distributed approaches that are not only limited by hardware requirements but also restrictive for active participation of the market agents. In this context, the work of this dissertation results in major contributions regarding new optimization algorithms for market design and resiliency improvement in distribution systems. In the DS market side, two novel contribution are presented: 1) A computational distributed coordination framework based on bilateral transactions where social welfare is maximized, and 2) A fully decentralized transactive framework where power suppliers, in a simultaneous auction environment, strategically bid using a Markowitz portfolio optimization approach. On the resiliency side, this research proposed a system restoration approach, taking into account uncertain devices and associated asynchronous information, by means of a two-module optimization models based on binary programming and three phase unbalanced optimal power flow. Furthermore, a Reinforcement Learning (RL) method along with a Monte Carlo tree search algorithm has been proposed to solve the scalability problem for resiliency enhancement.
Doctor of Philosophy
Distribution systems (DS) are evolving from traditional centralized and fossil fuel generation resources to networks with large scale deployment of responsive loads and distributed energy resources. Optimization-based decision-making methods to improve resiliency and coordinate DS participants are required. Prohibitive costs due to extended power outages require efficient mechanisms to avoid interruption of service to critical load during catastrophic power outages. Coordination mechanisms for various generation resources and proactive loads are in great need. Existing optimization-based approaches either neglect the asynchronous nature of the information arrival or are computationally intractable for large scale system. The work of this dissertation results in major contributions regarding new optimization methods for market design, coordination of DS participants, and improvement of DS resiliency. Four contributions toward the application of optimization approaches for DS are made: 1) A distributed optimization algorithm based on decomposition and best approximation techniques to maximize social welfare in a market environment, 2) A simultaneous auction mechanism and portfolio optimization method in a fully decentralized market framework, 3) Binary programming and nonlinear unbalanced power flow, considering asynchronous information, to enhance resiliency in a DS, and 4) A reinforcement learning method together with an efficient search algorithm to support large scale resiliency improvement models incorporating asynchronous information.

Emerging insurance markets, have limited access to financial instruments that they can use to create common hedge(s) to manage foreign exchange risk. This is the first empirical study to focus on the limitations when modelling foreign exchange rate transaction exposure in emerging insurance markets. This work is based on the cash flow methodology proposed by Martin and Mauer (2003, 2005) in reference to banks, and employed by Li et al. (2009) when assessing US insurance companies. Some econometric methodological innovations have been introduced to study the limitations of modelling foreign exchange rate transaction exposure in emerging insurance markets. An extensive literature review is followed by a quantitative investigation, to answer the following research questions. 1) Is the foreign exchange transaction exposure, as measured by a fundamental (economic) method of modelling the interplay of foreign exchange rates with other economic variables, significant, for all Egyptian insurance companies? 2) Is the foreign exchange transaction exposure, as measured by a technical (statistical) way of modelling the interplay of foreign exchange rates with other economic variables, significant for all Egyptian insurance companies? 3) Is the exchange transaction exposure for the Egyptian insurance industry, as a whole, significant? Although the foreign exchange rate transaction exposure for the Egyptian insurance industry, as a whole, is insignificant (question3), the percentage of Egyptian insurers affected by foreign exchange rate transaction exposure in US dollars, estimated at the individual firm level, was found to be 22% (question 1) and 35% (question2) respectively.

Human capital accumulation and its effect on labour market outcomes have been in the focus of economic research for decades. Traditionally the economic literature suggests that there might exist several forms of human capital, where human capital represents the knowledge, skills and health embodied in individuals. Skills and knowledge are largely acquired through education and experience but may also reflect, in part, innate abilities. In addition, some aspects of motivation and behaviour, as well as attributes such as the physical, emotional and mental health of individuals are also considered as human capital. These activities are referred to as human capital because people cannot be separated from their knowledge, skills and health, in the way they are separated from their physical and capital assets. Human capital accumulation is an important determinant of individuals' earning capacity and employment prospects, therefore plays an important role in determining the level and distribution of income in society. Moreover, the costs of these investments include direct outlays on market goods and the opportunity cost of the time that must be withdrawn from competing uses. Apart from direct investments in human capital people could also invest in constructing a network of relationships for example to find a job. Until now all these aspects have been studied separately, in this thesis I try to reconcile them.

The purpose of the diploma thesis was to assess the applicability of Market Approach in business valuation of the Czech entities. The first part comprises basic description of Valuation methods, the core of the thesis describes the guideline publicly traded companies method - multiples, comparable companies etc. -, guideline transaction method and industry multiples method. The application part consists of two valuations using guideline publicly traded companies method - the valuation of Komercni banka and of Trinecke zelezarny.

Emerging insurance markets, have limited access to financial instruments that they can use to create common hedge(s) to manage foreign exchange risk. This is the first empirical study to focus on the limitations when modelling foreign exchange rate transaction exposure in emerging insurance markets. This work is based on the cash flow methodology proposed by Martin and Mauer (2003, 2005) in reference to banks, and employed by Li et al. (2009) when assessing US insurance companies. Some econometric methodological innovations have been introduced to study the limitations of modelling foreign exchange rate transaction exposure in emerging insurance markets. An extensive literature review is followed by a quantitative investigation, to answer the following research questions. 1) Is the foreign exchange transaction exposure, as measured by a fundamental (economic) method of modelling the interplay of foreign exchange rates with other economic variables, significant, for all Egyptian insurance companies? 2) Is the foreign exchange transaction exposure, as measured by a technical (statistical) way of modelling the interplay of foreign exchange rates with other economic variables, significant for all Egyptian insurance companies? 3) Is the exchange transaction exposure for the Egyptian insurance industry, as a whole, significant? Although the foreign exchange rate transaction exposure for the Egyptian insurance industry, as a whole, is insignificant (question3), the percentage of Egyptian insurers affected by foreign exchange rate transaction exposure in US dollars, estimated at the individual firm level, was found to be 22% (question 1) and 35% (question2) respectively.

