LSTM-based Portfolio Optimization Study in the Food and Beverage Industry

This study investigates how to best allocate a portfolio using long short-term memory (LSTM) networks to anticipate stock prices in the food and beverage industry. The 10-year historical daily closing prices of five major food and beverage stocks were collected as model training data. A recurrent neural network model with LSTM and fully connected layers was constructed. The weight allocation of different stocks in the initial portfolio was evaluated by Monte Carlo simulation. The Nestle stock with higher risk-return was selected for individual stock price prediction. The results show that Nestle's stock price has a slight upward trend, and it is recommended to increase its weighting in the portfolio appropriately. However, the real range of price variations cannot be correctly anticipated due to the incomplete consideration of influencing factors. The main conclusion is that the LSTM model can reasonably fit the historical stock price time series and judge the general trend, but it needs to expand the data and features to improve the prediction accuracy. This preliminary study provides a basic analysis for LSTM predictive modelling of food and beverage stocks, but further optimization of the model design and expansion of the data range are needed to achieve a reliable optimization of the risk-return trade-off in portfolio management.