Intrinsic Universal Measurements of Non-linear Embeddings

A basic problem in machine learning is to find a mapping $f$ from a low dimensional latent space to a high dimensional observation space. Equipped with the representation power of non-linearity, a learner can easily find a mapping which perfectly fits all the observations. However such a mapping is often not considered as good as it is not simple enough and over-fits. How to define simplicity? This paper tries to make such a formal definition of the amount of information imposed by a non-linear mapping. This definition is based on information geometry and is independent of observations, nor specific parametrizations. We prove these basic properties and discuss relationships with parametric and non-parametric embeddings.Comment: work in progres

Cloud Adoption Factors in a Specific Business Area: Challenging the Findings of Organisation-Wide Cloud Computing Research

Existing literature investigates cloud adoption factors and their impact on the decision to adopt cloud services in organizations. These studies consider the decision to adopt cloud services as a horizontal organization-wide decision. In this paper we argue that most of cloud decisions in practice do not regard cloud adoption horizontally across the organization. Rather, they consider cloud adoption with respect to the particular business area in which the cloud service will be introduced. These are the types of decisions we investigate in this paper. Drawing on the cloud adoption literature and Diffusion of Innovation and Organizational Capability theories, we formulate our research model involving factors related to cloud’s relative advantage and to organizational innovativeness. Our findings show that cloud’s cost-reduction and remote access benefits tradeoff security concerns as the context of cloud adoption becomes specific and demonstrate the relevance of personnel innovativeness in cloud adoption decisions

A case study exploration of patient safety culture within an Acute NHS Trust, utilising Open Systems Theory

Background: The prevention of errors and adverse effects from healthcare in hospitals is a global priority. The beliefs, values, and norms of an organisation can support patient safety and influence staff behaviours. Aim: To understand perceptions of, and influences on, patient safety culture within an Acute NHS Trust in England. Method: A case study of one acute NHS hospital Trust with embedded units of analysis (two medical wards). Semi-structured interviews were conducted with 16 staff at different levels of the Trust. Documentary analysis included patient safety metrics and organisational safety documents. Theoretical framework: Open Systems Theory. Findings: There were differing perceptions at the different levels about acceptable levels of risk and the compromises needed to manage pressures. There was a lack of opportunities for interaction and dialogue to establish common values around patient safety. Micro level staff perceived that a balance had to be struck between maintaining quality of care and reporting patient safety. There was little internal or externally facing examination and interrogation of safety metrics that would convey a commitment to a positive patient safety culture. Conclusions: A more nuanced understanding of how a system contributes to patient safety has emerged and some of the factors that act as enablers of, or barriers to, a positive patient safety culture. Staff at all levels believed that patient safety was important but patient safety culture was more about measurement of events and avoidance of specific measurable harms than a clearly articulated set of values about safety. Recommendations: Organisations should regularly evaluate the effectiveness of patient safety feedback loops so clinical staff voices, including healthcare assistants, become part of meso/macro level decision-making regarding how safe patient throughput can be managed. Healthcare organisations should recognise the role that shift co-ordinators play in keeping patients safe at ward level by providing training for junior nurses to step into this role. Safety training at all levels is necessary to create a shared dialogue about risk, safety, reporting and learning so organisations should embrace the safety syllabus and training for NHS staff that was introduced in May 2021 and ensure staff have protected time for this training

A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

Portfolio performance evaluation of institutional investors: An empirical investigation of selection ability via the levels of institutional ownership, risks, firm size, and R&D expenditures

The purpose of this study is threefold: (1) To evaluate how the portfolio performance of institutional investors differs from the market index portfolio, first, as a whole, and second, as several different institutional ownership portfolios. (2) To investigate the relationship between the institutions\u27 superior stock selection ability and firm quality attributes such as beta, volatility, firm size, and R&D expenditures. Most previous academic work has focused on institutional investment behaviors, finding the relationship between institutional ownership and firm quality attributes, based on only mutual funds. (3) To develop a decision model for future institutional investors\u27 portfolio performance based on the explanatory variables used in this study. The dependent variable is portfolio gross return, and firm quality attributes are independent. The study group is selected from firms listed on the Compact Disclosure database during the period Jan. 1989-Dec. 1996. Approximately 8,000 NYSE, AMEX, and NASDAQ companies are employed in this study. As analytical tools, Sharpe\u27s measure (1966), Jensen\u27s alpha measure (1968), and Jobson and Korkie\u27s Z-statistic (1981) are used. From the results of the study, one may conclude that institutional investors as a whole are not superior stock selectors, however, specific institutional ownership portfolios performed in a superior manner. The institutions\u27 superior selection ability is partly related to such firm quality attributes as small firm and stock volatility effects. Previous studies find that institutional ownership is related to firm size; however, institution\u27s portfolio performances are found to be inversely related to size. Higher beta is not found to contribute to institutions\u27 superior portfolio performance. This study found that institutional investors act in a hyperopic manner when tested with R&D expenditures. However, amounts of a firm\u27s R&D expenditures are inversely related to institutions\u27 superior performance. Unexpectedly, stock volatility is found to contribute to institutions\u27 portfolio excess returns, based on Jensen\u27s measure. Finally, all firm quality attributes employed in this study as explanatory variables appear to be significantly related to portfolio returns. All variables are positively related to portfolio gross returns except R&D, which is inversely related

Developing a Machine Learning based Systematic Investment Startegy: A case study for the Construction Industry