Inadequate resources, poor market strategy, competition, contract regulation, and disparate performance outcomes are issues small business owners face while competing for government contracts. The purpose of this correlational study was to examine the market orientation-business performance relationship and the influence of market factors among veteran-owned small businesses competing for government contracts in the United States. A survey with adapted MARKOR and Government Regulation Lassez-Faire scales was administered to 203 veteran-owned small business owners. Resource-advantage theory served as the theoretical foundation for this study. The results of the multiple linear regression were significant, suggesting that market orientation relates to firm performance and total contract revenue. However, the regression models had a poor fit, with R² values ranging from .019 to .094, suggesting that significant results of this study lacked the power to conclude predictive accuracy. Market orientation did not significantly relate to contract bid to win rate and number of years in the government market. The PROCESS moderation analysis provided mixed results for market factors’ influence on the market orientation relationship with business performance outcomes. Study participants were market-oriented, with few seeing corresponding success. The introduction of new variables is necessary to make future models useful. Implications for positive social change include guidance for better-fitting models, ones that will inform the efforts to improve the survivability of small businesses in the B2G market. Veteran-owned small business owners should not waste resources on market orientation as a sole strategic focus for capturing and winning government contracts.

The production and consumption of electricity must be in balance in order to maintain the frequency in an electrical grid. During peak loads this may be troublesome to achieve due to lack of adequate production capabilities. Competitive electricity markets with price caps have a problem - insufficient revenues for peak production units which lead to mothballing or decommissioning of power plants. Inadequate production capability is solved through design of the electricity markets which renders in incentives to operate these power plants. This report analyzes the most common market designs with the Nordic countries of Finland, Norway, and Sweden in mind. The Nordic situation is used as the background for an evaluation of the impact of the chosen designs. The question of finding a market solution of the peak load problem is yet a prerequisite. The conclusions are that a clear definition of what a market solution is, is needed in order to determine which design to prefer. The view on what to address as the problem makes a difference - treating the symptoms or the root causes leads to usage of different market designs as solutions. The Nordic countries may be better off by waiting to see the effects of the full penetration of the Automatic Meter Reading systems, which may reduce the peak loads by increasing the demand responsiveness. The current market designs in the Nordic countries may also be sufficient while developing the market's demand response, and thereby not call for implementations of other market designs.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Evidências indicam que ações de empresas de um mesmo setor da economia apresentam retornos semelhantes ao longo do tempo, uma vez que estariam expostas a variáveis econômico-financeiras e técnico-operacionais semelhantes. Gestores de recursos, de maneira geral, utilizam esta evidência em suas avaliações diárias na busca pelos melhores investimentos. Entretanto, na grande maioria dos casos, não há um embasamento teórico e matemático que comprove essa relação entre as ações. O objetivo dessa dissertação é verificar se, para um grupo de ações classificadas como mais relevantes dentre as presentes na Bolsa de Valores brasileira, os preços diários de fechamento que se comportam analogamente correspondem a empresas de um mesmo setor econômico. Para testar tal hipótese, serão avaliados diferentes métodos de clusterização aplicados a matriz de dissimilaridade entre os dados estudados, que por sua vez será determinada a partir de diferentes técnicas não-paramétricas de cálculo de dependência entre dados. Os métodos testados serão comparados e o melhor escolhido através da aplicação de índices de validação de clusterizações.
Evidence indicates that shares of companies belonging to the same economic sector have similar returns over time, since they would be exposed to similar economic-financial and technical-operational variables. Portfolio managers, in general, use this evidence in their daily valuations in order to find the best investment alternatives. However, in most cases, there isn`t a theoretical and mathematical background proving this relationship between stocks exists. The objective of this dissertation is to determine whether, for a group of stocks classified as among the most important of the Brazilian stock market, the daily closing prices that behave similarly correspond to companies in the same economic sector. To test this hypothesis, various clustering methods were evaluated and applied to the dissimilarity matrix calculated for the analyzed data, which is determined using different non-parametric techniques for calculating the dependency between data. The models were compared and the best selected by applying clustering validation index.

Office space oversupply cost Australia billions of dollars during the 1990-92 recession. Australia, the United States, Japan, the U.K., South Africa, China, Thailand, and many other countries have suffered office oversupply cycles. Illiquid untenanted office buildings impair investors capital and cash flows, with adverse effects on macroeconomics, financial institutions, and individuals. This study aims to develop improved methods for medium term forecasting of office market adjustments to inform individual project development decisions and thereby to mitigate office oversupply cycles. Methods combine qualitative research, econometric estimation, system dynamics simulation, and institutional economics. This research operationalises a problem solving research paradigm concept advocated by Ken Lusht. The research is also indebted to the late James Graaskamp, who was successful in linking industry and academic research through time and space specific feasibility studies to inform individual property development decisions. Qualitative research and literature provided a list of contributing causes of office oversupply including random shocks, faulty forecasting methods, fee driven deals, prisoners dilemma game, system dynamics (lags and adjustment times), land use regulation, and capital market issues. Rather than choosing among these, they are all considered to be causal to varying degrees. Moreover, there is synergy between combinations of these market imperfections. Office markets are complex evolving human designed systems (not time invariant) so each cycle has unique historical features. Data on Australian office markets were used to estimate office rent adjustment equations. Simulation models in spreadsheet and system dynamics software then integrate additional information with the statistical results to produce demand, supply, and rent forecasts. Results include ++
models for rent forecasting and models for analysis related to policy and system redesign. The dissertation ends with two chapters on institutional reforms whereby better information might find application to improve market efficiency.Keywords. Office rents, rent adjustment, office market modelling, forecasting, system dynamics.

