Investment Weightings of Technology and Traditional Stocks: Investor Choice and Risk Management

Abstract

In the era of big data, the financial landscape is undergoing transformative changes through the integration of advanced technologies. This research delves into the dynamic realm of asset portfolios within the information technology industry, traditional sectors, and amalgamated portfolios containing popular giant stocks. Employing Python and machine learning, the study focuses on predicting expected returns, comparing and analyzing diverse portfolios to identify optimal investment strategies for risk management. Evaluation metrics include annual return, annual volatility, Sharpe ratio, and statistical charts. The findings highlight the lucrative potential of investing in the information technology sector, revealing an impressive annual return rate of 40.1%. In contrast, traditional industry portfolios not only underperform but also exhibit high-risk profiles and diminished returns. This research underscores the critical role of technological advancements and data-driven methodologies in shaping contemporary financial strategies for robust portfolio management. his research advocates for the strategic incorporation of technology-driven insights in financial decision-making, emphasizing the significant outperformance potential of information technology portfolios in navigating the complexities of modern investment.