Veritati
Institutional Repository of the Universidade Católica Portuguesa
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Global client satisfaction based on primary data TDGI
Publication . Corvo, João Daniel Dias; Xavier, Rute
This paper aims to help characterize TDGI global customer satisfaction, supported by primary data. The research was intended to address the limitations and biases that exist in the procedures that the company followed. It will help create new methodologies to measure and analyse customer satisfaction through primary data. The research took a qualitative approach to analysing primary data sourced from interviews and surveys. A thematic analysis was used to help understand what the major factors impacting customer satisfaction were and how they were rated. From the acquired data it was found that overall, the clients were satisfied with the services they were provided. Important themes, such as the clients’ relationships with the TDGI representatives and the team’s technical expertise, formed the basis for the high satisfaction level. Other factors which were pointed out as in need of some improvements included the firm’s proactivity level and its ability to control subcontracted external service providers. The findings from this paper imply that TDGI is doing well in satisfying its customers. The firm should continue striving to maintain the high quality of its services, and it can do so by recognizing the themes that were the most praised and taking measures to continue this way. On the other hand, the firm should also work on addressing the improvement points that were shared during the making of this paper. This process should be repeated occasionally to allow a constant monitoring of satisfaction.
Brand licensing in luxury : from fashion house to lifestyle brand : the Giorgio Armani case
Publication . Gomes, Natalie de Lima; Parada, Pedro
This master’s thesis explores how luxury fashion brands can strategically leverage brand licensing as a form of brand extension to create a multi-dimensional lifestyle brand. Written in a teaching case format, the dissertation focuses on Giorgio Armani, a luxury company that has evolved from a traditional fashion house into a unified luxury lifestyle brand. To understand how Armani has managed this transition, the research draws on secondary papers as well as expert interviews. By analysing Armani’s tiered brand hierarchy, the thesis demonstrates how the fashion house uses both upward and downward brand extensions to reach diverse consumer segments. Furthermore, the dissertation outlines the advantages and disadvantages of licensing in a luxury context and addresses challenges such as managing multiple licenses, ensuring quality control and maintaining a consistent brand image without diluting it. The findings demonstrate that Armani successfully navigates these complexities through value-based partner selection, strong creative oversight and a clearly defined brand architecture. Ultimately, this thesis contributes to the field of luxury brand management by offering valuable insights into how licensing can function as a strategic tool for long-term brand growth and product diversification.
Navigating the Milei effect : market implications of Argentina’s electoral shift
Publication . Jorge, João Francisco Costa; Stahl, Jörg
This dissertation explores the impact of the 2023 Argentina Presidential Election on stock market dynamics, focusing on sector-specific reactions in Argentina and potential spillover effects across the main Latin and North American stock markets. Using an event study methodology, the research examines abnormal returns surrounding the election date, to identify significant changes in investor sentiment following the surprising victory. Findings reveal that Utilities, Industrials, Consumer Non-cyclicals, Real Estate and Consumer Cyclicals emerged as “relative winners”, while Basic Materials were “relative losers”. No evidence of regional spillover effects was found, suggesting the impact of the election was contained within Argentina. Nevertheless, the results show there are sector-specific reactions, either positive or negative, due to the perception of future pro-market policies and austerity expectations.
Forecasting next-day market prices of stocks and bitcoin using social media sentiment analysis
Publication . Janesch, Daniel Malvin; Fernandes, Pedro Afonso
This thesis investigates whether sentiment scores derived from social media can improve the prediction of next-day market prices for financial assets, with a focus on Tesla ($TSLA) and Bitcoin ($BTC). Using freely available X (Twitter) datasets from Kaggle, three pre-trained Natural Language Processing (NLP) tools—VADER, TextBlob, and FinTwitBERT—were appplied to individually score the sentiment of each post. After extensive cleaning, filtering, and sampling, a total of 47,800 tweets were used for analysis—33,200 for $TSLA and 14,600 for $BTC. Sentiment scores were aggregated into daily averages and merged with financial data from Bloomberg. These features were then used as explanatory variables in three forecasting models: ARIMA/ARIMAX, XGBoost, and Long Short-Term Memory (LSTM) neural networks. The results show that for $TSLA, the introduction of sentiment scores—especially with FinTwitBERT—substantially improved forecasting performance. The strongest model, an LSTM with sentiment and financial variables, achieved a test R² of 0.76 compared to a baseline model with R² of 0.49. In contrast, all models performed poorly on $BTC, likely due to its higher volatility, optimistically biased sentiment, and large data gaps (about 50% of tweet days missing due to structural issues). These findings suggest that sentiment analysis can enhance short-term price forecasting for traditional stocks, while its value for highly volatile assets like Bitcoin remains unclear. Additionally, the results highlight the superior predictive performance of transformer-based sentiment models like FinTwitBERT over lexicon-based alternatives. This study lays the groundwork for future research in real-time or intraday prediction using more complex models and diverse social media sources.
Navigating uncertainty : quantifying residual load and enhancing portfolio resilience for EDP Portugal
Publication . Rauser, Daniel Marco; Nogueira, Miguel
Decarbonizing the energy sector is crucial to achieve climate goals set by international agreements. Energias de Portugal (EDP), which relies heavily on renewable energy, may face significant challenges due to increased potential variability and uncertainty in energy generation caused by climate change. These changes significantly influence the residual load, which is formally defined as the difference between total electricity demand and renewable energy supply. The thesis employs an innovative methodology that uses climate data with advanced predictive analytics to first assess EDP9s current residual load and later estimate future residual loads and its implications until 2045. It is novel in integrating different data, including company-specific data, high resolution climate data amongst others, to provide climate scenario risk assessments. Simulations are conducted to explore strategic mitigation paths such as energy storage expansion, optimizing investments, or bitcoin mining for excess energy utilization. The results show increased residual load in the future, mainly influenced by intensified climate extremes, including anticipated droughts that significantly affect hydropower production, which makes up 75% of EDP9s generation portfolio in severe climate scenarios. The originality of the study is in translating climate risks into interpretable financial terms for EDP and providing concrete recommendations for strategic adaptation especially concerning portfolio expansions. While EDP's current investments, including large-scale batteries and diversified renewable assets, are increasing resilience, this research quantifies further diversification. Ultimately, this thesis highlights the need for proactive, data-driven planning to ensure long term financial stability and operational reliability of renewable-centric energy providers in a climate-uncertain future.