Spatial autocorrelation is commonly found in the Hedonic Pricing model for real estate prices, but little attention has been paid to identify the causes behind. The primary objective of this research is to examine the causes of spatial autocorrelation in housing prices. Observed autocorrelation is often attributable to the omission of important location characteristics in the modelling process. Since it is practically impossible to exhaustively include all location characteristics, some variables may eventually be omitted, leaving spatially autocorrelated residuals in the Hedonic Pricing model. This thesis proposes a new source of spatial autocorrelation: real estate market liquidity. We hypothesize that liquidity affects the geographical boundary within which buyers and sellers search for price information. When the “immediate vicinity” of a property has few transactions, buyers and sellers may have to search for price information from more distant locations. Therefore, low liquidity in the vicinity of a property should strengthen the spatial autocorrelation of real estate prices. A Spatial - Liquidity Hedonic Pricing (SLHP) model is proposed to test the above hypothesis. The SLHP model generalizes traditional spatial autoregressive models by making the spatial process liquidity dependent. When applied to the apartment market in Hong Kong, the model is operationalized by defining “immediate vicinity” as the building where the subject unit locates. Furthermore, the SLHP model recognizes that past transactions may affect current transactions, but not vice versa, so the spatial weight matrix is simply lower triangular. Under this condition, we have shown that the Maximum Likelihood Estimation is equivalent to the Ordinary Least Squares Estimation. This greatly simplifies the estimation procedures and reduces the empirical analysis to a feasible scale. Based on 15 500 transactions of residential units in Taikooshing, Hong Kong from 1992 to 2006, we conclude that while positive spatial autocorrelation is present in housing prices, its magnitude decreases when liquidity, as measured by the past transaction volume in the immediate vicinity of a subject unit, is high. In addition, we found that current prices are spatially correlated with transactions occurred up to the last three months only, reflecting the relatively high information efficiency of Hong Kong’s residential market. All these results are generally robust across a variety of distance, liquidity, and time weight specifications. This study establishes liquidity as a determinant of spatial autocorrelation in real estate prices. This is a new finding contributing to the economic literature on liquidity effects and technical literature on spatial estimation. Our results not only reveal the spatially dependent price formation process in the real estate market, but also have practical applications on the hedonic modelling of real estate prices for mass valuation and index construction.
published_or_final_version
Real Estate and Construction
Doctoral
Doctor of Philosophy

The valuation of weather derivatives is complex since the underlying temperature process has no negotiable price. This thesis introduces a selection of models for the valuation of weather derivative contracts, governed by a stochastic underlying temperature process. We then present a new weather pricing model, which is used to determine the fair hedging price of a weather derivative under the assumptions of mean self-financing. This model is then extended to incorporate a compensation (or market price of risk) awarded to investors who hold undiversifiable risks. This results in the derivation of a non-linear two-dimensional PDE, for which the numerical evaluation cannot be performed using standard finite-difference techniques. The numerical techniques applied in this thesis are based on a broad range of lattice based schemes, including enhancements to finite-differences, quadrature methods and binomial trees. Furthermore simulations of temperature processes are undertaken that involves the development of Monte Carlo based methods.

ABSTRACT MAJID MEMARI, for the Masters of Science degree in Computer Science, presented on November 3rd, 2017 at Southern Illinois University, Carbondale, IL. Title: PREDICTING THE STOCK MARKET USING NEWS SENTIMENT ANALYSIS Major Professor: Dr. Norman Carver Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. GDELT is the largest, most comprehensive, and highest resolution open database ever created. It is a platform that monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day that stretches all the way back to January 1st, 1979, and updates daily [1]. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable [2]. On the other hand, other studies show that it is predictable. The stock market prediction has been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news [6]. The main objective of this research is to investigate the accuracy of predicting the unseen prices of the Dow Jones Industrial Average using information derived from GDELT database. Dow Jones Industrial Average (DJIA) is a stock market index, and one of several indices created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. This research is based on data sets of events from GDELT database and daily prices of the DJI from Yahoo Finance, all from March 2015 to October 2017. First, multiple different classification machine learning models are applied to the generated datasets and then also applied to multiple different Ensemble methods. In statistics and machine learning, Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Afterwards, performances are evaluated for each model using the optimized parameters. Finally, experimental results show that using Ensemble methods has a significant (positive) impact on improving the prediction accuracy. Keywords: Big Data, GDELT, Stock Market, Prediction, Dow Jones Index, Machine Learning, Ensemble Methods

