Innovation and technological improvements are essential components in generating growth. While this topic is well studied, limited research exists on the effectiveness of innovation and how it drives firm value. The 2008 Financial Crisis serves as a major historical event that significantly changed the economic environment in the US. Centering my analysis around this event, my study empirically examines the efficiency of innovation investments and industry-level stock returns. By taking patents issued as a percentage of R&D outlays, I measure Efficient Innovation—how effective a firm’s R&D is in generating significant innovative change.

In 1992, the World Bank Group’s success rate - as evaluated the Bank’s unit, the Independent Evaluation Group - had substantially declined. In response, the Bank formed a task force to determine what factors had caused the decline. The Task Force report detailed several problems with the Bank’s project selection and implementation process. A review of the report and other literature concludes that projects often fail to achieve their goals because of overly optimistic ex-ante appraisals, and project delays. The project selection and design process should attempt to mitigate the risk of project delay by ensuring that financing is available on time, site conditions are stable, and the supply of materials is adequate. A regression analysis based on projects implemented in the 21st century investigates how project success has changed since the report, and how the Bank can continue to improve its project selection process. It concludes that the Bank’s projects are more successful when implemented in countries with a political environment conducive to businesses. In addition, projects experience more delays and are less successful when the borrowing country is responsible for funding a large percentage of the project.

Die Motivation für diese Dissertation ist dualer Natur: Einerseits ist die Dissertation methodologisch orientiert und entwickelt neue statistische Ansätze und Algorithmen für maschinelles Lernen. Gleichzeitig ist sie praktisch orientiert und fokussiert sich auf den konkreten Anwendungsfall von Produktretouren im Onlinehandel. Die “data explosion”, veursacht durch die Tatsache, dass die Kosten für das Speichern und Prozessieren großer Datenmengen signifikant gesunken sind (Bhimani and Willcocks, 2014), und die neuen Technologien, die daraus resultieren, stellen die größte Diskontinuität für die betriebliche Praxis und betriebswirtschaftliche Forschung seit Entwicklung des Internets dar (Agarwal and Dhar, 2014). Insbesondere die Business Intelligence (BI) wurde als wichtiges Forschungsthema für Praktiker und Akademiker im Bereich der Wirtschaftsinformatik (WI) identifiziert (Chen et al., 2012). Maschinelles Lernen wurde erfolgreich auf eine Reihe von BI-Problemen angewandt, wie zum Beispiel Absatzprognose (Choi et al., 2014; Sun et al., 2008), Prognose von Windstromerzeugung (Wan et al., 2014), Prognose des Krankheitsverlaufs von Patienten eines Krankenhauses (Liu et al., 2015), Identifikation von Betrug Abbasi et al., 2012) oder Recommender-Systeme (Sahoo et al., 2012). Allerdings gibt es nur wenig Forschung, die sich mit Fragestellungen um maschinelles Lernen mit spezifischen Bezug zu BI befasst: Obwohl existierende Algorithmen teilweise modifiziert werden, um sie auf ein bestimmtes Problem anzupassen (Abbasi et al., 2010; Sahoo et al., 2012), beschränkt sich die WI-Forschung im Allgemeinen darauf, existierende Algorithmen, die für andere Fragestellungen als BI entwickelt wurden, auf BI-Fragestellungen anzuwenden (Abbasi et al., 2010; Sahoo et al., 2012). Das erste wichtige Ziel dieser Dissertation besteht darin, einen Beitrag dazu zu leisten, diese Lücke zu schließen. Diese Dissertation fokussiert sich auf das wichtige BI-Problem von Produktretouren im Onlinehandel für eine Illustration und praktische Anwendung der vorgeschlagenen Konzepte. Viele Onlinehändler sind nicht profitabel (Rigby, 2014) und Produktretouren sind eine wichtige Ursache für dieses Problem (Grewal et al., 2004). Neben Kostenaspekten sind Produktretouren aus ökologischer Sicht problematisch. In der Logistikforschung ist es weitestgehend Konsens, dass die “letzte Meile” der Zulieferkette, nämlich dann wenn das Produkt an die Haustür des Kunden geliefert wird, am CO2-intensivsten ist (Browne et al., 2008; Halldórsson et al., 2010; Song et al., 2009). Werden Produkte retourniert, wird dieser energieintensive Schritt wiederholt, wodurch sich die Nachhaltigkeit und Umweltfreundlichkeit des Geschäftsmodells von Onlinehändlern relativ zum klassischen Vertrieb reduziert. Allerdings können Onlinehändler Produktretouren nicht einfach verbieten, da sie einen wichtigen Teil ihres Geschäftsmodells darstellen: So hat die Möglichkeit, Produkte zu retournieren positive Auswirkungen auf Kundenzufriedenheit (Cassill, 1998), Kaufverhalten (Wood, 2001), künftiges Kaufverhalten (Petersen and Kumar, 2009) und emotianale Reaktionen der Kunden (Suwelack et al., 2011). Ein vielversprechender Ansatz besteht darin, sich auf impulsives und kompulsives (LaRose, 2001) sowie betrügerisches Kaufverhalten zu fokussieren (Speights and Hilinski, 2005; Wachter et al., 2012). In gegenwärtigen akademschen Literatur zu dem Thema gibt es keine solchen Strategien. Die meisten Strategien unterscheiden nicht zwischen gewollten und ungewollten Retouren (Walsh et al., 2014). Das zweite Ziel dieser Dissertation besteht daher darin, die Basis für eine Strategie von Prognose und Intervention zu entwickeln, mit welcher Konsumverhalten mit hoher Retourenwahrscheinlichkeit im Vorfeld erkannt und rechtzeitig interveniert werden kann. In dieser Dissertation werden mehrere Prognosemodelle entwickelt, auf Basis welcher demonstriert wird, dass die Strategie, unter der Annahme moderat effektiver Interventionsstrategien, erhebliche Kosteneinsparungen mit sich bringt.

