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Since early times, the effects of a booming sector in other sectors of a small economy have been of interest to scholars. There is a general perception that the booming Gaming sector has contributed to the overall growth in Macau through the trickle-down effect, passing on the benefits of growth to other sectors. After the liberalization of the gaming industry in 2002, this booming sector experienced several years of exponential growth, becoming the driving industry for Macao’s economy. Several scholars and researchers have dedicated their studies to the effects of the casino gaming industry as a booming sector in such a small economy. However, there is a gap in what concerns measuring the influence of the Gaming sector as a driving industry for several other sectors or following industries of Macau’s economy. The purpose of this research study is to investigate in what measure the Gaming sector in Macao leveraged the other economic sectors and how related or correlated are the different industries of Macao’s Economy. A protocol-driven understanding of the state of the art on the interrelations between economic sectors and different techniques used to study those inter-relations was conducted through a systematic literature review. Given the limited available data on the Gross Value Added (GVA), or Gross Domestic Product (GDP) on the supply side, as a central measure of economic activity in the different sectors, several possible interpolation models using auxiliary high-frequency data (indicators) were compared, to achieve the optimal model for interpolation of each variable. Several forecasts for the future performance of Macau's four major economic sectors were presented based on different regression techniques. Autoregressive Integrated Moving Average (ARIMA) models were developed to assess the dependence of the future performance of a sector’s GVA on its past performance. Optimal Vector Autoregressive (VAR) models were created to identify the explanatory power of some sectors of Macau’s economy in others. Based on available auxiliary data in high-frequency (quarterly) it was possible to interpolate the quarterly GVA per economic sector, available only in low-frequency (annually), for the major sectors of Macao’s economy. Some sectors have a considerable explanatory power on the performance of other sectors, however, the proposed regression models did not identify a clear relation between the performance of the Gaming sector and the performance of other major sectors from Macao’s economy
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The stock market's inherent volatility and complexity pose significant challenges for investors seeking to optimize their strategies. This thesis addresses the critical need for improved forecasting methods in stock price prediction by proposing a hybrid approach that combines traditional machine learning (ML) techniques, specifically Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks, with sentiment analysis derived from financial news and social media platforms. The research establishes a theoretical framework integrating quantitative data, such as historical stock prices, with qualitative sentiment data to enhance prediction accuracy. The study involves the collection of a comprehensive dataset covering stock prices and sentiment scores from various sources, including news articles and social media posts, from January 2010 to December 2023. Rigorous data preprocessing techniques, including normalization and feature engineering, are employed to prepare the data for analysis. A comparative analysis of the SVM and LSTM models uses multiple performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and classification accuracy. The findings reveal that the LSTM model significantly outperforms the SVM model in predictive accuracy, demonstrating its capability to capture complex temporal dependencies inherent in financial time series data. Furthermore, integrating sentiment analysis significantly enhances the predictive performance of both models. Notably, transformer-based sentiment analysis techniques, such as BERT and DistilBERT, provide superior sentiment classifications compared to traditional methods like VADER and TextBlob. The empirical results indicate that incorporating sentiment data leads to an average accuracy improvement of 12.8% over models that rely solely on historical price data. This research contributes to the evolving field of financial forecasting by emphasizing the importance of a hybrid approach that amalgamates quantitative and qualitative data. The implications of these findings extend beyond academic research, offering valuable insights for investors and financial analysts seeking to leverage advanced predictive models to navigate market uncertainties. Ultimately, this dissertation advocates adopting sophisticated hybrid models that enhance stock investment strategies and decision-making processes in the finance sector.
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This study sought to determine the strategy that allowed the Las Vegas Sands Corporation (LVS) to attain its leading status in the casino industry and to gain insight whether this status would continue given (i) the passing of the LVS founder, Sheldon Adelson, in January 2021, (ii)the sell-off of the company's Las Vegas properties early in 2022, and (iii) the firm's greater sensitivity to events in China caused by the company's increased reliance for most of its customers on the mainland China market. The study first identified the nature of the LVS competitive advantages when Adelson was directing the firm and then assessed whether these had been adversely impacted due to changes in the firm's markets, management or strategy. The study relied initially on the work of David Baron, Professor of Political Economy and Strategy at Stanford University who as early as 1981 advanced the view that corporate strategy needed to be divided in a Marketing Strategy (MS) and a Non-Market Strategy (NMS). The NMS component for LVS was critically important since government determined who could acquire a Macau casino concession and what level of visas would be provided to Mainland China gamblers to fill the Macau casinos. The key question became the nature of Adelson's Political Effectiveness as determined by the NMS he directed towards the China market. To resolve this issue, we adopted the Wellner & Lakotta proposal to extend Porter's Five Forces analytical framework by two additional dimensions, Government Interventors and Complementor Organizations. We concluded that it was highly likely that Goldman Sachs, the long-term financial backer of Sheldon Adelson, played a significant if not the major role in the success Adelson was able to achieve in the Greater China market.