Stochastische Anwendungen von großen Simulationsmodellen des Agrarsektors werden immer häufiger. Allerdings ist die stochastische Modellierung mit großen Marktmodellen rechenintensiv und mit hohen Kosten für Datenabspeicherung, -analyse und -manipulation verbunden. Gausssche Quadraturen sind effiziente Stichprobenmethoden, die wenige Punkte für die Approximation der zentralen Momente von gemeinsamen Wahrscheinlichkeitsverteilungen brauchen und somit die Kosten der Datenmanipulation senken. Für symmetrische Integrationsräume sind die Ecken des Oktaeder von Stroud (Stroud 1957) Formeln dritten Grades mit minimaler Anzahl von Punkten, die die stochastische Modellierung mit großen Modellen handhabbar machen kann. Es gibt trotzdem die Vermutung, dass Rotationen von Stroud''s Oktaeder einen Einfluss auf die Exaktheit der Quadraturen haben könnten; daher werden in dieser Studie acht unterschiedliche Rotationen (Quadraturformeln) getestet. Es zeigte sich, dass der Gebrauch der Formel von Artavia et al. (2009) oder der von Arndt (1996) bei der Generierung der Quadraturen entscheidend ist, und dass die Formel von Arndt einen höheren Exaktheitsgrad ergibt. Mit der Rotation, die sich aus der Formel von Arndt ergibt und Modellen oder Märkten mit starken Asymmetrien wie der Weizenmarkt in ESIM, könnten die Reihenfolge der stochastischen Variablen in der Kovarianz Matrix (A1 oder A2) oder die Methoden zur Einführung der Kovarianz Matrix (via Cholesky-Zerlegung –C– oder via die Diagonalisierungsmethode –D– ) einen bedeutsamen Einfluss auf die Exaktheit der Quadraturen haben. Mit Arndt''s Formel und weniger asymmetrischen Modellen oder Märkten, wie der Fall von Raps in ESIM, haben die Reihenfolgen A1 und A2 oder die Methoden zur Einführung der Kovarianz Matrix C und D weniger Einfluss auf die Exaktheit der Quadraturen.
Recently, stochastic applications of large-scale applied simulation models of agricultural markets have become more common. However, stochastic modeling with large market models incurs high computational and management costs for data storage, analysis and manipulation. Gaussian Quadratures (GQ) are efficient sampling methods requiring few points to approximate the central moments of the joint probability distribution of stochastic variables, and therefore reduce computational costs. For symmetric regions of integration, the vertices of Stroud''s n-octahedron (Stroud 1957) are formulas of degree 3 with minimal number of points, which can make the stochastic modeling with large economic models manageable. However, the conjecture exists that rotations of Stroud''s n-octahedron may have an effect on the accuracy of approximation of the model results. To address this, eight different rotations (quadrature formulas) were tested using the European Simulation Model (ESIM). It was found that using the formulas from Artavia et al. (2009) or Arndt (1996) in the generation of the quadratures is crucial, and furthermore, that the formula from Arndt yields higher accuracy. With the rotation obtained with Arndt''s formula and in models or markets with high asymmetries, as is the case for soft wheat in ESIM, the arrangement of the stochastic variables (A1 or A2) in the covariance matrix or the method selected to induce the covariance matrix (via Cholesky decomposition – C – or via the diagonalization method – D – ) may have a significant effect on the accuracy of the quadratures. With Arndt''s formula and with less asymmetric markets, as is the case for rapeseed in ESIM, the selection of arrangements A1 or A2 and of the method to induce the covariance C or D might not have a significant effect on the accuracy of the quadratures.

Modern financial engineering is a part of applied mathematics that studies market models. Each model is characterized by several parameters. Some of them are familiar to a wide audience, for example, the price of a risky security, or the risk free interest rate. Other parameters are less known, for example, the volatility of the security. This parameter determines the rate of change of security prices and is determined by several factors. For example, during the periods of stable economic growth the prices are changing slowly, and the volatility is small. During the crisis periods, the volatility significantly increases. Classical market models, in particular, the celebrated Nobel Prize awarded Black–Scholes–Merton model (1973), suppose that the volatility remains constant during the lifetime of a financial instrument. Nowadays, in most cases, this assumption cannot adequately describe reality. We consider a model where both the security price and the volatility are described by random functions of time, or stochastic processes. Moreover, the volatility process is modelled as a sum of two independent stochastic processes. Both of them are mean reverting in the sense that they randomly oscillate around their average values and never escape neither to very small nor to very big values. One is changing slowly and describes low frequency, for example, seasonal effects, another is changing fast and describes various high frequency effects. We formulate the model in the form of a system of a special kind of equations called stochastic differential equations. Our system includes three stochastic processes, four independent factors, and depends on two small parameters. We calculate the price of a particular financial instrument called European call option. This financial contract gives its holder the right (but not the obligation) to buy a predefined number of units of the risky security on a predefined date and pay a predefined price. To solve this problem, we use the classical result of Feynman (1948) and Kac (1949). The price of the instrument is the solution to another kind of problem called boundary value problem for a partial differential equation. The resulting equation cannot be solved analytically. Instead we represent the solution in the form of an expansion in the integer and half-integer powers of the two small parameters mentioned above. We calculate the coefficients of the expansion up to the second order, find their financial sense, perform numerical studies, and validate our results by comparing them to known verified models from the literature. The results of our investigation can be used by both financial institutions and individual investors for optimization of their incomes.

Dominant firms enjoy economic strengths which enable them to compete effectively in relevant markets through the use of collaborative knowledge management (CKM). While the literature is replete with general guiding principles for companies to adopt successful business strategies, there is very limited empirical research on effectively using CKM to improve company performance and market domination. The purpose of this study was to evaluate strategies for information sharing by companies to achieve better operations management and control, a wider range of customers, and stronger competitive edge in the global economy. Epistemological foundation for the study was provided by the literature on knowledge management and organizational dynamics. Data were collected by an electronically self-administered questionnaire on a convenience sample of 80 employees of three small businesses in Memphis, Tennessee. A quantitative method using Poisson regression was applied to test the hypotheses about relationships between six independent variables of value proposition, culture building, responsibilities, information technology, approaches and assessment and the dependent variable, collaborative knowledge management. Results indicate that value proposition, information technology, and building an organizational culture of responsibilities and best practices play significant roles in effective CKM. Social change implications of the study suggest that high-intensity collaborative knowledge management would produce creative leaders and workers, improved leader-worker collaboration, and more effective use of information technologies in organizational intelligence and decision making.