Este trabalho foi conduzido com o objetivo de comparar a formulação de ração por meio da programação linear (custo mínimo) e programação não linear (lucro máximo) para poedeiras comerciais. Utilizou-se 288 poedeiras da linhagem Hisex® White de 33 a 45 semanas de idade, com 1,540 ± 0,1167 kg de peso corporal. As aves foram distribuídas em delineamento em blocos ao acaso, com seis tratamentos com seis repetições de oito aves cada, totalizando 36 parcelas. Os tratamentos experimentais foram: 1) ração de custo mínimo com exigências nutricionais das Tabelas Brasileiras para Aves e Suínos; 2) ração de custo mínimo com as exigências nutricionais recomendadas pelo Manual da Linhagem; 3) ração de custo mínimo com exigências nutricionais obtidas de modelos matemáticos para otimização de desempenho; 4) ração de lucro máximo em cenário de mercado normal; 5) ração de lucro máximo em cenário de mercado favorável; e 6) ração de lucro máximo em cenário de mercado desfavorável. O desempenho foi avaliado por meio do consumo de ração, produção de ovos, peso dos ovos, massa de ovos e conversão alimentar. Na qualidade interna e externa, em quatro ovos por parcela, foram avaliadas o peso do ovo, gravidade específica, resistência à quebra da casca, peso da casca, espessura da casca, coloração da gema, altura de albúmen e unidade Haugh. Para avaliação econômica calculou-se o lucro. Os dados foram submetidos a análise de variância e em caso de significância (P<0,05) foi aplicado o teste de Scott-Knott (5%). Não houve diferença estatística (P>0,05) para a unidade Haugh, altura do albúmen e para as características de qualidade externa de ovos. Verificou-se efeito de tratamento (P<0,05) nas características de desempenho e qualidade de ovos por meio do valor absoluto e relativos de albúmen e gema. De forma geral, os tratamentos com as exigências obtidas pelos modelos matemáticos seguido das obtidas pelo manual da linhagem, ambos da programação linear, proporcionaram melhores resultados de desempenho, pois as rações foram nutricionalmente mais densas, no entanto, pioraram os resultados econômicos. Conclui-se que rações formuladas por meio de programação linear propiciaram as aves melhor desempenho, todavia, sem correspondente benefício econômico comparadas as rações formuladas por meio de programação não linear. / This study aimed to compares the feed formulation using linear (minimal cost) and non-linear (maximum profit) programming for commercial hens. Thus, 288 Hisex® white layer hens, from 33 to 45 weeks old, with 1,54 ± 0,12 kg of BWwere used. The hens were distributed in randomized blocks, with six treatments and six replicates with eight birds per replicate, totaling 36 plots. The treatments were: 1) minimum cost feed formulation, with nutritional requirements proposed by Rostagno et al. (2011); 2) minimum cost feed formulation, with nutritional requirements recommended by strain management guide; 3) minimum cost feed formulation, with nutritional requirements obtained from mathematical models for performance optimization; 4) maximum profit feed formulation in normal market scenario; 5) maximum profit feed formulation in favorable market scenario; and 6) maximum profit feed formulation in an unfavorable market scenario. The performance was evaluated from feed intake, egg production, egg weight, egg mass and feed conversion ratio. In four eggs per pen was evaluated the internal and external egg quality: egg weight, specific gravity, shell resistance, shell weight, shell thickness, yolk color, albumen height and Haugh unit. To economic evaluation, profit was calculated. Data were submitted to variance analyses and when significant (P<0.05) Scott-Knott test (5%) was applied. There was no statistical difference (P>0.05) for Haugh unit, albumen height and external quality of the eggs. There was effect of treatment (P<0.05) on performance and eggs quality from absolute and relative values of yolk and albumen. In general, treatments with requirements obtained by mathematical models or by strain management guide, both from linear programming, improved the performance results, since diets were denser nutritionally, however, worsened the economic results. In summary, feed formulated by linear programming improves performance, however, worsens economic results compared with nonlinear programming.

