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DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks
Portfolio optimization has been broadly investigated during the last decades and had a lot of applications in finance and economics. In this paper, we study the portfolio optimization problem in the Vietnamese stock market by using deep-learning methodologies and one dataset collected from the Ho Ch...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206164/ http://dx.doi.org/10.1007/978-3-030-47426-3_48 |
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author | Cao, Hieu K. Cao, Han K. Nguyen, Binh T. |
author_facet | Cao, Hieu K. Cao, Han K. Nguyen, Binh T. |
author_sort | Cao, Hieu K. |
collection | PubMed |
description | Portfolio optimization has been broadly investigated during the last decades and had a lot of applications in finance and economics. In this paper, we study the portfolio optimization problem in the Vietnamese stock market by using deep-learning methodologies and one dataset collected from the Ho Chi Minh City Stock Exchange (VN-HOSE) from the beginning of the year 2013 to the middle of the year 2019. We aim to construct an efficient algorithm that can find the portfolio having the highest Sharpe ratio in the next coming weeks. To overcome this challenge, we propose a novel loss function and transform the original problem into a supervised problem. The input data can be determined as a 3D tensor, while the predicted output is the unnormalized weighted proportion for each ticker in the portfolio to maximize the daily return Y of the stock market after a given number of days. We compare different deep learning models, including Residual Networks (ResNet), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Self-Attention (SA), Additive Attention (AA), and various combinations: SA + LSTM, SA + GRU, AA + LSTM, and AA + GRU. The experimental results show that the AA + GRU outperforms the rest of the methods on the Sharpe ratio and provides promising results for the portfolio optimization problem not only in Vietnam but also in other countries. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-47426-3_48) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7206164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72061642020-05-08 DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks Cao, Hieu K. Cao, Han K. Nguyen, Binh T. Advances in Knowledge Discovery and Data Mining Article Portfolio optimization has been broadly investigated during the last decades and had a lot of applications in finance and economics. In this paper, we study the portfolio optimization problem in the Vietnamese stock market by using deep-learning methodologies and one dataset collected from the Ho Chi Minh City Stock Exchange (VN-HOSE) from the beginning of the year 2013 to the middle of the year 2019. We aim to construct an efficient algorithm that can find the portfolio having the highest Sharpe ratio in the next coming weeks. To overcome this challenge, we propose a novel loss function and transform the original problem into a supervised problem. The input data can be determined as a 3D tensor, while the predicted output is the unnormalized weighted proportion for each ticker in the portfolio to maximize the daily return Y of the stock market after a given number of days. We compare different deep learning models, including Residual Networks (ResNet), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Self-Attention (SA), Additive Attention (AA), and various combinations: SA + LSTM, SA + GRU, AA + LSTM, and AA + GRU. The experimental results show that the AA + GRU outperforms the rest of the methods on the Sharpe ratio and provides promising results for the portfolio optimization problem not only in Vietnam but also in other countries. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-47426-3_48) contains supplementary material, which is available to authorized users. 2020-04-17 /pmc/articles/PMC7206164/ http://dx.doi.org/10.1007/978-3-030-47426-3_48 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Cao, Hieu K. Cao, Han K. Nguyen, Binh T. DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks |
title | DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks |
title_full | DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks |
title_fullStr | DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks |
title_full_unstemmed | DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks |
title_short | DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks |
title_sort | delafo: an efficient portfolio optimization using deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206164/ http://dx.doi.org/10.1007/978-3-030-47426-3_48 |
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