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. 61 , 28 December 2023


Open Access | Article

Dynamic Optimization of Investment Portfolio Based on Self-Attention Mechanism - LSTM Model Prediction

Songwei Wu * 1
1 Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 61, 110-119
Published 28 December 2023. © 2023 The Author(s). Published by EWA Publishing
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.
Citation Songwei Wu. Dynamic Optimization of Investment Portfolio Based on Self-Attention Mechanism - LSTM Model Prediction. AEMPS (2023) Vol. 61: 110-119. DOI: 10.54254/2754-1169/61/20231063.

Abstract

In the era of big data and advanced computational capabilities, financial market participants are continuously searching for innovative strategies to gain a competitive edge. A potential pathway emerges in the domain of deep learning, especially with regard to the LSTM neural architectures, which are renowned for their ability to handle and make predictions based on time series data. This study delves into the utilization of LSTM for predicting stock prices, emphasizing the advantages of dynamic investment portfolios in the rapidly fluctuating market conditions. Utilizing a dynamic window approach for time series data preprocessing, a self-attention mechanism - LSTM model was designed to anticipate the tendency of annual closing prices for five stocks from 2022 to 2023, Utilizing the initial 80% of stock price data as training set and allocating the residual 20% for validation. The performance of the dynamic optimization portfolio model was assessed by dynamically adjusting the weights of the stocks based on the last 20% of the data, and was subsequently compared to actual market cumulative returns. The findings indicate not only that the LSTM model offers a commendable level of accuracy in predicting stock prices, but also that the recursive algorithm for the dynamic optimization portfolio, constrained by maximum returns and minimal standard deviation, consistently outperforms the general market.

Keywords

Long Short Term Memory (LSTM), Dynamic Investment Portfolios, Dynamic Window Approach, Neural Networks, Recursive Algorithm

References

1. Grossman, I., Wilson, T., and Temple, J. (2023). Forecasting small area populations with long short-term memory networks. Socio-Economic Planning Sciences, 101658.

2. Luo, J., and Gong, Y. (2023). Air pollutant prediction based on ARIMA-WOA-LSTM model. Atmospheric Pollution Research, 14(6), 101761.

3. Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications, 227, 120346.

4. Febrian, R., Halim, B. M., Christina, M., Ramdhan, D., and Chowanda, A. (2023). Facial expression recognition using bidirectional LSTM - CNN. Procedia Computer Science, 216, 39-47.

5. Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., and Khatri, R. K.C. (2022). Predicting stock market index using LSTM. Machine Learning with Applications, 9, 100320.

6. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

7. Gers, F. A., Schmidhuber, J., and Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.

8. Wijnberg, K. M., Kilsdonk, R. A. H., and Bomers, A. (2022). Predicting urban flooding due to extreme precipitation using a long short-term memory neural network. Hydrology, 9(6), 105.

9. Park, J., Lee, K., Park, N., You, S. C., and Ko, J. (2023). Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment. Artificial Intelligence in Medicine, 142, 102570.

10. Li, D., and Hu, S. (2023). Adaptive consensus reaching process with dynamic weights and minimum adjustments for group interactive portfolio optimization. Computers & Industrial Engineering, 183, 109491.

11. Pal, R., Datta Chaudhuri, T., and Mukhopadhyay, S. (2021). Portfolio formation and optimization with continuous realignment: A suggested method for choosing the best portfolio of stocks using variable length NSGA-II. Expert Systems with Applications, 186, 115732.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
ISBN (Print)
978-1-83558-223-7
ISBN (Online)
978-1-83558-224-4
Published Date
28 December 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/61/20231063
Copyright
© 2023 The Author(s)
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

Copyright © 2023 EWA Publishing. Unless Otherwise Stated