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The objective of this paper is to determine whether the supply of sustainable investments meets the demand in terms of product quantity and investor preference. Indeed, this kind of investment keeps growing increasingly. In the first part of this master thesis, we start by giving a definition and identifying the origins of the concept of Socially Responsible Investment (SRI). Then, we made an analysis of the entire market, including the evolution of the offer, the market distribution and the motivations and obstacles to SRI. Afterwards, we talked about some of the points that we thought were important in the European Union's action plan. Indeed, some actions will have a greater impact on SRI supply and demand than others. After that, we proceeded to a definition of the different sustainable strategies. And we ended this part by discussing financial performance and the supply and demand for SRI. In the second part of this paper, we conducted two surveys in order to answer the question raised above. The first survey concerned the demand for SRI and was therefore conducted among investors residing in the Grand Duchy of Luxembourg. Secondly, different from Luxembourg such as banks and asset management companies responded to our second survey on SRI offerings. Thanks to this, we were able to analyze the SRI market in Luxembourg and compare our results with the SRI market from EU that we described in our first part. Finally, after writing this thesis, we can conclude that there is sufficient supply in terms of quantity but that investor preferences are not sufficiently assessed by financial advisors. Indeed, very few investors are aware of the concept of sustainable investment, but this will change thanks to the EU action plan. One of the aims of the Action Plan will be to oblige advisors to integrate sustainable aspects into the investor profile. Financial advisors will therefore be obliged to include sustainability in discussions with their clients.
SRI --- Sustainable Investment --- ESG criteria --- EU Action Plan --- Sustainable Finance --- Socially Responsible Investment --- Sciences économiques & de gestion > Finance
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Under the never-ending changes occurring in financial landscape which now go beyond financial performance incorporating other considerations associated with environmental, social, and technological factors into the decision-making process. Thus, the present research thesis is positioned at the center of the above combining both the technology innovation and eco-friendly finance. The objective of this master thesis is to explore in-depth the potential role of financial technology in contribution to a more sustainable financial ecosystem in the European Union. Due to the complexity of the subject, we adopted a qualitative study with an exploratory goal. Primary data is collected through semi-structured surveys circulated to experts in the field. The sample consists of seven interviewees who operate in different sectors comprising banks, Fin-Tech companies, and consulting firms. These valuable insights are analyzed and discussed using an identified set of themes to fully cover the topic, and to contribute to theory to implement the inductive approach we have chosen. Our key findings extracted from our study confirm the link binding financial technology with sustainability in Europe showcasing that, in addition to the positive outcomes, technology have the potential to advance eco-friendly practices in the future if treated correctly in term of challenges associated with the transformative force of financial technology, with obviously is possible the help of employees’ engagement and collaborative force. We have been able to identify relevant future studies judged to be essential to clear the fog on the subject.
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Le rapport suivant a été écrit dans le cadre de mon stage dans l'entreprise « Behave! ». Son principal objectif est d’identifier et de défendre le modèle de Machine Learning le plus pertinent dans la cadre de prévisions portant sur 7 styles d’investissement différents : « Growth », « Momentum », « Quality », « Size », « Value », « Volatility » et « Yield ». Étant donné que ce mémoire est rédigé selon une orientation "rapport d’entreprise", une part importante de ce document est consacrée à la construction de modèles et à l’analyse de résultats. De nombreuses recherches académiques ont néanmoins dû être effectuées et viendront, aussi souvent que possible, appuyer les conclusions établies au fur et à mesure des chapitres. Ma tâche au sein de l’entreprise peut être divisée en trois étapes majeures, il en va de même pour la construction de ce rapport. Premièrement, les facteurs de risque sont définis et systématiquement liés à leurs styles d’investissement. C’est l’occasion d’étudier les techniques utilisées par l’entreprise pour les calculer. Dans un deuxième temps, ce sont les modèles de Machine Learning qui sont définis et appliqués à un exemple simple en utilisant les logiciels « RStudio » et « Microsoft Azure Cortana Intelligence ». Dans ce mémoire, l’approche se limite aux modèles suivants : « Hidden Markov », « Random Forest », « Support Vector Machine » et « Neural Network ». Il s’agira enfin d’appliquer ces modèles aux styles d’investissement proposés par l’entreprise afin de pouvoir faire des comparaisons qui serviront ensuite de base à mes recommandations finales.
Machine Learning, factor investing, growth, momentum, quality, size, --- value, volatility, hidden markov, support vector machine, neural --- network, random forest, artificial intelligence, confusion matrix, --- performance, accuracy, investment, ESG criteria --- Sciences économiques & de gestion > Finance --- Ingénierie, informatique & technologie > Sciences informatiques
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