In this research work, an end-to-end systematic investment strategy based on machine learning models and leveraging the construction industry operational and management practices knowledge, is implemented. First, a literature research in the field of behavioral finance is done, presenting the current state of the knowledge and trends in the industry. A suitable investment opportunity exploiting prevailing market inefficiencies around earnings announcements is identified. Second, an extensive literature research is performed identifying the most relevant characteristics of construction companies’ operations and major risk factors they are exposed to. These insights are used to engineer a set of relevant variables. Third, advanced statistical techniques are used to select the most relevant subset of features, which includes market and analysts’ expectation data, macroeconomic indicators, the delay in reporting earnings, and the most important financial dimensions for construction firms. Fourth, the earnings’ surprise classification problem is characterized by a class imbalance and asymmetric misclassification costs. These issues are a consequence of the desired business application, and are addressed by selecting an appropriate evaluation metric. Additionally, considerations on the temporal dimension and generative process of the data are made to select an appropriate validation scheme. Five different state-of-the-art machine learning algorithms are considered: a multinomial logistic regression, a bagging classifier, a random forest, an XGBoost and a linear Support Vector Machine. The multinomial logistic regression is found to be the most suitable model, exhibiting a bias towards predicting positive earnings’ surprises over the rest of classes. The firm size, and the profitability and valuation measures, portrayed by the Return on Assets and Enterprise Value multiples, are found to be the most important variables when predicting earnings surprises. To conclude, the systematic investment strategy based on the investment signals produced by the selected machine learning model is back-tested, being the performance of the long-short portfolio driven by the positive surprise one as a consequence of the selected model bias. Keywords: Quantitative Investing, Machine Learning, Behavioral Financ

Strategy formation and subsidiary performance : the moderating effect of cultural differences

Dans la littérature sur la stratégie, il est reconnu qu' il y a deux tendances de base, qui souvent se chevauchent, à la racine de l'élaboration des stratégies: des stratégies planifiées et des stratégies émergeantes, et que toutes les stratégies peuvent se retrouver sur cette échelle, selon leur combinaison des dimensions suivantes: l'intention des dirigeants, le contrôle central et l'environnement (Mintzberg & Waters, 1985). La complémentarité de ces deux méthodes d'élaboration de stratégies a été démontrée par Anderson (2004), qui a empiriquement testé leurs effets positifs sur la performance organisationnelle, dans deux conditions environnementales : l'internationalisation et la turbulence. Il est également reconnu qu'une des sources les plus importantes des empêchements au succès des firmes multinationales et leurs filiales est la différence culturelle (Miroshnik, 2002). La raison pour expliquer ceci est qu'une augmentation de l'incertitude causée par la distance culturelle influence sur le choix du mode d'élaboration des stratégies (Peng, 2002). Cette explication est le focus de cette dissertation, dont le point de départ est de mieux comprendre l'influence de l'environnement culturel sur le processus d'élaboration des stratégies. Cette étude examine l'influence des différences culturelles entre les filiales et leurs sociétés mères (aux niveaux national et organisationnel) dans la relation entre le processus d'élaboration de stratégies (processus planifié et émergeant) et la performance organisationnelle. L'échantillon de population dans le cadre de cette recherche inclut des filiales étrangères de multinationales anglaises, françaises, allemandes, japonaises et états-uniennes ayant des activités sur le sol canadien. Les données de cette dissertation incluent des données cueiIlies de sources qualitatives, telles des entrevues personnelles auprès des PDG et vice-présidents qui ont le plus étroit contact avec le siège social. De plus, une enquête fut menée auprès des hauts dirigeants impliqués dans l'élaboration des stratégies de la filiale, et qui sont fréquemment en contact avec la société mère, afin de comprendre la perception des différences organisationnelles des cultures, de l'élaboration des stratégies et de la performance des données. Quatre mesures établies fournissent les principales variables indépendantes utilisées pour l'analyse statistique. Elles incluent les données secondaires sur la distance de la culture nationale basées sur les indices de pays de Hofstede (2001) appliqués dans l'équation de Kogut et Singh (1988) pour la distance de la \ud culture nationale. Les trois autres variables indépendantes et la variable dépendante utilisent les données primaires cueillies par voie d'enquête: la mesure de la distance culturelle de l'organisation selon l'échelle de Hofstede (1990), les mesures découlant des processus de stratégies planifiées et émergentes selon l'échelle de Anderson (2004), et une échelle qui mesure la performance subjective de la filiale. Les résultats ont été analysés en utilisant une analyse régressive hiérarchique afin de tester l'effet modérateur. Aussi, les variables ont été analysées utilisant l'index de corrélation et de régression linéaire de Spearman. Les résultats fournis appuient l'hypothèse qui propose la relation entre la distance culturelle organisationnelle et la performance de la filiale; la recherche a aussi trouvé que seule la distance organisationnelle culturelle a un effet significatif sur la performance, alors que l'influence de la distance de la culture nationale sur la performance n'est pas significative. Les statistiques exploratoires font une percée par rapport à l'influence modératrice de la distance cuIturelle organisationnelle sur la relation entre la stratégie décentralisée émergeante et la performance de la filiale, pour les grandes filiales. Cependant, aucun appui n'a été recensé pour la notion que la distance culturelle nationale influence la relation entre l'élaboration de la stratégie et la performance. D'autres recherches impliquent l'étude d'un autre facteur qui peut influencer l'élaboration de la stratégie et la performance de la filiale, à l'intérieur de la firme, tel le degré de contrôle exercé par les sociétés mères sur la filiale. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Stratégie, Formation des stratégies, Stratégie émergeant, Stratégie planifié, Distance culturelle organisationnelle, Distance culturelle nationale, Performance, Théorie institutionnelle, Multinational, Filiale