In Chapter 1 of the dissertation, the role of social networks is analyzed as an important determinant in the search behavior of the unemployed. Based on the hypothesis that the unemployed generate information on vacancies through their social network, search theory predicts that individuals with large social networks should experience an increased productivity of informal search, and reduce their search in formal channels. Due to the higher productivity of search, unemployed with a larger network are also expected to have a higher reservation wage than unemployed with a small network. The model-theoretic predictions are tested and confirmed empirically. It is found that the search behavior of unemployed is significantly affected by the presence of social contacts, with larger networks implying a stronger substitution away from formal search channels towards informal channels. The substitution is particularly pronounced for passive formal search methods, i.e., search methods that generate rather non-specific types of job offer information at low relative cost. We also find small but significant positive effects of an increase of the network size on the reservation wage. These results have important implications on the analysis of the job search monitoring or counseling measures that are usually targeted at formal search only. Chapter 2 of the dissertation addresses the labor market effects of vacancy information during the early stages of unemployment. The outcomes considered are the speed of exit from unemployment, the effects on the quality of employment and the short-and medium-term effects on active labor market program (ALMP) participation. It is found that vacancy information significantly increases the speed of entry into employment; at the same time the probability to participate in ALMP is significantly reduced. Whereas the long-term reduction in the ALMP arises in consequence of the earlier exit from unemployment, we also observe a short-run decrease for some labor market groups which suggest that caseworker use high and low intensity activation measures interchangeably which is clearly questionable from an efficiency point of view. For unemployed who find a job through vacancy information we observe a small negative effect on the weekly number of hours worked. In Chapter 3, the long-term effects of participation in ALMP are assessed for unemployed youth under 25 years of age. Complementary to the analysis in Chapter 2, the effects of participation in time- and cost-intensive measures of active labor market policies are examined. In particular we study the effects of job creation schemes, wage subsidies, short-and long-term training measures and measures to promote the participation in vocational training. The outcome variables of interest are the probability to be in regular employment, and participation in further education during the 60 months following program entry. The analysis shows that all programs, except job creation schemes have positive and long-term effects on the employment probability of youth. In the short-run only short-term training measures generate positive effects, as long-term training programs and wage subsidies exhibit significant locking-in'' effects. Measures to promote vocational training are found to increase the probability of attending education and training significantly, whereas all other programs have either no or a negative effect on training participation. Effect heterogeneity with respect to the pre-treatment level education shows that young people with higher pre-treatment educational levels benefit more from participation most programs. However, for longer-term wage subsidies we also find strong positive effects for young people with low initial education levels. The relative benefit of training measures is higher in West than in East Germany. In the evaluation studies of Chapters 2 and 3 semi-parametric balancing methods of Propensity Score Matching (PSM) and Inverse Probability Weighting (IPW) are used to eliminate the effects of counfounding factors that influence both the treatment participation as well as the outcome variable of interest, and to establish a causal relation between program participation and outcome differences. While PSM and IPW are intuitive and methodologically attractive as they do not require parametric assumptions, the practical implementation may become quite challenging due to their sensitivity to various data features. Given the importance of these methods in the evaluation literature, and the vast number of recent methodological contributions in this field, Chapter 4 aims to reduce the knowledge gap between the methodological and applied literature by summarizing new findings of the empirical and statistical literature and practical guidelines for future applied research. In contrast to previous publications this study does not only focus on the estimation of causal effects, but stresses that the balancing challenge can and should be discussed independent of question of causal identification of treatment effects on most empirical applications. Following a brief outline of the practical implementation steps required for PSM and IPW, these steps are presented in detail chronologically, outlining practical advice for each step. Subsequently, the topics of effect estimation, inference, sensitivity analysis and the combination with parametric estimation methods are discussed. Finally, new extensions of the methodology and avenues for future research are presented.
In Kapitel 1 der Dissertation wird die Rolle von sozialen Netzwerken als Determinante im Suchverhalten von Arbeitslosen analysiert. Basierend auf der Hypothese, dass Arbeitslose durch ihr soziales Netzwerk Informationen über Stellenangebote generieren, sollten Personen mit großen sozialen Netzwerken eine erhöhte Produktivität ihrer informellen Suche erfahren, und ihre Suche in formellen Kanälen reduzieren. Durch die höhere Produktivität der Suche sollte für diese Personen zudem der Reservationslohn steigen. Die modelltheoretischen Vorhersagen werden empirisch getestet, wobei die Netzwerkinformationen durch die Anzahl guter Freunde, sowie Kontakthäufigkeit zu früheren Kollegen approximiert wird. Die Ergebnisse zeigen, dass das Suchverhalten der Arbeitslosen durch das Vorhandensein sozialer Kontakte signifikant beeinflusst wird. Insbesondere sinkt mit der Netzwerkgröße formelle Arbeitssuche - die Substitution ist besonders ausgeprägt für passive formelle Suchmethoden, d.h. Informationsquellen die eher unspezifische Arten von Jobangeboten bei niedrigen relativen Kosten erzeugen. Im Einklang mit den Vorhersagen des theoretischen Modells finden sich auch deutlich positive Auswirkungen einer Erhöhung der Netzwerkgröße auf den Reservationslohn. Kapitel 2 befasst sich mit den Arbeitsmarkteffekten von Vermittlungsangeboten (VI) in der frühzeitigen Aktivierungsphase von Arbeitslosen. Die Nutzung von VI könnte dabei eine „doppelte Dividende“ versprechen. Zum einen reduziert die frühe Aktivierung die Dauer der Arbeitslosigkeit, und somit auch die Notwendigkeit späterer Teilnahme in Arbeitsmarktprogrammen (ALMP). Zum anderen ist die Aktivierung durch Information mit geringeren locking-in‘‘ Effekten verbunden als die Teilnahme in ALMP. Ziel der Analyse ist es, die Effekte von frühen VI auf die Eingliederungsgeschwindigkeit, sowie die Teilnahmewahrscheinlichkeit in ALMP zu messen. Zudem werden mögliche Effekte auf die Qualität der Beschäftigung untersucht. Die Ergebnisse zeigen, dass VI die Beschäftigungswahrscheinlichkeit signifikant erhöhen, und dass gleichzeitig die Wahrscheinlichkeit in ALMP teilzunehmen signifikant reduziert wird. Für die meisten betrachteten Subgruppen ergibt sich die langfristige Reduktion der ALMP Teilnahme als Konsequenz der schnelleren Eingliederung. Für einzelne Arbeitsmarktgruppen ergibt sich zudem eine frühe und temporare Reduktion, was darauf hinweist, dass Maßnahmen mit hohen und geringen „locking-in“ Effekten aus Sicht der Sachbearbeiter austauschbar sind, was aus Effizienzgesichtspunkten fragwürdig ist. Es wird ein geringer negativer Effekt auf die wöchentliche Stundenanzahl in der ersten abhängigen Beschäftigung nach Arbeitslosigkeit beobachtet. In Kapitel 3 werden die Langzeiteffekte von ALMP für arbeitslose Jugendliche unter 25 Jahren ermittelt. Die untersuchten ALMP sind ABM-Maßnahmen, Lohnsubventionen, kurz-und langfristige Maßnahmen der beruflichen Bildung sowie Maßnahmen zur Förderung der Teilnahme an Berufsausbildung. Ab Eintritt in die Maßnahme werden Teilnehmer und Nicht-Teilnehmer für einen Zeitraum von sechs Jahren beobachtet. Als Zielvariable wird die Wahrscheinlichkeit regulärer Beschäftigung, sowie die Teilnahme in Ausbildung untersucht. Die Ergebnisse zeigen, dass alle Programme, bis auf ABM, positive und langfristige Effekte auf die Beschäftigungswahrscheinlichkeit von Jugendlichen haben. Kurzfristig finden wir jedoch nur für kurze Trainingsmaßnahmen positive Effekte, da lange Trainingsmaßnahmen und Lohnzuschüsse mit signifikanten locking-in‘‘ Effekten verbunden sind. Maßnahmen zur Förderung der Berufsausbildung erhöhen die Wahrscheinlichkeit der Teilnahme an einer Ausbildung, während alle anderen Programme keinen oder einen negativen Effekt auf die Ausbildungsteilnahme haben. Jugendliche mit höherem Ausbildungsniveau profitieren stärker von der Programmteilnahme. Jedoch zeigen sich für längerfristige Lohnsubventionen ebenfalls starke positive Effekte für Jugendliche mit geringer Vorbildung. Der relative Nutzen von Trainingsmaßnahmen ist höher in West- als in Ostdeutschland. In den Evaluationsstudien der Kapitel 2 und 3 werden die semi-parametrischen Gewichtungsverfahren Propensity Score Matching (PSM) und Inverse Probability Weighting (IPW) verwendet, um den Einfluss verzerrender Faktoren, die sowohl die Maßnahmenteilnahme als auch die Zielvariablen beeinflussen zu beseitigen, und kausale Effekte der Programmteilahme zu ermitteln. Während PSM and IPW intuitiv und methodisch sehr attraktiv sind, stellt die Implementierung der Methoden in der Praxis jedoch oft eine große Herausforderung dar. Das Ziel von Kapitel 4 ist es daher, praktische Hinweise zur Implementierung dieser Methoden zu geben. Zu diesem Zweck werden neue Erkenntnisse der empirischen und statistischen Literatur zusammengefasst und praxisbezogene Richtlinien für die angewandte Forschung abgeleitet. Basierend auf einer theoretischen Motivation und einer Skizzierung der praktischen Implementierungsschritte von PSM und IPW werden diese Schritte chronologisch dargestellt, wobei auch auf praxisrelevante Erkenntnisse aus der methodischen Forschung eingegangen wird. Im Anschluss werden die Themen Effektschätzung, Inferenz, Sensitivitätsanalyse und die Kombination von IPW und PSM mit anderen statistischen Methoden diskutiert. Abschließend werden neue Erweiterungen der Methodik aufgeführt.

