A Literature Study of Portfolio Management: Integrating Cash Optimization, AI, Financial Modeling, and Sustainable Investment Strategies

This paper explores advancements in portfolio management and investment strategies through the integration of cash optimization, financial modeling, artificial intelligence (AI), and sustainable property investment. It emphasizes the critical role of advanced optimization techniques, such as Particle Swarm Optimization (PSO), in improving portfolio performance by effectively balancing risk and return. Financial modeling is analyzed for its ability to evaluate complex risk-reward trade-offs, thereby promoting informed decision-making and enhancing investor autonomy. The study further investigates the transformative potential of artificial intelligence, particularly large language models like ChatGPT, in automating and streamlining investment strategies. Despite certain limitations in long-term forecasting, AI is shown to provide significant value in optimizing short- to medium-term investment decisions. Additionally, the research examines sustainability within property investment portfolios, demonstrating the vital importance of corporate growth, efficiency, and environmental responsibility in achieving long-term profitability. By integrating innovative techniques, this paper provides actionable insights for researchers, investors, and policymakers to optimize portfolio performance, enhance resilience, and promote sustainable financial outcomes in an increasingly dynamic market environment.