This paper examines the impact of the U.S. tax and financial reporting treatment of foreign earnings on the payouts to shareholders of U.S. multinational companies (MNCs). I find the U.S. tax and financial reporting treatment of foreign earnings weakens the otherwise strong, positive association between foreign earnings and the probability and level of dividend payments, but I do not observe an effect on the probability or level of stock repurchases or on the level of total payout. I also find U.S. MNCs with tax and/or financial reporting incentives to keep their foreign profits reinvested abroad make more extensive use of repurchases than dividends when making distributions to shareholders. This study contributes to our understanding of the impact of the current U.S. worldwide tax system on U.S. MNCs' real decisions.

The term consideration set is used in marketing to refer to the set of items a customer thought about purchasing before making a choice. While consideration sets are not directly observable, finding common ones is useful for market segmentation and choice prediction. We approach the problem of inducing common consideration sets as a clustering problem. Our algorithm combines ideas from binary clustering and itemset mining, and differs from other clustering methods by reflecting the inherent structure of subset clusters. Further, we introduce two speed-up methods to make the algorithm more efficient and scalable for large datasets. Experiments on both real and simulated datasets show that our algorithm clusters effectively and efficiently even for sparse datasets. A novel evaluation method is also developed to compare clusters found by our algorithm with known ones. Based on the clusters found by our algorithm, different classification models are built for each particular consideration set. The advantages of the two-stage model are it builds specific model for different clusters, and it helps us to capture the characteristics of each group of the data by analyzing each model.

Researchers and practitioners have recognized the need for business leaders to establish effective internal control frameworks. Some small and medium enterprises (SME) leaders lack strategies for improving internal control systems. The purpose of this multiple case study was to explore the strategies leaders of SMEs in Nigeria use for improving internal control practices. Building on the internal control theory and transactional leadership theory, semistructured face-to-face and phone interviews were conducted with 8 purposively-selected leaders of SMEs in Nigeria who successfully implemented internal control practices. The 5 themes that emerged from the thematic analysis of the interview data were: segregation of duty; adherence to processes, policies, and procedures; staffing, training, and experience; information technology; and staff empowerment and management commitment. The findings from this study indicate that leaders of SMEs in Nigeria use similar strategies to improve internal control practices. All participants used segregation of duty and adherence to processes, policies, and procedures as strategies for improving internal control practices. SME leaders should possess adequate leadership skills for improving internal control systems in their business. The result of this study may contribute to positive social change by providing SME leaders with knowledge on strategies for improving internal control practices which will minimize loss of assets and boost profitability and business sustainability. With increase in business profitability, leaders of SMEs will increase the firms' corporate social responsibility through payment of more taxes, and provision of employment opportunities and social amenities to the local community.

Small businesses in Zimbabwe make up 94% of the country's business population but only contribute 15% to the country's economy due to a high failure rate during the first 5 years. The purpose of this descriptive multiple case study was to explore strategies and skills that may contribute to the sustainability of small businesses during the first 5 years. The study population consisted of 5 small business owners in Ntepe village in Zimbabwe who had sustained their businesses for the first 5 years of operations. The conceptual framework that grounded this study was management theory. The data collection process involved conducting semistructured interviews with small business owners. Data analysis involved the adoption of methodological triangulation, thematic analysis, and member checking to ensure reliability and credibility of the data collected. The data collected presented two main themes: developing leadership skills and planning for positive performance. Key attributes and skills of a small business leader include trustworthiness, ability to lead resolutions that solve problems, effective communication of quality expectations, development of customer focus, and ability to address the needs of employees. Planning for positive performance includes goal setting, creating policies and procedures, and developing a control system for financial activities. This study may contribute to social change by providing data on proven strategies used by small business managers to sustain their businesses during the first 5 years of operations. The community may benefit from owners being better prepared to sustain their small businesses, given that these businesses may then hire employees and contribute to the local economy.