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
É princípio fundamental da Análise de Decisão que as previsões subjetivas utilizadas em uma análise devam ser baseadas na síntese de toda evidência disponível. Então, quando parte da evidência do tomador de decisão consiste de diversos modelos de previsão ou opiniões de especialistas, a Teoria da Decisão requer a formulação da combinação destes preditor. Este trabalho considera as metodologias Bayesianas Outperformance e Quase-Bayes, bem como o modelo clássico de Combinação Ótima , aplicação à combinação de previsões de preços médios de petróleo, geradas por especialistas da Petrobrás para diversos mercados internacionais. É apresentada uma descrição teórica das metodologias, seguida de uma análise comparativa entre os desempenhos das previsões individuais e das combinações, e estas entre si.
It is a fundamental principle of the Decision Analysis that the subjective forecasts used in na analysis should be based upon a synthesis of all the available evidence. Thus, when part of the decision-maker s evidence is in the form of a variety of forecasting models, or expert opinions. Decision Theory requires him to formulate a combination of these predictors. This work takes into account the Bayesian methodologies Outperformance and Quasi-Bayes, as well as the classical model of Optimal Combination, all applied to the combination of petroleum medium prices, generated by experts fo Petrobrás, for several international markets. It is presents a theoretical description of the methodologies followed by a comparative analysis between performances of individual forecasts and combinations, and these among themselves.

