In this paper we examine abnormal returns during active repurchasing programs and if the intensity of repurchasing programs impacts the returns. Through the Jensen’s Alpha approach our findings show us that positive abnormal returns are experienced by repurchasing firms under our study period that ranges from 2010 to 2019. The results show us that during active repurchasing programs companies have showed positive average annual abnormal returns ranging from 1,8% to 6%. We also find that the intensity of share repurchases does not have a statistically significant effect on the given abnormal returns. However, our results indicate that the abnormal returns are higher when the repurchases occurred, rather than when they are authorized.

A vast literature has investigated the returns to investment in agriculture research and generally found extremely high rates of return. These results suggest policymakers would do well to maintain or increase resource allocation to public agricultural research. Remarkably little attention has been paid, however, to the issue of how best to allocate public agricultural research funding between competing research areas and organizations. This paper considers the relative returns to alternative uses of public agricultural research funds committed to the agricultural experiment stations of 10 western states of the United States over the years 1967-91. A model of expected utility maximization subject to risk is presented with comparative analysis. After establishing empirically that the mean variance analysis would be an inappropriate method to solving the problem, a stochastic dominance testing method is employed to identify dominated and undominated research categories and state agricultural experiment stations. The mean variance analysis also is used to evaluate whether research productivity has been increasing or decreasing over time, and to establish which among the western states hold absolute advantage in particular research areas.

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This thesis studies three important topics in modeling financial volatility. First, the jump clustering in ex post variance and its implications on forecasting, second, the underlying distribution of stochastic volatility and third, the role of non-Gaussian multivariate return distribution combined with a realized GARCH framework. The first chapter is on variance jumps. Financial markets present unexpected and large jumps, due to unobserved news flow. I focus on modeling the ex post variance jumps, their time- dependent arrivals and their sizes. I use a discrete-time bivariate model, with two autoregressive components which capture the long and short-run memory of the ex post variance measures. I estimate contemporaneous and time-dependent jumps in the log-measures of realized variance and bipower variation. The results from S&P500 show that the variance jumps are frequent and persistent. I examine the ability of jumps to forecast returns and ex post variance densities over horizons of up to 50 days out-of-sample. Modeling jumps significantly improves ex post variance density forecasts for all horizons and improves forecasts of the returns density. In the second chapter I explore the empirical non-Gaussian features of stochastic volatility. The standard assumption in a stochastic volatility specification is typically a restrictive Gaussian AR(1) structure. I drop this assumption and instead I assume that latent log-volatility follows an infinite mixture of normals with a Dirichlet process prior. The ex post measure of realized variance is used as a source of information to help identify the unknown distribution of log- volatility. Results from major stock indices show strong evidence of non-Gaussian distributional behaviour of volatility. The proposed framework captures asymmetry and thick tails in returns as well as realized variance. In out-of-sample forecasting, the new model provides improved density forecasts for returns, negative returns and log-realized variance. In the third chapter a new approach for multivariate realized GARCH models is proposed. Two new extensions that have non-Gaussian innovations are developed. The first one is a parametric version, with multivariate-t innovations. The second one is a nonparametric approximation of the return distribution using an infinite mixture of multivariate normals given a Dirichlet process prior. The proposed models are based on the assumption that the realized covariance follows an Inverse Wishart distribution with conditional mean set to the conditional covariance of returns. The benefits of the proposed models are demonstrated from density forecasting and portfolio applications. Results from two equity datasets indicate that modeling the tail behaviour improves return density forecasting compared to the Gaussian assumption. The proposed models produce the least volatile global minimum variance portfolios out-of-sample and provide improved forecasts of Value-at-Risk and Expected Shortfall. / Thesis / Doctor of Business Administration (DBA)

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This paper has four chapters. The first chapter serves as an introduction. The second chapter studies the transaction fees in the bitcoin system. The transaction fees and transaction volume in the bitcoin system increase whenever the network is congested and results from a simple VAR show that it is indeed the case. To account for the empirical findings, we build a model where users and miners together determine the transaction fee and transaction volume endogenously. Even though the fluctuating transaction fee mechanism in bitcoin introduces the extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fix transaction fee rate. The second chapter studies the time-varying price dispersion among different bitcoin exchanges. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility. The results show that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion. The third chapter studies the relationship between connections and returns in the bitcoin investor network. Using transaction data from the bitcoin blockchain, we reach three conclusions. First, on average, the annualized returns of connected addresses in the network are 20.75% above those of their unconnected peers. Second, returns also differ among those connected addresses. By dividing the connected ad- dresses into ten deciles based on their centrality, we find that addresses in the two most-connected deciles earn higher returns than the other connected addresses. Third, eigenvector centrality is more related than degree centrality to higher returns, implying that quality of connections matters. / Doctor of Philosophy / This paper has four chapters. The first chapter serves as an introduction. The second chapter studies the transaction fees in the bitcoin system. The transaction fees in the bitcoin system can fluctuate given the amount of unconfirmed transactions in the bitcoin network. Our results show that the transaction fees and transaction volume in the bitcoin system increase whenever the network is congested. To account for this findings, we build a model and show that users and miners together can determine the transaction fee and transaction volume. Even though the fluctuating transaction fee mechanism in bitcoin introduces the extra cost of uncertainty to users, a back-of- envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fix transaction fee rate. The second chapter studies the price dispersion among different bitcoin exchanges. Our results show that transaction fees and bitcoin price growth can be important explanatory factors for the price dispersion among different bitcoin exchanges. The third chapter studies the relationship between connections and returns in the bitcoin investor network. Using transaction data from the bitcoin blockchain, we reach three conclusions. First, on average, those connected addresses in the network earn higher returns than their unconnected peers. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten groups based on their centrality, we find that addresses in the two most-connected groups earn higher returns than the other connected addresses. Third, eigenvector centrality, which measures the quality of connections, is more related than degree centrality, which measures the quantity of connections, to higher returns, implying that quality of connections matters.

