Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model
The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conven...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang Feiyu [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch ; Französisch |
| Erschienen: |
2020 |
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| Übergeordnetes Werk: |
In: E3S Web of Conferences - EDP Sciences, 2013, 214, p 02040(2020) |
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| Übergeordnetes Werk: |
volume:214, p 02040 ; year:2020 |
| Links: |
|---|
| DOI / URN: |
10.1051/e3sconf/202021402040 |
|---|
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10.1051/e3sconf/202021402040 doi (DE-627)DOAJ05367152X (DE-599)DOAJbd3360701b3d40519c291d8380bb7fdc DE-627 ger DE-627 rakwb eng fre GE1-350 Wang Feiyu verfasserin aut Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy. Environmental sciences In E3S Web of Conferences EDP Sciences, 2013 214, p 02040(2020) (DE-627)778372081 (DE-600)2755680-3 22671242 nnns volume:214, p 02040 year:2020 https://doi.org/10.1051/e3sconf/202021402040 kostenfrei https://doaj.org/article/bd3360701b3d40519c291d8380bb7fdc kostenfrei https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/74/e3sconf_ebldm2020_02040.pdf kostenfrei https://doaj.org/toc/2267-1242 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 214, p 02040 2020 |
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10.1051/e3sconf/202021402040 doi (DE-627)DOAJ05367152X (DE-599)DOAJbd3360701b3d40519c291d8380bb7fdc DE-627 ger DE-627 rakwb eng fre GE1-350 Wang Feiyu verfasserin aut Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy. Environmental sciences In E3S Web of Conferences EDP Sciences, 2013 214, p 02040(2020) (DE-627)778372081 (DE-600)2755680-3 22671242 nnns volume:214, p 02040 year:2020 https://doi.org/10.1051/e3sconf/202021402040 kostenfrei https://doaj.org/article/bd3360701b3d40519c291d8380bb7fdc kostenfrei https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/74/e3sconf_ebldm2020_02040.pdf kostenfrei https://doaj.org/toc/2267-1242 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 214, p 02040 2020 |
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Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model |
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The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy. |
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The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy. |
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The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy. |
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