 A solution to portfolio optimisation based on random forest and long short-term memory networksJiaxiu Lin, Ying Sun and Yuelin Gao International Journal of Complexity in Applied Science and Technology, 2025, vol. 1, issue 3, 211-232 Abstract: The randomness of the stock market presents a significant challenge in utilising known information to construct investment portfolios that maximise returns while minimising risks. This paper employs the entropy weight method combined with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assign weights to indicators and subsequently ranks and selects stocks from the CSI 300 Index constituents in the Chinese market and the NASDAQ-100 Index constituents in the US. market. Using random forest (RF) and long short-term memory (LSTM) to predict stock closing prices, the experimental results show that the LSTM model achieves higher predictive accuracy and more stable errors. By selecting the top 7 stocks based on monthly returns to construct a portfolio, the study analyses the returns of investment strategies under different prediction models. The results demonstrate that the portfolio constructed using the LSTM prediction model outperforms other portfolios in terms of cumulative return, annualised return, and Sharpe ratio. Keywords: stock market; investment portfolio; entropy weight-TOPSIS; random forest; RF; long short-term memory; LSTM. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcast:v:1:y:2025:i:3:p:211-232 Access Statistics for this article More articles in International Journal of Complexity in Applied Science and Technology from Inderscience Enterprises Ltd |
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