Stock Market Trend Prediction Model for the Egyptian Stock Market Using Neural Networks and Fuzzy Logic

ElAal, Maha Mahmoud Abd; Selim, Gamal Eldin I.; Fakhr, W.

doi:10.1007/978-3-642-24553-4_13

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…The result showed accuracies of 61.5%, 61.9%, and 60.7% for the proposed models, respectively. EAal et al [68] proposed a trend prediction system based on Artificial Neural Networks (ANN) and fuzzy logic rules. The model is trained over the Egyptian stock market for 3 years.…”

Section: Silva Et Al Inmentioning

confidence: 99%

Robust Deep Learning Models for Predicting the Trend of Stock Market Prices During Market Crash Periods

Ghasemieh

2024

Preprint

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Investing in the stock market involves both opportunities and risks. However, the stock market may experience some fluctuations, which most investors regard as serious threats -- particularly when prices fall sharply due to external circumstances. At the beginning of 2020, as a consequence of the COVID-19 pandemic, equity market indices fell sharply. During this time, many investors suffered significant losses. Despite substantial research in stock market forecasting and the development of various efficient models, existing methods fall short in proposing sustainable and stable models during a financial crisis. To address this research gap, we propose two novel deep learning models: a Convolutional Neural Network (CNN)-based ensemble model, namely GAFECNN-Stacking and a stacking ensemble of enhanced WGANs-based model, namely GAFEWGAN, that maintains a high level of resilience to the stock market crash. The GAF-ECNN Stacking and GAF-EWGAN models achieved an average of 13.26% and 16.49% annual returns over 20 selected stocks.

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Section: Silva Et Al Inmentioning

confidence: 99%

Robust Deep Learning Models for Predicting the Trend of Stock Market Prices During Market Crash Periods

Ghasemieh

2024

Preprint

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“…Time series are obtained by evaluating the relationships obtained from the Bayesian graph so that when the graph becomes stable, the stock market has a stable trend. ElAal et al [15] have predicted the trend of the Egyptian market using neural networks and fuzzy logic. In this method, neural network functions such as classification were used, which were the indicators of the analysis of their input features.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a combined Markov-bayesian model in order to predict stock prices in the stock exchange

Hajiaghajani

2023

IJIMES

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Investing in shares offered on the stock exchange is one of the most profitable options in the capital market. The stock market has a non-linear and chaotic system that is influenced by political, economic, and psychological conditions. Forecasting time series, such as stock price forecasting, is one of the most important problems in the field of economics and finance because the data is unstable and has many variables that are influenced by many factors. There are many ways to predict stock prices. Non-linear intelligent systems such as artificial neural networks, fuzzy neural networks, and genetic algorithms can be used to predict stock prices. In this research, a hybrid system based on Bayesian networks and the Markov model is proposed to predict the daily trend of the stock market. Bayesian networks are used to specify relationships between variables in forecasting. Finally, the Markov model is used to predict the market trend in the sets extracted from the Bayesian network. The evaluation criteria in the proposed system show the high efficiency of this method.

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“…To take full advantage of the strengths of advanced machine learning techniques to produce broader impacts, effective practical implementations of predictive systems must incorporate the use of innovative technologies. Stock prices prediction can be transferred to two types of problems: (1) decision making or classification problems for price trend prediction, such as fuzzy rule-based systems (ElAal et al, 2012), neural networks (ElAal et al, 2012, Lertyingyod & Benjamas, 2017, and random forests with imbalance learning (Zhang et al, 2018), and (2) time series prediction (TSP) problems for price value prediction. Various machine learning techniques have been applied for TSP problems (Jadhav et al, 2015,He & Qin, 2010.…”

Section: Machine Learning Techniques For Computational Financementioning

confidence: 99%

Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

Zheng

2021

Expert Systems with Applications

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Stock Market Trend Prediction Model for the Egyptian Stock Market Using Neural Networks and Fuzzy Logic

Cited by 8 publications

References 3 publications

Robust Deep Learning Models for Predicting the Trend of Stock Market Prices During Market Crash Periods

Robust Deep Learning Models for Predicting the Trend of Stock Market Prices During Market Crash Periods

Designing a combined Markov-bayesian model in order to predict stock prices in the stock exchange

Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

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