Thesis advisor: Zhijie Xiao / In recent years, quantile regression has achieved increasing prominence as a quantitative method of choice in applied econometric research. The methodology focuses on how the quantile of the dependent variable is influenced by the regressors, thus providing the researcher with much information about variations in the relationship between the covariates. In this dissertation, I consider two quantile regression models where the information set may contain quantiles of the regressors. Such frameworks thus capture the dependence between quantiles - the quantile of the dependent variable and the quantile of the regressors - which I call models of quantile dependence. These models are very useful from the applied researcher's perspective as they are able to further uncover complex dependence behavior and can be easily implemented using statistical packages meant for standard quantile regressions. The first chapter considers an application of the quantile dependence model in empirical finance. One of the most important parameter of interest in risk management is the correlation coefficient between stock returns. Knowing how correlation behaves is especially important in bear markets as correlations become unstable and increase quickly so that the benefits of diversification are diminished especially when they are needed most. In this chapter, I argue that it remains a challenge to estimate variations in correlations. In the literature, either a regime-switching model is used, which can only estimate correlation in a finite number of states, or a model based on extreme-value theory is used, which can only estimate correlation between the tails of the returns series. Interpreting the quantile of the stock return as having information about the state of the financial market, this chapter proposes to model the correlation between quantiles of stock returns. For instance, the correlation between the 10th percentiles of stock returns, say the U.S. and the U.K. returns, reflects correlation when the U.S. and U.K. are in the bearish state. One can also model the correlation between the 60th percentile of one series and the 40th percentile of another, which is not possible using existing tools in the literature. For this purpose, I propose a nonlinear quantile regression where the regressor is a conditional quantile itself, so that the left-hand-side variable is a quantile of one stock return and the regressor is a quantile of the other return. The conditional quantile regressor is an unknown object, hence feasible estimation entails replacing it with the fitted counterpart, which then gives rise to problems in inference. In particular, inference in the presence of generated quantile regressors will be invalid when conventional standard errors are used. However, validity is restored when a correction term is introduced into the regression model. In the empirical section, I investigate the dependence between the quantile of U.S. MSCI returns and the quantile of MSCI returns to eight other countries including Canada and major equity markets in Europe and Asia. Using regression models based on the Gaussian and Student-t copula, I construct correlation surfaces that reflect how the correlations between quantiles of these market returns behave. Generally, the correlations tend to rise gradually when the markets are increasingly bearish, as reflected by the fact that the returns are jointly declining. In addition, correlations tend to rise when markets are increasingly bullish, although the magnitude is smaller than the increase associated with bear markets. The second chapter considers an application of the quantile dependence model in empirical macroeconomics examining the money-output relationship. One area in this line of research focuses on the asymmetric effects of monetary policy on output growth. In particular, letting the negative residuals estimated from a money equation represent contractionary monetary policy shocks and the positive residuals represent expansionary shocks, it has been widely established that output growth declines more following a contractionary shock than it increases following an expansionary shock of the same magnitude. However, correctly identifying episodes of contraction and expansion in this manner presupposes that the true monetary innovation has a zero population mean, which is not verifiable. Therefore, I propose interpreting the quantiles of the monetary shocks as having information about the monetary policy stance. For instance, the 10th percentile shock reflects a restrictive stance relative to the 90th percentile shock, and the ranking of shocks is preserved regardless of shifts in the shock's distribution. This idea motivates modeling output growth as a function of the quantiles of monetary shocks. In addition, I consider modeling the quantile of output growth, which will enable policymakers to ascertain whether certain monetary policy objectives, as indexed by quantiles of monetary shocks, will be more effective in particular economic states, as indexed by quantiles of output growth. Therefore, this calls for a unified framework that models the relationship between the quantile of output growth and the quantile of monetary shocks. This framework employs a power series method to estimate quantile dependence. Monte Carlo experiments demonstrate that regressions based on cubic or quartic expansions are able to estimate the quantile dependence relationships well with reasonable bias properties and root-mean-squared errors. Hence, using the cubic and quartic regression models with M1 or M2 money supply growth as monetary instruments, I show that the right tail of the output growth distribution is generally more sensitive to M1 money supply shocks, while both tails of output growth distribution are more sensitive than the center is to M2 money supply shocks, implying that monetary policy is more effective in periods of very low and very high growth rates. In addition, when non-neutral, the influence of monetary policy on output growth is stronger when it is restrictive than expansive, which is consistent with previous findings on the asymmetric effects of monetary policy on output. / Thesis (PhD) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

