Study and Analysis of Stock Market Prediction Techniques

Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The...
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Autor*in:	Kokare Siddhesh [verfasserIn] Kamble Anvit [verfasserIn] Kurade Shubham [verfasserIn] Patil Deepali [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	stock market prediction lstm cnn cnn-lstm

Übergeordnetes Werk:	In: ITM Web of Conferences - EDP Sciences, 2014, 44, p 03033(2022)
Übergeordnetes Werk:	volume:44, p 03033 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1051/itmconf/20224403033

Katalog-ID:	DOAJ025947125

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allfieldsSound	10.1051/itmconf/20224403033 doi (DE-627)DOAJ025947125 (DE-599)DOAJ046e17f1ab9749e7bfe8b56c740d5d73 DE-627 ger DE-627 rakwb eng T58.5-58.64 Kokare Siddhesh verfasserin aut Study and Analysis of Stock Market Prediction Techniques 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very challenging and strenuous task of financial stock market. The development of efficient models for prediction decisions is very difficult because of the convolution of stock market financial data and should have high accuracy. This study attempts to compare existing models for the stock market. Various Machine learning methods like Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Convolution Neural Networks – Long Term Short Memory (CNN-LSTM) have been used for the comparison. The models are estimated using conventional strategic measure: MAE (Mean Absolute Error). The measured low values indicates that the models are effective in predicting stock prices. stock market prediction lstm cnn cnn-lstm Information technology Kamble Anvit verfasserin aut Kurade Shubham verfasserin aut Patil Deepali verfasserin aut In ITM Web of Conferences EDP Sciences, 2014 44, p 03033(2022) (DE-627)77837209X (DE-600)2755683-9 22712097 nnns volume:44, p 03033 year:2022 https://doi.org/10.1051/itmconf/20224403033 kostenfrei https://doaj.org/article/046e17f1ab9749e7bfe8b56c740d5d73 kostenfrei https://www.itm-conferences.org/articles/itmconf/pdf/2022/04/itmconf_icacc2022_03033.pdf kostenfrei https://doaj.org/toc/2271-2097 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_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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 44, p 03033 2022
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callnumber-first	T - Technology
author	Kokare Siddhesh
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title	Study and Analysis of Stock Market Prediction Techniques
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title_auth	Study and Analysis of Stock Market Prediction Techniques
abstract	Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very challenging and strenuous task of financial stock market. The development of efficient models for prediction decisions is very difficult because of the convolution of stock market financial data and should have high accuracy. This study attempts to compare existing models for the stock market. Various Machine learning methods like Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Convolution Neural Networks – Long Term Short Memory (CNN-LSTM) have been used for the comparison. The models are estimated using conventional strategic measure: MAE (Mean Absolute Error). The measured low values indicates that the models are effective in predicting stock prices.
abstractGer	Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very challenging and strenuous task of financial stock market. The development of efficient models for prediction decisions is very difficult because of the convolution of stock market financial data and should have high accuracy. This study attempts to compare existing models for the stock market. Various Machine learning methods like Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Convolution Neural Networks – Long Term Short Memory (CNN-LSTM) have been used for the comparison. The models are estimated using conventional strategic measure: MAE (Mean Absolute Error). The measured low values indicates that the models are effective in predicting stock prices.
abstract_unstemmed	Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very challenging and strenuous task of financial stock market. The development of efficient models for prediction decisions is very difficult because of the convolution of stock market financial data and should have high accuracy. This study attempts to compare existing models for the stock market. Various Machine learning methods like Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Convolution Neural Networks – Long Term Short Memory (CNN-LSTM) have been used for the comparison. The models are estimated using conventional strategic measure: MAE (Mean Absolute Error). The measured low values indicates that the models are effective in predicting stock prices.
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title_short	Study and Analysis of Stock Market Prediction Techniques
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