Maximizing Returns with Linear Programming in Systematic Investment Plans

Abstract

Systematic Investment Plans (SIPs) have gained popularity as a disciplined approach to investing, allowing individuals to invest fixed amounts at regular intervals. However, optimizing the allocation of funds across various assets to maximize returns while minimizing risk remains a challenge. This paper explores the application of Linear Programming (LP) in optimizing SIPs. Using Python, the investment problem has been formulated as a linear programming model. It demonstrates how investors can maximize returns subject to constraints such as budget, risk tolerance, and investment horizon. The results indicate that LP can be a powerful tool for enhancing the efficiency of SIPs, providing a structured approach to asset allocation that aligns with investors' financial goals. The research highlights the role of LP in determining the optimal portfolio by solving objective functions that represent returns, while also factoring in real-world investment constraints. Through this approach, investors can potentially improve the performance of their SIP portfolios and make more informed decisions that align with their financial goals. The results demonstrate the effectiveness of LP in systematic investment strategies.