My dissertation consists of two essays on mutual funds. The first essay examines the role of extreme positive returns on future fund flows using maximum style-adjusted daily returns (hereafter MAX) over the previous month. My results suggest that there is a positive and significant relation between MAX and future fund flows. The results are robust to controls for fund performance, fund size, age, turnover, fund fees, volatility, and skewness of fund returns. Of particular interest, this relation exits only in retail funds. Moreover, MAX is persistent from one month to the next, but MAX-based investment strategies are associated with lower risk-adjusted returns than investors could have achieved in otherwise similar funds. Overall, my analysis suggests that mutual fund investors are attracted to maximum style-adjusted daily returns, which is in line with the theoretical argument that investors exhibit a preference for lottery-like payoffs. These investors are successful in achieving a lottery-like return profile, but this strategy is costly in terms of expected returns The second essay studies the effect of recent and long-term mutual fund performance on future fund flows. I document that investors' response to recent performance depends on average long-term performance. In particular, a recent loser fund experiences outflows only if its longer-term performance is also poor. Similarly, recent good performance leads to more inflows only if the fund has also good long-run performance. In contrast, investors ignore recent performance if it provides a signal that conflicts with the longer-term signal. This implies that good fund managers with a longer-term focus will find it easier to attract future inflows than managers with a short-term horizon.

