How labor importation constrains talent policy for economic diversification: Evidence from Macao

Integration or segmentation of markets determines whether substantial advantages in risk reduction can be attained through portfolio diversification in foreign securities. In an integrated market, investors face risk from country-specific factors and factors, which are common to all countries, but price only the later, as country-specific risk is diversifiable. The aim of this study is two-fold, firstly, investigating the superiority of the Fama-French three-factor model over Capital Asset Pricing Model (CAPM) and later using the superior model to test for integration of Indian and US equity markets (a proxy for global markets). Based on a sample of Bombay Stock Exchange 500 non-financial companies for the period 2003–2019, the data suggest the superiority of Fama-French three-factor model over CAPM. Using the Non-Linear Seemingly Unrelated Regression technique, the first half of the sample period (2003–2010) shows evidence of market segmentation; however, the second sub-period (2011–2019) shows weak signs of market integration, which is supported by the Johansen test of cointegration, suggesting that Indian market is gradually getting integrated with global markets.

The study reviews the application of machine learning tools in financial investment portfolio management, focusing on cluster analysis for asset allocation, diversification, and risk optimization. The paper aims to explore the use of clustering analysis to broaden the concept of portfolio diversification beyond traditional volatility metrics. An open dataset from Yahoo Finance includes a ten-year historical period (2014–2024) of 130 actively traded securities from international stock markets used. Dataset selection prioritizes top liquidity and trading activity. Python analytical tools were employed to clean, process, and analyze the data. The methodology combines classical Markowitz optimization with clustering analysis techniques, highlighting variance-return trade-offs. Various asset characteristics, including annualized return, standard deviation, Sharpe ratio, correlation with indices, skewness, and kurtosis, were incorporated into the clustering models to reveal hidden patterns and groupings among financial assets. Results show that while clustering enhances insights into asset diversity, classical approaches remain historically superior in optimizing risk-adjusted returns. This study concludes that clustering complements, rather than replaces, classical methods by broadening the understanding of diversification and addressing many diversity factors, such as metrics of the technical, graphical, and fundamental analysis. The paper also introduces the diversity rate based on clustering, which measures the variance balance by all features within and between clusters, providing a broader perspective on diversification beyond traditional metrics. Future research should investigate dynamic clustering techniques, integrate fundamental economic indicators, and develop adaptive models for effective portfolio management in evolving financial markets.

The Norwegian maritime industry is at the forefront of green technology development, with shipyards playing a crucial role in testing, verification, and development. However, the industry faces challenges such as high personal costs, increasing competition from abroad, and cyclical market trends. This study aims to assess financial performance as indicator of firm-level competitiveness based on a set of 12 financial measures and test the hypothesis of the positive impact of portfolio diversification on shipyards’ competitiveness.
The analysis utilizes data from four large construction yards and four medium-sized construction, repair, and maintenance yards in the Møre region. The methodology involves constructing a Shipyard Competitiveness Index with sub-indices for liquidity, profitability, solvency, and efficiency. Regression analysis is conducted to investigate the impact of ship variety, as a diversification parameter, on the competitiveness level.
The obtained results reveal that during the analyzed period (2009–2020), companies in the group of large shipyards had better financial performance until 2017, while on the contrary, the second group of shipyards in the same period showed an increase in their competitiveness index. Moreover, the findings proved the presence of the positive relationship between diversification of portfolio and competitiveness index.
This study contributes valuable insights for the Norwegian shipbuilding industry, highlighting the importance of financial performance assessment in measuring competitiveness. The study provides a foundation for future discussions on fostering sustainable growth and innovation within the maritime sector.