Portfolio Optimization Using LSTM for Five Selected Stocks

Portfolio construction is very critical in the financial markets as it assists investors in achieving maximum returns with minimum risks through diversification. In this paper, Long Short-Term Memory (LSTM) models are employed to predict stock prices and optimize investment portfolios. Historical adjusted closing prices from Yahoo Finance for the period from January 1, 2023, to March 1, 2024, were used to predict the future stock prices of Apple Inc. (AAPL), Microsoft Corporation (MSFT), BlackRock Inc. (BLK), JPMorgan Chase & Co. (JPM), and Tesla Inc. (TSLA) using LSTM models. Portfolios were constructed using mean-variance optimization based on these predictions, and Monte Carlo simulations were employed to capture the uncertainty around the estimates. The results are treasured for investors in financial markets as they spotlight the limitations and capability risks of depending totally on gadget learning predictions for portfolio optimization. This emphasizes the want for a greater comprehensive method to investment decision-making that considers more than one factors and methodologies.