Moroccan Stock Market Prediction Using LSTM Model on a Daily Data

Ifleh, Abdelhadi; Kabbouri, Mounime El

doi:10.1007/978-981-16-2248-9_2

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…There is a wide range of performance measures to judge the precision of the prediction model; in our work we use those measures (Ifleh & El Kabbouri, 2021):…”

Section: Forecasting Performance Measuresmentioning

confidence: 99%

Stock price indices prediction combining deep learning algorithms and selected technical indicators based on correlation

Ifleh,

El Kabbouri

2023

AGJSR

Self Cite

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PurposeThe prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.Design/methodology/approachThe methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.FindingsThe experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.Originality/valueThis article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

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“…There is a wide range of performance measures to judge the precision of the prediction model; in our work we use those measures (Ifleh & El Kabbouri, 2021):…”

Section: Forecasting Performance Measuresmentioning

confidence: 99%

Stock price indices prediction combining deep learning algorithms and selected technical indicators based on correlation

Ifleh,

El Kabbouri

2023

AGJSR

Self Cite

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“…To address this problem, the LSTM model introduces a new cell known as a memory cell "C t," which can hold information from the deepest levels. Every memory cell has three gates: an entrance gate "I t," a forget gate "f t" and an output gate "O t" [33].…”

Section: Long Short-term Memory Lstmmentioning

confidence: 99%

Concrete Compressive Strength Prediction Using Machine Learning Algorithms

Turkey

Beddu

Ahmed

et al. 2022

Preprint

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The most widely utilized construction material is concrete. Concrete's physical qualities differ depending on the kind. In this paper, we predicted the compressive strength of four types of lightweight aggregate geopolymer concretes (LWAGC), namely, lightweigh expanded clay Leca, recycled foam masonry aggregate RFA, Porcelanite aggregate PA and recycled brick aggregate RBA. For predictions, we used seven models, specifically, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Decision Tree (DT), Random Forest (RF), Support Vector Regressor (SVR) and Linear Regression (LR). For evaluation, we employed six metrics, Root Mean Square Error (RMSE), Mean Squared Logarithmic Error (MSLE), Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Logarithmic Error (RMSLE) and Coefficient of Determination R². The results of predictions demonstrate that RF offers high accuracy for Leca type (MSE= 2,6697; RMSE= 1,6339; MSLE= 0,0072; RMSLE= 0,0851; MAE= 1,3434; R²=0,8687), Porcelanite type (MSE= 2,9650; RMSE= 1,7219; MSLE= 0,0065; RMSLE= 0,0805; MAE= 1,5693; R²=0,8437) and RFA type (MSE= 1,7028; RMSE= 1,3049; MSLE= 0,0059; RMSLE= 0,0768; MAE= 1,1300; R²=0,8099) because the predictions are closer to the real values, and DT offers better predictions than other models for RBA type (MSE= 3,3069; RMSE= 1,8185; MSLE= 0,0066; RMSLE= 0,0812; MAE= 1,5610; R²=0,8764). İn order to test the models in predicting new value we gave them a 675° as new temperature and we found that that LR is more accurate than other models in predicting the CS of Leca, RBA and Porcelanite types. However, CNN outperformed other models in predicting RFA type.

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“…But our research stands out because it combines feature selection and regression algorithms to enhance stock price prediction. The study [16] combines technical indicators (TIs) with the LSTM model to predict stock prices, outperforming the random forest model. While this approach is promising, it does not explicitly delve into a comprehensive feature selection method or hybrid composition of algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hybrid machine learning for stock price prediction in the Moroccan banking sector

Itri,

Mohamed,

Omar

et al. 2024

IJECE

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Analyzing historical stock market data using machine-learning techniques is crucial for data scientists and researchers to optimize stock price prediction models. This study uses machine learning regression algorithms and feature selection methods to optimize a simulated stock price prediction model using real historical data from Bank of Africa, a Moroccan bank. The approach compares multiple supervised regression algorithms, such as linear regression, extreme gradient boosting, ordinary least squared, random forest regressor, a linear least-squares L2-regularized, epsilon-support vector regression, and linear support vector regression. Each of these algorithms is associated with different feature selection algorithms to improve the performance of the prediction model. The analysis results revealed that hybridizing algorithms between the highest score percentiles, univariate linear regression, and linear support vector regression perform better according to the root mean squared error and R2-Score measures. This approach overcomes the problems associated with high-dimensional data by reducing the number of features and improving prediction accuracy.

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Moroccan Stock Market Prediction Using LSTM Model on a Daily Data

Cited by 5 publications

References 12 publications

Stock price indices prediction combining deep learning algorithms and selected technical indicators based on correlation

Stock price indices prediction combining deep learning algorithms and selected technical indicators based on correlation

Concrete Compressive Strength Prediction Using Machine Learning Algorithms

Hybrid machine learning for stock price prediction in the Moroccan banking sector

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