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An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods

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Abstract

At the beginning of 2020, the COVID-19 pandemic caused a sharp decline in equity market indices, which remained stagnant for a considerable period. This resulted in significant losses for many investors. Despite extensive research on stock market prediction and the development of various effective models, there has been no specific effort to create a stable model during a financial crisis. Several studies have been conducted to forecast stock market trends and prices using advanced techniques like machine learning, deep learning, generative adversarial networks, and reinforcement learning. However, none of the existing forecasting models address the issue of market crashes, leading to substantial losses. We propose a GAF-EWGAN, a stacking ensemble model that combines enhanced WGANs with Gramian Angular Fields. This model demonstrates a high level of resilience during stock market crashes, effectively preventing investors from experiencing losses and generating significant profits. The GAF-EWGAN model achieved an average annual return of 16.49% across 20 selected stocks. Financial indicators indicate its reliability for real-world transactions.

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Data availability

The source code is available at gitlab.com/Alireza. Ghasemieh/stock_advisor.

The Stock market data is collected from Nasdaq Stock Exchange and New York Stock Exchange through Alphavantage API for 20 stocks over the last 21 years.

Abbreviations

ApT:

Annual Return

ARIMA:

Autoregressive integrated moving average

A-RNN:

Attention based RNN

BA:

Batting Average

BaH:

Buy and Hold

BB:

Bollinger Bands

CCI:

Commodity Channel Index

CNN:

Convolutional Neural Network

C-RNN:

Convolutional deep-Recurrent Neural Network

DL:

Deep Learning

DPS:

Dividend Per Share

DWNN:

Deep and Wide Neural Networks

EWGAN:

Ensamble WGAN

FFNN:

Feedforward neural network

GAF:

Gramian Angular Fields

GAN:

Generative Adversarial Network

GAN-HPA:

Generative Adversarial Network-based Hybrid Prediction Algorithm

GARCH:

Generalized autoregressive conditional heteroskedasticity

GEW:

Geneva Emotion Wheel

GRU:

Gated recurrent units

LDA:

Latent Dirichlet Allocation

LSGAN:

Least-squares GAN

LSTM:

Long Short-Term Memory

MA:

Moving Average

ML:

Machine Learning

MRF:

Markov Random Fields

NTN:

Neural tensor network

PSR:

Phase-space reconstruction

RF:

Random forests

RNN:

Recurrent neural network

SVM:

Support vector machines

SVR:

Support vector regressions

TI:

Technical Indicator

VAE:

Variational Auto Encoder

WGAN:

Wasserstein GAN

WLR:

Win-Loss Ratio

ATR :

Average True Range

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This research is funded by the Toronto Metropolitan University, Faculty of Engineering and Architectural Science.

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  1. Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada

    Alireza Ghasemieh & Rasha Kashef

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  1. Alireza Ghasemieh
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Ghasemieh, A., Kashef, R. An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods. Appl Intell 53, 28479–28500 (2023). https://doi.org/10.1007/s10489-023-05016-2

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