The market model is the most frequently estimated model in financial economics and has proven extremely useful in the estimation of systematic risk. In this era of rapid globalization of financial markets there has been a substantial increase in cross listings of stocks in foreign and regional capital markets. As many as a third to a half of the stocks in some major exchanges are foreign listed. The multiple listings of stocks has major implications for the estimation of systematic risk. The traditional method of estimating the market model by using data from only one market will lead to misleading estimates of beta. This study demonstrates that the estimator for systematic risk and the methodology itself changes when stocks are listed in multiple markets. General expressions are developed to obtain the estimator of global beta under a variety of assumptions about the error terms of the market models for different capital markets. The assumptions pertain both to the volatilities of the abnormal returns in each market, and to the relationship between the markets. Explicit expressions are derived for the estimation of global systematic risk beta when the returns are homoscedastic and also under different heteroscedastic conditions both within and/or between markets. These results for the estimation of global beta are further extended when return generating process follows an autoregressive scheme

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics; and, (Ph.D.)--Massachusetts Institute of Technology, Operations Research Center, 1999. / Includes bibliographical references (p. 144-151). / The unifying contribution of this thesis is to show that optimization is a very powerful tool that provides unexpected insights and impact on a variety of domains, such as probability, finance and revenue management. The thesis has two parts: In the first part, we use optimization models and techniques to derive optimal bounds for moment type problems in probability and finance. In the probability framework , we derive optimal inequalities for P(X E S), for a multivariate random variable X that has a given collection of moments, and an arbitrary set S. We provide a complete characterization of the problem of finding optimal bounds, from a complexity standpoint. We propose an efficient algorithm to compute tight bounds when S is a union of a polynomial number of convex sets, and up to second order moments of X are known. We show that it is NP-hard to obtain such bounds if the domain of X is Rn+, or if moments of third or higher order are given. Using convex optimization methods, we prove explicit tight bounds that generalize the classical Markov and Chebyshev inequalities, when the set S is convex. We examine implications to the law of large numbers, and the central limit theorem. In the finance framework, we investigate the applicability of such moment methods to obtain optimal bounds on financial quantities, when information about related instruments is available. We investigate the relation of option and stock prices just based on the no-arbitrage assumption, without assuming any model for the underlying price dynamics. We introduce convex optimization methods, duality and complexity theory to shed new light to this relation. We propose efficient algorithms for finding best possible bounds on option prices on multiple assets, based on the mean and variance of the underlying asset prices and their correlations and identify cases under which the derivation of such bounds is NP-hard. Conversely, given observable option prices, we provide best possible bounds on the moments of the underlying assets as well as prices of other options on the same asset. Our methods naturally extend for the case of transactions costs. The second part of this thesis applies dynamic and linear optimization methods to network revenue management applications. We investigate dynamic policies for allocating inventory to correlated, stochastic demand for multiple classes, in a network environment so as to maximize total expected revenues. We design a new efficient algorithm, based on approximate dynamic programming that provides structural insights into the optimal policy by using adaptive, non-additive bid-prices from a linear programming relaxation. Under mild restrictions on the demand process, our algorithm is asymptotically optimal as the number of periods in the time horizon increases, capacities being held fixed. In contrast, we prove that this is not true for additive bid-price mechanisms. We provide computational results that give insight into the performance of these algorithms, for several networks and demand scenarios. We extend these algorithms to handle cancellations and no-shows by incorporating overbooking decisions in the underlying mathematical programming formulation. / by Ioana Popescu. / Ph.D.