In this thesis, a conceptual definition of involuntary mobility is developed and used to estimate the incidence of such mobility between the years 2010 and 2012. This definition is then employed to understand how risk is structured in the housing market and how this impacts on the subjective sense of security derived from the home. The work of authors such as Beck (1992a; 1992b; 1994; 1999; 2010a; 2010b; 2011) and Giddens (1991), which considers risk as an inherent part of social structures, is drawn upon. This informs the adoption of the concept of precarity, which is utilised in order to understand the tenure arrangements in the private rented sector. Three empirical strands are drawn on. These are; the compilation of figures on individual reasons for residential mobility in order to build an incidence estimate for the years 2010–2012; the combination of two datasets, the British Household Panel Survey and Understanding Society, to assess the frequency of multiple involuntary moves; and the assessment through semi-structured interviewing of the impact that the ending by landlords of tenancies has on the subjective security tenants derive from their homes. It is found that involuntary mobility is widespread, and risk is seen to be an inherent part of the life course and embedded in the structures of the housing market. The concept of precarity, as it applies to the housing market, is reflected on. It is argued that precarity emerges at the intersection between legal and structural factors, here the tenancy agreement, the nature of private landlordism, and the structure of the private rented sector as a collection of small-scale landlords. Finally, it is found that the experience of such precariousness has a notable impact on the experience of private renting and the extent to which individuals feel ‘at home’ in the tenure.

Purpose: The purpose of this thesis is to identify the factors of adoption within alternative e-payments methods, and gain a deeper understanding of the reasons to why these factors affect the consumer’s adoption. This will be conducted through empirical research, as well as looking into previous theory in order to propose a research framework that is appropriately adapted to the specific phenomenon of alternative e-payments.   Problem: The substantial growth of e-commerce, and the limitations within conventional card payment has paved way for the development of new and innovative alternative e-payment methods. Past research point towards that, there are gaps in literature, where there is a lack of qualitative studies within the field. Furthermore, Sweden is one of the nations with the highest level of innovativeness, as well as e-commerce usage making it an appropriate market to analyze.   Method and Methodology: By applying an intepretevism paradigm, this research views knowledge from a subjective viewpoint. The primary data is collected through a qualitative approach, using one-to-one semi structured interviews, in order to obtain deeper a response. The gathered data is later on analyzed and compared with the previous research within the field.   Findings: The findings of this thesis identifies the factors of adoption within alternative e-payments and why they have an affect. Certain factors seemed to be of greater importance than others, and many of the factors held similarities showing their interdependent nature. Furthermore, new factors were identified as well as the factors being highly context specific, identifying gaps that should be addressed in future research.

This study aimed to answer the question whether time to market and competition pressure are important issues for new companies with a software products and whether they affect the development process and decision-making regarding releasing or the product. The study is based on the literature data and interviews with six technology startup companies. It investigated how the concept of minimum viable product is used by the companies for testing the product-market fit and how they apply principles of user-centred design for providing a good user experience of their products. I found that innovative products helped the companies escape direct competition. The development cycle was mostly defined by the industry standards and concrete customer needs rather than the competition pressure. User-centred design practices are widely implemented by the startups, but they are not always complete. In today's software market, the experience that the customers are provided with new products seems to be more important for the startups than formalisation of the development process and the product's time to market.
Den här studien sökte besvara frågan huruvida tid till marknadsintroduktion och tryck från konkurrenter är viktiga faktorer för nya uppstartbolag inom mjukvaruutveckling och huruvida dessa faktorer påverkar utvecklingsprocessen och beslut rörande produktlansering. Studien baserar sig på en litteraturgranskning och intervjuer med sex teknologiuppstartbolag. Studien undersökte hur konceptet av minsta bärkraftiga produkt används av uppstartbolagen för att testa hur bra produkten passar marknaden och hur bolagen använder principer från användarcentrerad design för att ge en god användarupplevelse i sina produkter. Jag fann att innovatina produkter hjälpte bolagen undvika direkt konkurrens. Utvecklingscykeln bestämdes framför allt av industristandarder och konkreta behov från kunderna i stället för av trycket från konkurrenter. Användarcentrerad designpraxis är vida erkänd hos uppstartbolagen, men tillämpningen är inte alltid komplett. I dagens mjukvarumarknad verkar kunders användarupplevelse i nya produkter vara viktigare för uppstartbolagen än att formalisera utvecklingsprocessen och produktens tid till marknadsintroduktion.

