Application of BiLSTM-Transformer in Portfolio Optimization

Research Article
Open access

Application of BiLSTM-Transformer in Portfolio Optimization

Chuchu Sun 1* , Haoqin Li 2 , Sihan Fu 3
  • 1 Nanjing University of Aeronautics and Astronautics    
  • 2 Jinan University    
  • 3 Wenzhou-Kean University    
  • *corresponding author sccnuaa@nuaa.edu.cn
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/26/20230573
AEMPS Vol.26
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-95-9
ISBN (Online): 978-1-915371-96-6

Abstract

Portfolio optimization models that use predictions can effectively capture short-term investment opportunities. However, in traditional models, inaccurate predictions of the expected excess return of different assets can negatively impact investment performance. Deep learning models have demonstrated significant advantages over time series models in this regard. This paper connects Transformer model and the BiLSTM model, which is short for bi-directional Long Short-Term Memory, for return prediction for portfolio model performance enhancement. To be specific, the model of BiLSTM-Transformer is firstly applied for predicting the yield of alternative assets, which is then incorporated in the mean–variance (MV) model. Using 6 component stocks of the US30 index as alternative assets, 270 investments are conducted, and the empirical results are compared with LSTM and Transformer model. The comparison verifies the superiority of BiLSTM-Transformer model in improving prediction accuracy and boost of portfolio model performance.

Keywords:

prediction, portfolio, optimization, stock, asset

Sun,C.;Li,H.;Fu,S. (2023). Application of BiLSTM-Transformer in Portfolio Optimization. Advances in Economics, Management and Political Sciences,26,212-217.
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References

[1]. Markowitz, H.: Portfolio selection. The Journal of Finance 7(1), 77-91 (1952).

[2]. Dai, Y.L.: Analysis and evaluation of Markowitz model. Financial Research (9) (1991).

[3]. Freitas, F. D., Souza, A. F. D., Almeida, A. R. D.: Prediction-based portfolio optimization model using neural networks. Neurocomputing 72, 2155–2170 (2009).

[4]. Hao, C. Y., Wang, J. Q., Xu, W.: Prediction-based portfolio selection model using support vector machines. In Proceedings of sixth international conference on business intelligence and financial engineering, 567–571 (2013).

[5]. Zhu, M.: Return distribution predictability and its implications for portfolio selection. International Review of Economics & Finance 27, 209–223 (2013).

[6]. Paiva, F. D., Cardoso, R. T. N., Hanaoka, G. P., et al.: Decision-making for Financial Trading: A Fusion Approach of Machine Learning and Portfolio Selection. Expert Systems with Applications (115) (2019).

[7]. Andriosopoulos, D.: Computational Approaches and Data Analytics in Financial Services: A Literature Review. Journal of the Operational Research Society 70(10) (2019).

[8]. Baek, Y., Kim, H. Y.: ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications 113, 457-480(2018).

[9]. Ozbayoglu, A. M., Gudelek, M. U., Sezer, O. B.: Deep learning for financial applications: A survey. Applied Soft Computing 93, 106384 (2020).

[10]. Ma, Y., Han, R., Wang, W.: Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications 165, 113973 (2021).

[11]. Zhang, N., Yan, S., Fan, D.: Return prediction and portfolio optimization based on deep learning. Statistical Research 36(3), 67-79 (2019).

[12]. Huang, Z., Xu, P., Liang, D., et al.: TRANS-BLSTM: Transformer with bidirectional LSTM for language understanding. arXiv preprint arXiv:2003.07000, (2020).

[13]. Zhao, Z., Chen, Y., Liu, J., et al.: Evaluation of Operating State for Smart Electricity Meters Based on Transformer–Encoder–BiLSTM. IEEE Transactions on Industrial Informatics 19(3), 2409-2420 (2022).

[14]. Varun, Y., Sharma, A., Gupta, V.: Trans-kblstm: An external knowledge enhanced transformer bilstm model for tabular reasoning. Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 62-78 (2022).

[15]. Wu, X.H., Sun, C., Hao, X.Y.: Stock Closing Price Interval Prediction Based on CEEMDAN-WTD-Bilstm-Transformer Model. Available at Research gate https://www.researchgate.net/, last accessed 2023/4/2.

[16]. Vaswani, Ashish, et al.: Attention is all you need. Advances in neural information processing systems 30, 5998-6008(2017).

Cite this article

Sun,C.;Li,H.;Fu,S. (2023). Application of BiLSTM-Transformer in Portfolio Optimization. Advances in Economics, Management and Political Sciences,26,212-217.

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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About volume

Volume title: Proceedings of the 2023 International Conference on Management Research and Economic Development

ISBN：978-1-915371-95-9(Print) / 978-1-915371-96-6(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.26
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Markowitz, H.: Portfolio selection. The Journal of Finance 7(1), 77-91 (1952).

[2]. Dai, Y.L.: Analysis and evaluation of Markowitz model. Financial Research (9) (1991).

[3]. Freitas, F. D., Souza, A. F. D., Almeida, A. R. D.: Prediction-based portfolio optimization model using neural networks. Neurocomputing 72, 2155–2170 (2009).

[4]. Hao, C. Y., Wang, J. Q., Xu, W.: Prediction-based portfolio selection model using support vector machines. In Proceedings of sixth international conference on business intelligence and financial engineering, 567–571 (2013).

[5]. Zhu, M.: Return distribution predictability and its implications for portfolio selection. International Review of Economics & Finance 27, 209–223 (2013).

[6]. Paiva, F. D., Cardoso, R. T. N., Hanaoka, G. P., et al.: Decision-making for Financial Trading: A Fusion Approach of Machine Learning and Portfolio Selection. Expert Systems with Applications (115) (2019).

[7]. Andriosopoulos, D.: Computational Approaches and Data Analytics in Financial Services: A Literature Review. Journal of the Operational Research Society 70(10) (2019).

[8]. Baek, Y., Kim, H. Y.: ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications 113, 457-480(2018).

[9]. Ozbayoglu, A. M., Gudelek, M. U., Sezer, O. B.: Deep learning for financial applications: A survey. Applied Soft Computing 93, 106384 (2020).

[10]. Ma, Y., Han, R., Wang, W.: Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications 165, 113973 (2021).

[11]. Zhang, N., Yan, S., Fan, D.: Return prediction and portfolio optimization based on deep learning. Statistical Research 36(3), 67-79 (2019).

[12]. Huang, Z., Xu, P., Liang, D., et al.: TRANS-BLSTM: Transformer with bidirectional LSTM for language understanding. arXiv preprint arXiv:2003.07000, (2020).

[13]. Zhao, Z., Chen, Y., Liu, J., et al.: Evaluation of Operating State for Smart Electricity Meters Based on Transformer–Encoder–BiLSTM. IEEE Transactions on Industrial Informatics 19(3), 2409-2420 (2022).

[14]. Varun, Y., Sharma, A., Gupta, V.: Trans-kblstm: An external knowledge enhanced transformer bilstm model for tabular reasoning. Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 62-78 (2022).

[15]. Wu, X.H., Sun, C., Hao, X.Y.: Stock Closing Price Interval Prediction Based on CEEMDAN-WTD-Bilstm-Transformer Model. Available at Research gate https://www.researchgate.net/, last accessed 2023/4/2.

[16]. Vaswani, Ashish, et al.: Attention is all you need. Advances in neural information processing systems 30, 5998-6008(2017).

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