Browsing by Issue Date, starting with "2021-02-01"
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- Stock market prediction via machine learning and investor sentiment data : a quantitative investment strategyPublication . Beeger, Dominik Lukas; Tran, DanThis study shows sentiment data’s ability to predict stock prices by applying modern machine learning models and investment strategies on data from different industry sectors. Sentiment-based investment strategies outperform benchmarks in return and risk measurements. The findings are consistent with previous academic research that already proved sentiment’s forecasting ability on major stock markets with other statistical methods. The author finds that sentiment-based strategies work best for negative performing industry sectors and high volatile sectors. In general, high volatile time periods enable sentiment-based models to predict stock returns more accurately; in low volatile periods, sentiment strategies underperform. Additionally, data based on single companies supposedly provide clearer signals than index-based features; thus, it can be concluded that input data lose significance through indexing. Based on these results, the author created an investment strategy that can be used for further research and professional investment strategies The author also finds out that high accuracy scores of applied machine learning models must not be followed by high financial performances. This finding can be explained by the complex distribution of returns. In our case, the majority is concentrated on returns with small magnitudes, which increases the importance of days with extremely positive and negative returns. These returns determine the overall performance more than returns with small magnitudes. It can be concluded that in finance data relations are more complex, which is why it is more important to adapt models to the data structure instead of maximizing machine learning performance.
- Consumers response to different certifications in social enterprises : the mediating role of brand credibilityPublication . Paiva, Catarina Santos; Bicho, Marta Liliana NunesThe objective of this dissertation is to understand the impact of certification type (awarded certification - symbolic certification) on consumer response, namely perceived trustworthiness and perceived expertise, taking into consideration the mediating role of brand credibility. An online survey containing a between-group factorial experiment design was used to test our hypothesis on a convenience sample of Portuguese consumers using a high-involvement product. Data was analysed through independent samples t-tests and the SPSS PROCESS macro to test for mediation. The findings displayed that, mediated by brand credibility, consumer response was higher for the awarded certification scenario. This study contributes to theory as it expands scientific knowledge in the field of social entrepreneurship along with consumer behaviour and marketing theories. While introducing a refresh view on certifications and their differences, proving that consumers perceive certifications types differently. Title: Consumers response to different certifications in social enterprises – the mediating role of brand credibility
- Becoming a purpose-driven organization : why and how to transitionPublication . Pouilleau, Victoria Charlotte Adelaide; Cruz, Rui Nuno Tavares de Almeida Moreira daGiven the emergency of actual social and environmental problematics, can companies still think of their legitimacy independently from their contribution to the world? Then, which responsibility should be given to shareholders? This is from this radical questioning, putting at stake the very fundamentals of our economic model, that the concept of purpose-driven companies was born. But installing a purpose within a company requires not only a modification in communication or status, but in the global functioning of a company. This is where difficulties could start to threaten actions: differences of visions, lack of engagement from the shareholders, difficulties to convince, to adapt its economic model… This paper and its problematic came from a will to give companies, that wish to transition to become purpose-driven, some motivations and guidelines. Our quantitative study, by collecting and analyzing the opinions of late millennials and early generation Z participants, representing the future of consumers and employees, we hope to make companies realize all the pertinence there is to choose to transition to become purpose driven in order to become more perennial, and coherent with how society is evolving.
- Relação entre ajustamento ao envelhecimento, satisfação com a vida, variáveis sociodemográficas, presença de doença física e percepção subjetiva de saúde em adultos mais velhosPublication . Gandarela, Joana Margarida da Silva; Maia, Berta Maria Marinho Rodrigues
- Enjoy chocolate without any bitter aftertaste : creating a fairer value chain in the chocolate industry by shifting chocolate manufacturing to Ghana : the case of FairafricPublication . Von Podewils, Sophie Isabell Charlotte Freiin; Bicho, Marta Liliana NunesThis study examines the social enterprise Fairafric which generates social impact in the chocolate industry by shifting chocolate production to Africa. The two central questions of the present analysis are how the entrepreneurial structure of Fairafric contributes to Fairafric’s success regarding social impact in the chocolate industry and commercial success on the chocolate market and how Fairafric acquires competitive advantages on the chocolate market. At first, a literature review on social enterprises and entrepreneurial structures, an overview of the global chocolate industry’s value chain, and a presentation of Fairafric are provided as factual basis. The first part of the analysis focuses on how Fairafrics entrepreneurial structure contributes to Fairafric’s social and commercial success. For that purpose the social entrepreneurship process framework of Lumpkin et al. (2013) is applied. The second part of the analysis focuses on how the business strategy of Fairafric enables Fairafric to acquire competitive advantages on the chocolate retail market. For this purpose, the differentiation strategy analysis framework of Michael Porter is applied to Fairafric’s business strategy. The results of the analysis show that Fairafric has a strong entrepreneurial orientation that positively affects Fairafric’s social impact and business performance. Furthermore, the analysis shows that Fairafric has acquired significant competitive advantages because of a strong market differentiation of the brand, brand purpose, the products and the communication. On a general level, the thesis shows how entrepreneurial tools are suitable and effective in order to achieve social impact.