This thesis deals with application of optimisation methods based on linear and mixed-integer linear programming to various problems in the power sector related to electricity production. The thesis goal is to test the applicability of such methods to formulating and solving various instances from the class of real-world electricity production problems, and to find the advantages and disadvantages associated with using these methods. Introductory chapters describe the main characteristics of power markets, including the historical and regulatory context. Fundamental properties of power markets on both demand and supply side are also described, both from a real-world and a modelling point of view. Benefits of optimisation and modelling are discussed, in particular the solution feasibility and optimality as well as insights gained from sensitivity analysis which is often difficult to replicate with the original system. In the core of the thesis, optimisation techniques are applied to three case studies, each of which deals with a specific problem arising during electricity production. In the first problem, the profit of gas-fired power plant in Slovakia from selling power on the day-ahead market is maximised. The model is set up using both technical and commercial constraints. The second problem deals with the problem of representing a two-dimensional production function which primarily arises for a hydro generator with large variations in the level of its reservoir. Several representations of the original function using piecewise linear subsets are presented, compared, and characterised by their computational intensity both theoretically and practically. In the third problem, the prices on the German day-ahead market in 2011 are modelled. Contrary to the previous two models, the model does not capture an optimisation problem faced by a single producer, but incorporates a large subset of the whole market instead. Consequently the model is formed out of generic constraints relevant to all power plants whose parameters are estimated. By combining information about the aggregate availability of power plants with the estimated efficiencies a full supply curve for each day is created. Different scenarios are analysed to test the impact of uncertain inputs such as unknown or estimated constraints. The choice of the investigated problems stems from the attempt to cover electricity production problems from the point of view of multiple criteria. The three investigated electricity production problems span a broad range from the decisions of a single power plant to the modelling a power market as a whole. Formulations of the production function with different level of detail are presented ranging from a simple linear relationship to several bivariate function formulations. While each problem answers a specific question, they all illustrate the ease with which various electricity production problems can solved using optimisation methods based on linear and mixed-integer linear programming. This is mainly due to the ability of these methods to approximate even non-linear functions and constraints over non-convex domains and find global solutions in reasonable time. Moreover, models formulated with these methods allow sensitivity and scenario analyses to be carried out easily as is illustrated in each of the case studies.

This study investigates and evaluates how different portfolio optimization methods perform when varying assets and financial market scenarios. Methods included are mean variance, Conditional Value-at-Risk, utility based, risk factor based and Monte Carlo optimization. Market scenarios are represented by stagnating, bull and bear market data from the Bloomberg database. In order to perform robust optimizations resampling of the Bloomberg data has been done hundred times. The evaluation of the methods has been done with respect to selected ratios and two benchmark portfolios. Namely an equally weighted portfolio and an equally weighted risk contributions portfolio. The study found that mean variance and Conditional Value-at-Risk optimization performed best when using linear assets in all the investigated cases. Considering non-linear assets such as options an equally weighted portfolio performs best.
Den här studien undersöker och utvärderar hur olika portföljoptimeringsmetoder presterar med varierande finansiella tillgångsslag och marknadsscenarion. De metoder som har undersökts är: väntevärde-varians, villkorligt-värde-av-risk, nyttjande- och Monte Carlo baserad optimering. De marknadsscenarion som valts är: stagnerande, uppåt- samt nedåtgående scenarion där marknadsdata hämtats från Bloomberg för respektive tillgång. För att erhålla robusta optimeringsresultat har data omsamplats hundra gånger. Utvärderingen av metoderna har gjorts med avseende på utvalda indikatorer och två jämförelseportföljer, en likaviktad portfölj och en likariskviktad portfölj. Studien fann att portföljer genererade av väntevärde-varians och villkorligt-värde-av-risk optimering visade bäst prestanda, när linjära tillgångar använts i samtliga scenarion. När ickelinjära tillgångar såsom optioner har använts gav den likaviktade jämförelseportföljen bäst resultat i samtliga scenarion.

This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development and has shown much potential. Stochastic characteristics of stock market data are analyzed and found to be definitely not Gaussian, but instead better fit to a generalized t-distribution. Probability distribution models are validated with goodness-of-fit tests. For analysis, stock data is segmented into non-overlapping time “windows” to support unconditional statistical evaluation. Univariate and multivariate ARIMA and SSA time series models are evaluated for independence. ARIMA models are found to be independent, but SSA models are not able to reach independence. Statistics for out-of-sample forecasts are computed for every stock in every window, and multivariate-univariate confidence interval shrinkages are examined. Results are compared for univariate, bivariate, and trivariate combinations of highly-correlated stocks. Effects are found to be mixed. Bivariate modeling and forecasting with three different covariates are investigated. Examination of results with covariates of trading volume, principal component analysis (PCA), and volatility reveal that PCA exhibits the best overall forecasting accuracy in the entire field of investigated elements, including univariate models. Bivariate-PCA structures are applied in a back-testing environment to evaluate economic significance and robustness of the methods. Initial results of back-testing yielded similar results to those from earlier independent testing. Inconsistent performance across test intervals inspired the development of a second technique that yields improved results and positive economic significance. Robustness is validated through back-testing across multiple market trends.

Russia's startups fail at high rates. The purpose of this multiple case study was to understand the market-entry strategies used by accelerated startup managers to succeed in business longer than the first 3 years. The target population for this study was 3 startup owners who completed an acceleration program from the Internet Initiatives Development Fund and continued to operate businesses that generated revenue. The participants in the study were located in 3 different cities in Russia: Moscow, Saint Petersburg, and Tomsk. The conceptual framework for the study was Raheem and Akhuemonkhan's theory of enterprise development and von Bertalanffy's general system theory. Data collection involved semistructured interviews, review and analysis of company documents, reflective journal entries, and direct observation of the management operations and processes. Data were analyzed using Yin's 5-step data analysis process. A thematic analysis of the data revealed 4 themes: evolution of an entrepreneur, sales strategy, acceleration impact, and recommendations for accelerators and incubators. The results of the study may contribute to startup survivability as well as exchanging successful experience among new entrepreneurs. For those people who plan to start a business, this study may contribute understanding the skills for initiating a startup.