Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction

Abstract

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 secs and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.

Incorporating causality in energy consumption forecasting using deep neural networks

“…Eachempati et al (2021) have also shown that the deep neural networks outperform the traditional methods of predicting the accounting disclosure, another type of financial behaviour that has gained considerable attraction from the deep learning applications (Almagtome, 2021). Another aspect of risk behaviour that was detected using such techniques is the detection of fraudulent reviews for online marketing websites (Kumar et al, 2022a(Kumar et al, , 2022b. Kumar et al, (2022aKumar et al, ( , 2022b examined the different pre-processing and feature engineering techniques to extract both reviews and review-centric features and showed that unifying these features in ML classifiers resulted in better detection of fraudulent reviews than the contemporary methods.…”

“…Another aspect of risk behaviour that was detected using such techniques is the detection of fraudulent reviews for online marketing websites (Kumar et al, 2022a(Kumar et al, , 2022b. Kumar et al, (2022aKumar et al, ( , 2022b examined the different pre-processing and feature engineering techniques to extract both reviews and review-centric features and showed that unifying these features in ML classifiers resulted in better detection of fraudulent reviews than the contemporary methods. Furthermore, using deep learning techniques and text mining Huang et al (2022) provided a more accurate estimate of financial distress for beneficiaries and investors than classical machine learning algorithms.…”

Adapting deep learning models between regional markets

“…In keeping with the approach taken by Ghoshal and Robert [17], the input datasets were then batched into tensors of shape (20,4). These tensors create a 20-day historical window for each input vector consisting of the Open, High, Low and Close prices of each day.…”

“…Finally, each (20,4) tensor generated (following Ghoshal and Roberts [17]) was individually normalised to ensure the visual appearance of the normalised candlestick is identical to the unscaled candlestick. The (20,4) input data tensors were stacked into one of either the training, validation or testing datasets, based on date, and consistent with the approach of Ghoshal and Roberts [17]. The resultant tensors had dimensionality (# samples in data split, 20, 4).…”

“…Motivation for such research is also driven by the ongoing need of professionals to use the latest tools when investing in financial markets. As a result of the industrywide push to identify investment opportunities more accurately, there is a continuing drive towards the adoption of predictive algorithms such as neural networks, which have been shown to obtain significant results in several fields, including computer vision, natural language processing, health analytics, engineering, and game-playing [1][2][3][4][5][6][7][8][9]. The attraction to such investment pursuits has driven the value of the world's stock markets to the point that it is well-known that these markets comprise a substantial quantity of global wealth.…”