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 183-190). / Optimization under uncertainty forms the foundation for many of the fundamental problems the operations research community seeks to solve. In this thesis, we develop and analyze algorithms that incorporate ideas from machine learning to optimize uncertain objectives directly from data. In the first chapter, we consider problems in which the decision affects the observed outcome, such as in personalized medicine and pricing. We present a framework for using observational data to learn to optimize an uncertain objective over a continuous and multi-dimensional decision space. Our approach accounts for the uncertainty in predictions, and we provide theoretical results that show this adds value. In addition, we test our approach on a Warfarin dosing example, and it outperforms the leading alternative methods. / In the second chapter, we develop an approach for solving dynamic optimization problems with covariates that uses machine learning to approximate the unknown stochastic process of the uncertainty. We provide theoretical guarantees on the effectiveness of our method and validate the guarantees with computational experiments. In the third chapter, we introduce a distributionally robust approach for incorporating covariates in large-scale, data-driven dynamic optimization. We prove that it is asymptotically optimal and provide a tractable general-purpose approximation scheme that scales to problems with many temporal stages. Across examples in shipment planning, inventory management, and finance, our method achieves improvements of up to 15% over alternatives. In the final chapter, we apply the techniques developed in previous chapters to the problem of optimizing the operating room schedule at a major US hospital. / Our partner institution faces significant census variability throughout the week, which limits the amount of patients it can accept due to resource constraints at peak times. We introduce a data-driven approach for this problem that combines machine learning with mixed integer optimization and demonstrate that it can reliably reduce the maximal weekly census. / by Christopher George McCord. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 213-221). / The availability and prevalence of data have provided a substantial opportunity for decision makers to improve decisions and outcomes by effectively using this data. In this thesis, we propose approaches that start from data leading to high-quality decisions and predictions in various application areas. In the first chapter, we consider problems with observational data, and propose variants of machine learning (ML) algorithms that are trained by taking into account decision quality. The traditional approach to such a task has often focused on two-steps, separating the estimation task from the subsequent optimization task which uses these estimated models. Consequently, this approach can miss out on potential improvements in decision quality by considering these tasks jointly. Crucially, this leads to stronger prescriptive performance, particularly for smaller training set sizes, and improves the decision quality by 3 - 5% over other state-of-the-art methods. / We introduce the idea of uncertainty penalization to control the optimism of these methods which improves their performance, and propose finite-sample regret bounds. Through experiments on real and synthetic data sets, we demonstrate the value of this approach. In the second chapter, we consider observational data with decision-dependent uncertainty; in particular, we focus on problems with a finite number of possible decisions (treatments). We present our method of prescriptive trees, that prescribes the best treatment option by learning from observational data while simultaneously predicting counterfactuals. We demonstrate the effectiveness of such an approach using real data for the problem of personalized diabetes management. In the third chapter, we consider stochastic optimization problems when the sample average approximation approach is computationally expensive. / We introduce a novel measure, called the Prescriptive divergence which takes into account the decision quality of the scenarios, and consider scenario reduction in this context. We demonstrate the power of this optimization-based approach on various examples. In the fourth chapter, we present our work on a problem in predictive analytics where we focus on ML problems from a modern optimization perspective. For sparse shape-constrained regression problems, we propose modern optimization based algorithms that are scalable, and recover the true support with high accuracy and low false positive rates. / by Nishanth Mundru. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Individuals have an increasing number of work-related role-identities (RIDs). Identity theory (IDT) helps explain how the psychological importance (i.e., centrality) of one’s RIDs—arranged in a relatively stable, cognitive schema—and perceived social cues from one’s leader interact to shape role-related behaviors and work-related attitudes. Despite the theory’s emphasis on dyadic interaction, the extant literature has focused primarily on only one side of the interaction at a time, either from the leader’s perspective (e.g., getting followers to identify more strongly with a team RID) or more commonly from the follower’s perspective (e.g., how a high-centrality RID influences positive emotional states). Furthermore, the literature has ignored how dyadic interactions relating to one focal RID may influence an individual’s other RIDs contained within the same cognitive hierarchy. This study extends the original interactional aspects of IDT by investigating first the effects of perceived leader-follower centrality congruence on follower performance and attitudes. Next, it seeks to further our understanding of whether the effects of perceived RID-centrality congruence differ, depending on a given RID’s relative position in the centrality hierarchy: congruence effects should be greater for more-central RIDs. Finally, this study expands our understanding IDT by examining how the perceptions of the leader’s centrality on the follower’s most-central RID moderates the effects of perceived congruence on separate RIDs contained in the follower’s cognitive centrality hierarchy. Specifically, I propose that for the follower’s most-central RID, perceptions of high leader centrality of this RID will mitigate the negative relationships of incongruence on followers’ least-central work-RID. I collected data from a sample of 442 respondents who were online panel participants, and I tested my hypotheses and research questions utilizing moderated polynomial regression with response surface analysis. Results indicate that RID-centrality congruence is an important variable relating to performance and work attitudes. Furthermore, my results suggest that the positive effects of perceived centrality congruence are stronger when congruence occurs on one’s most-central RID compared to RIDs that are less central to followers. Researchers and practitioners should therefore consider not only the centrality of a particular type of work-RID (e.g., Team identity), but they should account for a RID’s centrality relative to the centrality of other concurrent work-RIDs. My results further suggest that verification of one RID can mitigate the effects of incongruence on other, concurrent work-RIDs lower in followers’ centrality hierarchies.

Over the next 20 years the labor shortage in the U.S. is expected to grow to 25 million with skilled labor being in especially high demand (Employment Policy Foundation, 2001). As such, the firm's ability to recruit human capital will increase in importance. Research suggest that person-organization fit is an important predicator of early stage recruiting outcomes such as organizational attraction (e.g., Kristof-Brown, Zimmerman, & Johnson, 2005) As such, this dissertation seeks to increase our understanding of the causes of overall PO fit perceptions in the context of realistic early recruiting outcomes. Organizational brand image, individual affectivity, and measures of PO fit on specific work attributes are hypothesized to be related to job seekers perceptions of overall fit perceptions.