- The impact of mission drift in social enterprise´s legitimacy : a consumer perspectivePublication . Mendes, Pedro Henrique Pires; Bicho, Marta Liliana NunesThis dissertation strives to explain the consumers perceptions over the legitimacy of a social enterprise when facing mission drift. Mission drift takes place when organizations deviate from the social mission to pursue commercial activities in order to enhance financial objectives. Thus, the aim of this paper is to understand the consumers perceptions of the legitimacy of social enterprises in the presence of mission drift, how it affects these organizations, and explain what social enterprise’s leaders might do to prevent the risk of mission drift. For this purpose, a qualitative research was conducted based on semi-structured, face-to-face interviews to consumers that identify themselves with social enterprises. The research concluded that mission drift will damage two specific types of legitimacy, pragmatic and moral legitimacy, since for consumers it will be impeditive for the organization to create social value and to act in accordance with the general norms in society. Additionally, contrarily to the existing literature, some findings suggest the need of mission drift for organizations to be able to support the long-term sustainability of their social mission. However, research on this topic is still lacking. Furthermore, some consumers expect a lack of support from stakeholders due to legitimacy issues caused by mission drift being this one of the strategies suggested to overcome mission drift: to integrate and give more voice to consumers in the decision-making process. Therefore, this dissertation has made valuable contributions to the field of social enterprises, mission drift and legitimacy.
- Market timing : the forecasting ability of machine learning algorithmsPublication . Dölle, Eric Marcel; Tran, DanThe present thesis analyzes whether shifts in the direction of the stock market can be predicted by supervised learning algorithms and aims to contribute to the emerging literature on the use of machine learning in finance. Previous findings on the feasibility of market timing strategies vary. Many proposed strategies fail to predict market movements consistently enough to beat the returns achieved by buy-and-hold investors. While most market timing strategies are based on standard econometric models, research on the market timing ability of machine learning techniques remains in its infancy. In this thesis, logistic regressions, artificial neural networks, and support vector machines were used to predict whether the excess return of the S&P 500 is positive or negative over time horizons of one week, two weeks, and one month. Based on the predictions, market timing strategies that allocate capital between stocks and a risk-free asset were developed and subsequently backtested over a 23-year period. The results indicate that the proposed strategies outperform various buy-and-hold portfolios, even after transaction costs are deducted. Forecasts over the two-week and one-month horizons were significantly more accurate than predictions over the one-week horizon. A timing strategy based on a hybrid model that combines different algorithms outperformed all other models in terms of risk-adjusted returns. The findings suggest that, contrary to the prevailing view, shifts in the stock market are in fact predictable.
- Corporate bankruptcy : can machine learning methods enhance the prediction of failure?Publication . Ferreira, Eva Maria Ribeiro Silva; Tran, DanThis dissertation aims to enhance the performance of traditional corporate bankruptcy prediction models through the application of machine learning techniques and models, and industry effects. The data used includes 3664 companies out of which 144 went bankrupt throughout the period of 2000 until 2019, and it was structured to emulate the design of the variables Campbell et al. (2008) used in their study. Evidence was found that implies the improvement of various metrics’ results from the use of machine learning techniques and models. The model with the highest F1-score, meaning the most balanced, is the Logit with the application of hyperparameter tuning and industry effects. The model with the highest Recall, which means the percentage of bankruptcies correctly predicted, is the Logit with the application of the oversampling technique. Furthermore, both Support Vector Machines (SVM) and Artificial Neural Networks (NN) models delivered balanced and enhanced results compared with the two benchmarks (Altman Z-Score and Simple Logit models). The improvement techniques provided the models with distinct results. Oversampling led mostly to a higher percentage of bankruptcies predicted, while hyperparameter tuning and industry effects provided the models with more precise results. The variable importance in each type of model was also analysed. Overall, the Campbell et al. (2008) market variables (SIGMA, RSIZE, EXRET and PRICE) are highly significant for the positive results of all three types of models studied.
- Can we detect and predict fraud with machine learning?Publication . Fatela, Catarina Teixeira Santos; Tran, DanThis project aims to construct a machine learning model that is able to detect and predict financial fraud. Based on 3,095 U.S. fraudulent firms, 61 features and 24 years of analysis, this neural network will be able to detect and predict a large part of frauds. Following research and analysis, financial fraud was concluded to have a huge impact, not only for the companies in terms of costs and penalties, but also for investors and for the market as a whole. Many of the fraudulent companies disappear after the discovery of fraud and are unable to recover. The consequences this reflects on the employees, investors and the market are incalculable. Firms undergoing an initial public offering tend to have more incentives to commit fraud, since they are at a critical stage of their life cycle and want to attract as many investors as possible. This project will address these companies separately as well due to their interesting characteristics and evident incentives to commit fraud. This model can be used by investors, by banks in their credit risk models, by venture capitalists and last but not least, institutions like the Securities and Exchange Committee (SEC) that regulate the market.
- From space to mental space: a cognitive perspective into narrative and the architecture of the human mindPublication . Abrantes, Ana MargaridaSpatial metaphors are pervasive across models and theories about the structure and the meaning-making processes of the human mind: metaphorical and metonymic mappings (Lakoff and Johnson 1980, Barcelona 2012), mental spaces (Fauconnier 1994, 1997) or semantic domains (Fauconnier and Sweetser 1996, Brandt 2004) are examples of this spatial ubiquity in cognitive science. In narratology, categories of location and place are often correlated with narrative spaces as expression of a dynamics of unfolding of events, from initial situation to catastrophe to its consequence and result (Brandt 2009). Narrative as such is viewed as a compelling way of worldmaking (Nünning 2010, Goodman 1975), inviting further metaphors in the description of the reading experience, such as ‘being transported’ by means of ‘mentally performing’ narrated actions and experiences (Gerrig 2003).