A prediction model of stock market trading actions using generative adversarial network and piecewise linear representation approaches

Abstract

Supervised learning applied stock prediction tasks and obtained satisfactory performance. The trading strategies are very complex and diverse but supervised learning is only learned and fitted by gold standard trading strategies. Supervised learning approaches often have over-fitting problems. To learn distribution of gold standard answers, the generative adversarial network (GAN) models can generate extra similar samples to improve performance. Therefore, the paper proposes a generative GAN-based frameworks with the piecewise linear representation (PLR) approach to learn three trading actions, namely buying, selling, and holding. The proposed framework consists of two parts: first, PLR approach uses to detect historical prices to form trading sequences with three actions, PLR can provide a guided trading strategy to discriminator of GAN. Second, the generator of GAN is used to generate/predict daily trading actions, and the discriminator is used to detect the real/fake trading actions from the PLR/generator of GAN. Experimental results indicate that the proposed GAN-based frameworks outperform the long short-term memory network.

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

“…Hybrid DL approaches frequently combine DL techniques with traditional methods [71][72][73][74][75] or DL architectures with each other, such as CNN-LSTM, LSTM or BiLSTM with attention mechanisms (AMs), transformer models, and graph convolutional neural network (GraphCNN). [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90] These hybrid DL models prove to be efficient in identifying complex patterns and relationships in data due to the high capacity and adaptability of DL architectures, especially in applications like SPF. Chandar 71 proposed a new method for stock trading by combining technical indicators and CNNs, termed TI-CNN.…”

“…This synthetic data can help augment the training data, allowing models to generalize better to unseen data and potentially leading to more accurate forecasts. The Wu et al 87 introduced a novel framework that combines GAN with piecewise linear representation for predicting stock market trading actions such as buying, selling, and holding. Staffini 88 proposed a novel approach to predicting stock prices using a deep convolutional GAN (DCGAN).…”

A Generative Model for Deep Fake Augmentation of Phonocardiogram and Electrocardiogram Signals Using Lsgan and Cycle Gan

“…The Generative Adversarial Network (GAN) is a widely utilized form of data augmentation model that is employed for the creation of time series data as well as visualizations [10]. Various fields such as health care [22], stock market predictions [25], image segmentation [2], text classification [5] etc., have incorporated the utilisation of GANs.…”