References
[1]. Nan W 2015 A comparison of several statistical inference methods for VaR (Guangxi: Guangxi Normal University).
[2]. Guru B K and Yadav I S 2019 Financial development and economic growth: panel evidence from BRICS Journal of Economics, Finance and Administrative Science vol 24 p 113-26.
[3]. Bustos O and Pomares-Quimbaya 2020 Stock market movement forecast: A systematic review Expert Systems with Applications vol 156 p 113464.
[4]. Y Lecun, L Bottou, Y Bengio and P Haffner 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE vol 86.11 p 2278-324.
[5]. Yang C, Zhai J and Tao G 2020 Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory Mathematical Problems in Engineering vol 2020 p 1-13.
[6]. Mehtab S and Sen J 2020 Stock price prediction using convolutional neural networks on a multivariate timeseries.
[7]. Henrique B M, Sobreiro V A and Kimura H 2018 Stock price prediction using support vector regression on daily and up to the minute prices The Journal of Finance and Data Science vol 4.3 p 183-201.
[8]. Saeed M, Ahmad I and Usman M A 2021 Do the stocks’ returns and volatility matter under the COVID-19 pandemic? A Case Study of Pakistan Stock Exchange iRASD Journal of Economics vol 3.1 p 13-26.
[9]. Hoseinzade E and Haratizadeh S 2019 CNNpred: CNN-based stock market prediction using a diverse set of variables Expert Systems with Applications vol 12 p 273-85.
[10]. Wright R E 1995 Logistic regression pp 217–244.
[11]. Cortes C and Vapnik V 1995 Support-vector networks Machine learning vol 20 pp 273-97.
[12]. Rumelhart D E, Hinton G E and Williams R J 1986 Learning representations by back-propagating errors nature Nature vol 323.6088 pp 533-536.
[13]. McClelland J L, Rumelhart D E and PDP Research Group 1987 Psychological and Biological Models vol 2.
[14]. Nti I K, Adekoya A F and Weyori B A 2020 A comprehensive evaluation of ensemble learning for stock-market prediction Journal of Big Data vol 7.1 p 1-40.
[15]. Ruibo W 2019 Research on regularized cross-validation method for prediction performance comparison of supervised learning algorithms.
[16]. Hyun Sik Sim, Hae In Kim and Jae Joon Ahn 2019 Is Deep Learning for Image Recognition Applicable to Stock Market Prediction? Complexity vol 2019.
[17]. Goodfellow I, Bengio Y and Courville A 2016 Deep learning Nature vol 521.7553 p 436-44.
[18]. Smith L N 2018 A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay ArXiv Preprints vol 1803.09820.
[19]. Smith S L, Kindermans P J, Ying C and Le V Q 2017 Don't decay the learning rate, increase the batch size ArXiv Preprint vol 1711.00489.
Cite this article
Zheng,X. (2023). Comparative analysis of machine learning and deep learning techniques for prediction of the stock market. Applied and Computational Engineering,17,139-149.
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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References
[1]. Nan W 2015 A comparison of several statistical inference methods for VaR (Guangxi: Guangxi Normal University).
[2]. Guru B K and Yadav I S 2019 Financial development and economic growth: panel evidence from BRICS Journal of Economics, Finance and Administrative Science vol 24 p 113-26.
[3]. Bustos O and Pomares-Quimbaya 2020 Stock market movement forecast: A systematic review Expert Systems with Applications vol 156 p 113464.
[4]. Y Lecun, L Bottou, Y Bengio and P Haffner 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE vol 86.11 p 2278-324.
[5]. Yang C, Zhai J and Tao G 2020 Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory Mathematical Problems in Engineering vol 2020 p 1-13.
[6]. Mehtab S and Sen J 2020 Stock price prediction using convolutional neural networks on a multivariate timeseries.
[7]. Henrique B M, Sobreiro V A and Kimura H 2018 Stock price prediction using support vector regression on daily and up to the minute prices The Journal of Finance and Data Science vol 4.3 p 183-201.
[8]. Saeed M, Ahmad I and Usman M A 2021 Do the stocks’ returns and volatility matter under the COVID-19 pandemic? A Case Study of Pakistan Stock Exchange iRASD Journal of Economics vol 3.1 p 13-26.
[9]. Hoseinzade E and Haratizadeh S 2019 CNNpred: CNN-based stock market prediction using a diverse set of variables Expert Systems with Applications vol 12 p 273-85.
[10]. Wright R E 1995 Logistic regression pp 217–244.
[11]. Cortes C and Vapnik V 1995 Support-vector networks Machine learning vol 20 pp 273-97.
[12]. Rumelhart D E, Hinton G E and Williams R J 1986 Learning representations by back-propagating errors nature Nature vol 323.6088 pp 533-536.
[13]. McClelland J L, Rumelhart D E and PDP Research Group 1987 Psychological and Biological Models vol 2.
[14]. Nti I K, Adekoya A F and Weyori B A 2020 A comprehensive evaluation of ensemble learning for stock-market prediction Journal of Big Data vol 7.1 p 1-40.
[15]. Ruibo W 2019 Research on regularized cross-validation method for prediction performance comparison of supervised learning algorithms.
[16]. Hyun Sik Sim, Hae In Kim and Jae Joon Ahn 2019 Is Deep Learning for Image Recognition Applicable to Stock Market Prediction? Complexity vol 2019.
[17]. Goodfellow I, Bengio Y and Courville A 2016 Deep learning Nature vol 521.7553 p 436-44.
[18]. Smith L N 2018 A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay ArXiv Preprints vol 1803.09820.
[19]. Smith S L, Kindermans P J, Ying C and Le V Q 2017 Don't decay the learning rate, increase the batch size ArXiv Preprint vol 1711.00489.