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
Open
Access | Article
Portfolio Optimization by LSTM with a Selection of Six Stocks
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Abstract
As the financial industry undergoes continuous evolution, efficient asset allocation has become increasingly crucial. However, traditional methods employed for portfolio optimization are often deemed inefficient and in need of improvement. To address this, recent advancements in deep learning techniques provide a promising perspective to tackle portfolio optimization, offering new possibilities for maximizing returns or minimizing risk based on specific objectives and constraints. This study delves into the analysis of stock data from six distinct industries. By utilizing the LSTM model and employing the Monte Carlo method, efficient frontier, and other advanced techniques, a training set is constructed to generate predictions using the first 80% of the data. For testing purposes, the remaining 20% of the data is utilized to assess how well the created portfolio performed. Various performance metrics such as portfolio returns, volatility, Sharpe Ratio, and maximum reduction are calculated to assess the effectiveness of the LSTM-based portfolio. Additionally, a comparison is made against other benchmark portfolios or strategies. The results for evaluation show that the LSTM-based portfolio outperform the commonly used benchmark model. This study illuminates the potential of deep learning in the financial industry, presenting groundbreaking applications that offer novel portfolio allocation strategies.
Keywords
LSTM-based portfolio, Mean-Variance model, Monte Carlo simulation, efficient frontier
References
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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
- 10.54254/2754-1169/48/20230423
- 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