LSTM-based Portfolio Optimization Strategy
DOI:
https://doi.org/10.54097/n4ypa255Keywords:
Long short-term memory, Portfolio optimization, Mean-variance, Investment Strategy.Abstract
In the highly competitive and constantly evolving landscape of the investment realm, the construction of an optimal investment portfolio has emerged as a cornerstone for investors aiming to adeptly navigate the complex interplay between risks and returns. With the rapid advancement of technology, the performance and trends of stocks have become increasingly intertwined with technological developments. This study analyzes five tech-dependent stocks and applies the Long Short-Term Memory (LSTM) model for price prediction. The combination of LSTM and Mean-Variance Optimization (MVO) model outputs daily portfolio weights using two strategies: minimizing variance and maximizing Sharpe ratio. The minimal variance model produces a cumulative return of 193.1164% during a 30-day testing period from day 71 to day 100; the maximum Sharpe ratio model produces a return of 61.2246%, both exceeding the return of the CSI 300 Index at just 14.6193%. The outcomes validate the effectiveness of the suggested approach and provide insightful analysis for portfolio managers choosing their course of action.
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