"The information ratio is a very controversial topic in the business world. Some portfolio managers put a lot of weight behind this risk-analysis measurement while others believe that this financial statistic can be easily manipulated and thus shouldn't be trusted. In this paper, an attempt will be made to show both sides of this issue by defining the information ratio, applying this definition to real world situations, explaining some of the negative impacts on the information ratio, comparing this ratio to other statistical measures, and showing some ways to improve a portfolio manager's information ratio. "

How can a company decrease their return rates? And can be this conducted by integrating more with the customer via social media? These two main research questions are the core of this Master-Thesis and are related on the mail-order company BON’A PARTE. The focus on the target markets is Denmark, Germany, and Sweden.Fitting problems, different expectations of the order, and inadequate price-performance ratio are the most return reasons for BON’A PARTE customers. In the fashion industry it is very important to satisfy the customer, especially meeting their demands. Due to the straightforwardness of the Internet it is difficult for mail-order companies to build customer loyalty since Internet users can change via one click to the competitors.In order to reach the study purpose, research question related to e-commerce, returns management, and consumer interaction via social media were focused on.The used methodology during the work was literature, a survey, and a case study. For the theory part literature was used and the survey gave an important overview of the return reasons within the company. By ordering garments from the company a qualitative analysis could be developed which reflected the customers’ expectations.By minimizing the gaps between the customers and the company, which involves keeping the company’s promise the return rate can be decrease and BON’A PARTE can build up a personal relationship to their customers. Social media, like the networks Facebook and Twitter are good possibilities to reach new customers and keep their loyal ones. Through interacting with blogs BON’A PARTE can communicate in a better way with their consumers. / Program: Magisterutbildning i Applied Textile Management

This thesis describes three different experiments investigating dishonesty. Chapter one investigates the use of default values and prompts in a tax filing system. Pre-populated fields simplify the process of filing taxes, thereby reducing the scope for errors. Such defaults may increase the scope for non-compliance if set incorrectly. The chapter describes an experiment investigating the effect of correct and incorrect defaults. The results show that setting defaults that underestimate taxpayers’ true liability produces a fall in compliance. Nudges designed to mitigate the adverse effect of pre-population are also described. Nudges using descriptive norms in a dynamic manner that react to taxpayer decisions raise compliance. The chapter concludes that the use of defaults is worthwhile only if the data is of sufficient quality. Chapter two describes a model for lying aversion containing cost elements in terms of the size of the lie told and in the positive deviation above a reference point reflecting the point at which someone becomes concerned about the credibility of the value being reported or about appearing boastful. An experiment based on a numeracy test where subjects have the ability to cheat by paying themselves for their performance is used to test the model. Two treatments are detailed using modal values from initial control sessions to set different reference points. The results show a greater propensity among subjects to report false values under the higher reference point consistent with the model. Chapter three details an experimental investigation into lying behaviour between two samples, one a sample of undergraduate student subjects the other of workers recruited through Amazon Mechanical Turk. Results from a senderreceiver game based on a lottery draw show a higher propensity to report partially false values among student subjects, consistent with a higher reputational concern on behalf of the workers compared to students.

This paper examines the lockup expiration date event for technology firms post Global Financial Crisis to investigate the existence of abnormal returns around this date and determine the explanatory power that insider trading and the increase in available shares have on the abnormal return. Contributions to literature include using an updated sampling, targeting the technology industry, and constructing unique variables such as the dollar value of insider trades around the lockup expiration date. There exists statistically significant three-day cumulative abnormal returns of -1.33%. Firms with higher percentages of insiders who sell their positions tend to experience a further decrease in cumulative abnormal returns (CAR). The supply effect of these shares being opened to the market is not significant at the 95% confidence level. Thus, insider trading rather than increased supply accounts for variations in the abnormal returns across technology firms.