Artificial Neural Networks architectures for stock price prediction: comparisons and applications

By Luca Di Persio, O. Honchar. 2016

220 citations recorded by the source corpus.

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Citations in the corpus

Recurrent Neural Networks Approach to the Financial Forecast of Google Assets (2017, neutral): “Nevertheless, black box methods, like the NNs ones, are appealing because they require no prior assumptions on the stochastic nature of the underlying dynamics, see, e.g., [5], and references therein.”
A deep learning based stock trading model with 2-D CNN trend detection (2017, methodology): “[8] have examined differences between Convolutional Neural Network, Multi-layer Perceptron(MLP) and Long Short-Term Memory(LSTM).”
A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021, methodology): “To reduce overfitting, the establishment of a one-dimensional input to predict only be achieved by the CNN framework in [32].”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “Periso & Honchar (Di Persio & Honchar, 2016) used a CNN which took a one-dimensional input for making prediction only based on the history of closing prices while ignoring other possible variables like technical indicators.”
Bankruptcy prediction using imaged financial ratios and convolutional neural networks (2019, background): “ber of applications of deep learning to financial analysis is extremely limited except for several reports on the prediction of stock price fluctuations (Yoshihara, Fujikawa, Seki, and Uehara, 2014; Aggarwal and Aggarwal, 2016; Persio and Honchar, 2017; Chong, Han, and Park, 2017; Bao, Yue, and Rao,”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “Unlike CNNs which are less widely applied in ﬁnancial markets, the LSTM has been popular in recent years, [42, 28, 43, 44, 45, 46, 47, 20] all utilising LSTMs to analyse ﬁnancial data.”
Evaluation of bidirectional LSTM for short-and long-term stock market prediction (2018, background): “[16] explored the effectiveness and efficiency of applying LSTM network to financial time series prediction.”
Artificial Intelligence and Marketing: Pitfalls and Opportunities (2020, background): “Applications of MLP include stock price prediction (Di Persio & Honchar, 2016), customer churn prediction (Ismail, Awang, Rahman, & Makhtar, 2015), credit scoring prediction (Zhao et al., 2015), and evaluating customer loyalty (Ansari & Riasi, 2016).”
Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations (2017, background): “Generally, to handle non-linearities in financial time series, Neural Networks (NN) [23, 24, 25] and Support Vector Machines (SVM) [26, 27] have been utilized [2].”