Optimization of Investment Portfolio Selection Using Time Series Algorithms

Abstract

In contemporary financial analytics, precise forecasting and optimizing investment portfolios are paramount. Traditional diversification methods have limitations in volatile markets, necessitating innovative approaches. This article proposes leveraging time series algorithms to enhance the analysis of market data and improve forecasting accuracy. The algorithm's development and implementation offer a breakthrough, significantly improving investment decision accuracy. The focus is on adapting algorithms to dynamic market conditions, utilizing new technologies for effective risk management, and achieving a balance between risk and return. This article addresses gaps in existing research, emphasizing the importance of time series algorithms in optimizing investment portfolios for robust decision-making in diverse market scenarios.