Yes / The concept of waste and how it can be reduced, recycled, refurbished or reused in its current form has been widely discussed in industry. The importance of waste reduction from an environmental and economic perspective has also heightened in both industry and within the research arena. Thus said, stringent steps have been taken to facilitate the collection of and capture residual value in waste items. This article explores this premise in relation to medicines waste as part of the wider medicines optimisation agenda.

The last decades have seen a rapid increase of sustainable investing, also known as ESG (Environmental, Social and Governance) investing. There has also been an increasing body of academic literature devoted to whether investors can gain any financial benefits from taking ESG under consideration. Previous literature of portfolio performance in terms of risk-adjusted returns has given much of its attention to best-in-class approaches, which is a strategy that selects top performers in ESG within a sector or industry. The purpose of this study is foremost to investigate a best-effort approach to ESG investing, which is a strategy that focuses on the top improvers in ESG. The purpose is further to compare this with a best-in-class approach, since the findings from earlier studies of this strategy still are inconsistent. The region chosen to perform this study in is the Eurozone. Several theories that have implications for portfolio studies and abnormal returns are taken under consideration in relation to the study and its findings. This includes the efficient market hypothesis, the adaptive market hypothesis and modern portfolio theory. The theoretical framework also cover asset-pricing models and the notions of risk-adjusted returns. A quantitative study with a deductive approach are used to form portfolios, with a Eurozone index as the investable universe. Best-effort and best-in-class portfolios as well as difference portfolios of the two approaches are created, based on ESG data and different cut-off rates for portfolio inclusion. As for risk-adjusted performance measure, the Carhart four-factor model are used. The overall results are mostly insignificant findings in terms of abnormal returns. However, three best-effort portfolios based on the top ESG improvers show significant positive abnormal returns. These findings are strongest for the environmental and social factor. As for the best-in-class approach, only the governance portfolios provided weakly significant results in terms of abnormal returns. Further, the study is not able to significantly distinguish between a best-effort and a best-in-class approach when it comes to risk-adjusted performance. The exception is the environmental factor based on the top performers in each approach, where the best-effort portfolio outperforms the best-in-class portfolio. Finally, none of the portfolios provided significant negative risk-adjusted returns. This can at least be considered as good news for ESG investing, since it indicates that investors do not have to sacrifice risk-adjusted returns in order to invest in a more sustainable way.

One big issue for the mail order business is how to avoid and manage returns. Oneapproach being taken is that consumer insight can result in better customer satisfaction andfewer returns. The fashion industry delivers more than just a function within the clothes;fashion, excitement, and customer service for example.It is believed that a part of demand chain management, consumer insight, can help toreduce the amount of returns. This thesis approaches this issue from a company point ofview. It is investigated how mail order businesses utilizes opportunities given byeCommerce, how the companies perceives their customers, and how well they are able totailor their services to different consumer groups.Online sales channels were reviewed in order to investigate how the company is perceivedand what kinds of efforts the companies go through in order to add value to the customers.These results were combined with interviews of three companies in the mail order businessand one company selling clothes in retail stores.The complexity of the customer and the silo mentality in many companies was the firsthurdle to emerge. It was hard to get in contact with the right person. The interviewsshowed both focus on products and focus of really understanding the customer. Thisshowed both in the rigid layout of the homepages and the lack of understanding thatcustomers might have different needs when it comes to value adding services. However,the interviewed companies had varying ways of defining their customer’s needs.It is evident after this thesis that a lot of work can be done in order to better understand thecustomers, for example investigating causes to returns and how differentiated servicesmight improve how the customers perceive the service. In order to succeed in a holisticapproach, cooperation between mail order companies might be required.

A large body of literature has explored that investor sentiment and customer satisfaction have been viewed as important metrics to evaluate asset prices, little attention has been paid to whether investor sentiment influence the impact of customer satisfaction on stock returns. This paper examines whether customer-based portfolios can create abnormal returns by employing different risk-adjusted models in different sentiment states. This study finds that portfolio composed of firms with better customer satisfaction can continuously beat the benchmark index and create abnormal returns when adopting different risk-adjusted models. Furthermore, portfolio with better customer satisfaction can significantly outperform the market even in pessimistic and neutral investor sentiment state. This is because of higher CS firms can create stable business cash flows, alleviate customer complaints and strengthen customer loyalty, which will create insurance-like protection for firms in pessimistic investor state, which results in significant abnormal returns even in pessimistic investor state. These results suggest that customer-based metrics are valuable information that should be included in financial models.