Long short-term memory autoencoder based network of financial indices
We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time se...
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| Vydané v: | Humanities & social sciences communications Ročník 12; číslo 1; s. 100 - 15 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Palgrave Macmillan UK
29.01.2025
Springer Nature B.V Springer Nature |
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| ISSN: | 2662-9992, 2662-9992 |
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| Abstract | We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time series data spanning from 2000 to 2022, encompassing multiple financial crises within the S&P 500 stock indices. By training a modified LSTMAE with normalized stock index returns, we extract the inherent correlations embedded in the model weights. We create directional threshold networks by applying a fixed threshold, calculated as the sum of the mean and standard deviation of matrices from various years. Our investigation explores the topological characteristics of these threshold networks across different years. Notably, the observed network properties exhibit unique responses to the various financial crises that occurred between 2000 and 2022. Furthermore, our sector analysis reveals substantial sectoral influences during times of crisis. For example, during global financial crises, the financial sector assumes a prominent role, exerting significant influence on other sectors, particularly during the European Sovereign Debt (ESD) crisis. During the COVID-19 pandemic, the health care and consumer discretionary sectors are predominantly impacted by other sectors. Our proposed method effectively captures the underlying network structure of financial markets and is validated by a comprehensive analysis of network metrics, demonstrating its ability to identify significant financial crises over time. |
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| AbstractList | Abstract We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time series data spanning from 2000 to 2022, encompassing multiple financial crises within the S&P 500 stock indices. By training a modified LSTMAE with normalized stock index returns, we extract the inherent correlations embedded in the model weights. We create directional threshold networks by applying a fixed threshold, calculated as the sum of the mean and standard deviation of matrices from various years. Our investigation explores the topological characteristics of these threshold networks across different years. Notably, the observed network properties exhibit unique responses to the various financial crises that occurred between 2000 and 2022. Furthermore, our sector analysis reveals substantial sectoral influences during times of crisis. For example, during global financial crises, the financial sector assumes a prominent role, exerting significant influence on other sectors, particularly during the European Sovereign Debt (ESD) crisis. During the COVID-19 pandemic, the health care and consumer discretionary sectors are predominantly impacted by other sectors. Our proposed method effectively captures the underlying network structure of financial markets and is validated by a comprehensive analysis of network metrics, demonstrating its ability to identify significant financial crises over time. We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time series data spanning from 2000 to 2022, encompassing multiple financial crises within the S&P 500 stock indices. By training a modified LSTMAE with normalized stock index returns, we extract the inherent correlations embedded in the model weights. We create directional threshold networks by applying a fixed threshold, calculated as the sum of the mean and standard deviation of matrices from various years. Our investigation explores the topological characteristics of these threshold networks across different years. Notably, the observed network properties exhibit unique responses to the various financial crises that occurred between 2000 and 2022. Furthermore, our sector analysis reveals substantial sectoral influences during times of crisis. For example, during global financial crises, the financial sector assumes a prominent role, exerting significant influence on other sectors, particularly during the European Sovereign Debt (ESD) crisis. During the COVID-19 pandemic, the health care and consumer discretionary sectors are predominantly impacted by other sectors. Our proposed method effectively captures the underlying network structure of financial markets and is validated by a comprehensive analysis of network metrics, demonstrating its ability to identify significant financial crises over time. |
| ArticleNumber | 100 |
| Author | Lee, Jae Woo Tuhin, Kamrul Hasan Nobi, Ashadun Rakib, Mahmudul Hasan |
| Author_xml | – sequence: 1 givenname: Kamrul Hasan surname: Tuhin fullname: Tuhin, Kamrul Hasan organization: Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman University – sequence: 2 givenname: Ashadun surname: Nobi fullname: Nobi, Ashadun organization: Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University – sequence: 3 givenname: Mahmudul Hasan surname: Rakib fullname: Rakib, Mahmudul Hasan organization: Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Department of Computer Science and Engineering, Daffodil International University – sequence: 4 givenname: Jae Woo orcidid: 0000-0003-0622-4649 surname: Lee fullname: Lee, Jae Woo email: jaewlee@inha.ac.kr organization: Department of Physics, Inha University |
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| Snippet | We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary... Abstract We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our... |
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| SubjectTerms | 4014/2801 4014/4006 Causality COVID-19 Deep learning Economic crisis Extraction Financial institutions Health care Health services Humanities and Social Sciences Indexes International finance Machine learning Matrices Methods multidisciplinary National debt Networks Pandemics Property Public debt Rates of return Science Science (multidisciplinary) Securities markets Short term Short term memory Stock exchanges Thresholds Time series |
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| Title | Long short-term memory autoencoder based network of financial indices |
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