Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 48 , 01 December 2023
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Access | Article
Evaluation of Performance for LSTM-based Minimal Variation Optimized Portfolios
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Abstract
The paper focuses on evaluate the effectiveness of the combined method of LSTM models and minimal variation optimized portfolio in achieving promising return. Daily adjusted closed prices of 21 stocks in American market are collected. Two distinct portfolios are then created and optimized based on their according optimal portfolio weights obtained from forecasting results of LSTM models and minimal variation optimization. The 21-day portfolio return can then be calculated based on the real-world returns of these stocks. Portfolio 1 achieves a 21-day return of 4.3%, and portfolio 2 achieves 0.8%. Returns of both portfolios are significantly higher than the S&P500 index return of the same time period, which is around -4.8%. It is safe to conclude that LSTM enhanced minimal variation optimized portfolios are effective in reaching promising returns even when the market is not optimistic. By adopting and modifying the method, investors can expect to gain considerable returns in the stock market even in the time period when the general market is not optimistic. The research also serves as a replicable example of steps to optimized investing portfolios.
Keywords
LSTM, minimal variation optimized portfolios, S&P500 index return
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-143-8
- ISBN (Online)
- 978-1-83558-144-5
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/48/20230422
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited