Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA) for stock market prices prediction

Polamuri, Subba Rao; Srinivas, Kudipudi; Mohan, A. Krishna

doi:10.1016/j.jksuci.2021.07.001

Cited by 29 publications

(15 citation statements)

References 21 publications

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“…The MACD line is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA. The signal line is a nineperiod EMA of the MACD line [3]…”

Section: Proposed Methodsmentioning

confidence: 99%

Effective Stock Prediction Model Using MACD Method

Hamzah

Winardi²

2022

ijicom

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Stock market predictions help investors to optimize benefits in the financial markets. Various papers have proposed different techniques in stock market forecasting, but no model can provide accurate predictions. In this study, we discuss how to predict stock prices using a MACD (Moving Average Convergence/Divergence Oscillator) method. We collect the dataset, preprocess it, extract features, evaluate the model, and then deploy the MACD method to develop a stock price prediction model. In this study, we collect several features, including date, open, high, low, close, and volume, to conduct the training and testing process. The results of the experiments reveal good accuracy and a low error rate. As a result, it has the potential to be a promising solution for dealing with accurate and dynamic prices. Based on the experimental result, our proposed model can obtain a transaction profit rate of 40.00% and an average profit per transaction of 1.42%.

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“…The MACD line is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA. The signal line is a nineperiod EMA of the MACD line [3]…”

Section: Proposed Methodsmentioning

confidence: 99%

Effective Stock Prediction Model Using MACD Method

Hamzah

Winardi²

2022

ijicom

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“…They combine the Multi-Model-based Hybrid Prediction Algorithm with the GAN-based Hybrid Prediction Algorithm. Further, they obtained an improved model named Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm for stock market prices prediction (Polamuri et al, 2021 ). The learning process approach based on a predictive probabilistic neural network corresponds to a different way of using training in the Conditional Generative Adversarial Networks (cGAN) as a predictive model in portfolio optimization, stock market prediction and trade execution (Zhang et al, 2019 ; Lee and Seok, 2021 ).…”

Section: Post-hoc Explanations Using Xai To Build Trust For ...mentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

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“…Mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to assess the performance of the proposed LSTM-and BE-LSTM-based model (Rezaei et al 2021;Polamuri et al 2021). The following is the formula for these metrics:…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Deep neural networks (DNN), convolutional neural networks (CNN), and long short-term memory networks (LSTM) are three deep neural models that have been efficiently used in the literature to predict stock prices (Ananthi and Vijayakumar 2021;Chen et al 2021;Dash et al 2019;El-Chaarani 2019). Among these aforementioned methods, LSTM has been employed in deep learning models for stock price prediction, and it has produced better results (Liu et al 2021;Nelson et al 2017;Polamuri et al 2021;Rezaei et al 2021;Shen and Shafiq 2020). Although these approaches are acknowledged to be highly useful in data investigation, accuracy in prediction becomes challenging when the time series data is highly unstable and stochastic.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model

Jafar,

Akhtar,

El-Chaarani

et al. 2023

JRFM

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Predicting trends in the stock market is becoming complex and uncertain. In response, various artificial intelligence solutions have emerged. A significant solution for predicting the trends of a stock’s volatile and chaotic nature is drawn from deep learning. The present study’s objective is to compare and predict the closing price of the NIFTY 50 index through two significant deep learning methods—long short-term memory (LSTM) and backward elimination LSTM (BE-LSTM)—using 15 years’ worth of per day data obtained from Bloomberg. This study has considered the variables of date, high, open, low, close volume, as well as the 14-period relative strength index (RSI), to predict the closing price. The results of the comparative study show that backward elimination LSTM performs better than the LSTM model for predicting the NIFTY 50 index price for the next 30 days, with an accuracy of 95%. In conclusion, the proposed model has significantly improved the prediction of the NIFTY 50 index price.

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Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA) for stock market prices prediction

Cited by 29 publications

References 21 publications

Effective Stock Prediction Model Using MACD Method

Effective Stock Prediction Model Using MACD Method

Enhancing portfolio management using artificial intelligence: literature review

Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model

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