Enhancing Portfolio Performances through LSTM and Covariance Shrinkage

Abstract

Portfolio optimization is a crucial aspect of finance, requiring advanced analytical tools and modeling techniques. This paper proposes a new method for portfolio optimization that combines Long Short-Term Memory (LSTM) forecasting with Covariance Shrinkage and Mean-Variance Optimization (MVO) to construct diversified portfolios that maximize risk-adjusted returns. The study utilizes an LSTM-based model to predict stock prices, evaluating its performance using the RMSE metric. The calculated RMSE of 0.0849 indicates accurate and robust predictions. The portfolio constructed shows different weights each day for different assets based on the minimum variance and maximum Sharpe ratio portfolios. As of January 3rd, 2023, the assets with the largest proportion in the Maximum Sharpe Ratio portfolio and in the Minimum Volatility portfolio, are respectively BA, accounting for 27.64% of the portfolio and PG, accounting for 32.66% of the portfolio. This paper compares the performance of the proposed method and benchmark methods by applying 30 daily portfolio weights to real returns. The portfolio constructed by the proposed method has higher cumulative return with a higher Sharpe ratio and lower maximum drawdown, indicating a better ability to diversify risks and create returns. The proposed method offers a new perspective on portfolio optimization, which can potentially benefit investors and asset managers.