Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market

Oukhouya, Hassan; El Himdi, Khalid

doi:10.3390/iocma2023-14409

Cited by 15 publications

(5 citation statements)

References 19 publications

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“…Using LSTM models in stock market forecasting enables the effective capture of long-term dependencies, resulting in precise predictions. The LSTM cell operates based on four equations, which govern its underlying principle [11]:…”

Section: Lstm Modelmentioning

confidence: 99%

“…Results showed that this approach outperformed the baseline ARIMA model regarding Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE) on a Forex dataset from 2008-2018. In a comparative conducted by Oukhouya and El Himdi [11], various ML methods, including XGBoost, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and LSTM, were evaluated for forecasting a daily closing price of the Morocco Stock Index 20 (MSI 20). The results demonstrated that the SVR and MLP models, optimized with Grid Search (GS), have outperformed other models and achieved outstanding accuracy in price prediction.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models

OUKHOUYA,

KADIRI,

EL HIMDI

et al. 2023

Stat., optim. inf. comput.

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Forecasting time series is crucial for financial research and decision-making in business. The nonlinearity of stock market prices profoundly impacts global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan, French, German, British, US, and Hong Kong markets, respectively. We compare the performance of machine learning models, including Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and the hybrid LSTM-XGBoost, and utilize the skforecast library for backtesting. Results show that the hybrid LSTM-XGBoost model, optimized using Grid Search (GS), outperforms other models, achieving high accuracy in forecasting daily prices. This contribution offers financial analysts and investors valuable insights, facilitating informed decision-making through precise forecasts of international stock prices.

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Section: Lstm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models

OUKHOUYA,

KADIRI,

EL HIMDI

et al. 2023

Stat., optim. inf. comput.

View full text Add to dashboard Cite

show abstract

“…In contrast, this work focuses on more comprehensive feature selection and hybrid algorithm composition. Oukhouya and El Himdi [15] explored the use of various machine learning methods like SVR, XGBoost, MLP, and LSTM to forecast daily prices of the MSI 20 index. But our research stands out because it combines feature selection and regression algorithms to enhance stock price prediction.…”

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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“…In stock market prediction, various time-series methodologies, including Long Short-Term Memory (LSTM)-based models, have been employed to develop predictive frameworks [5]. Although LSTM models have proven their worth across numerous sequence learning tasks, their global modeling approach, which relies on the entirety of the training data, may sometimes overlook subtle nuances within certain feature space areas.…”

Section: Introductionmentioning

confidence: 99%

Stock Market Prediction With Transductive Long Short-Term Memory and Social Media Sentiment Analysis

Peivandizadeh,

Hatami,

Nakhjavani

et al. 2024

IEEE Access

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In an era dominated by digital communication, the vast amounts of data generated from social media and financial markets present unique opportunities and challenges for forecasting stock market prices. This paper proposes an innovative approach that harnesses the power of social media sentiment analysis combined with stock market data to predict stock prices, directly addressing the critical challenges in this domain. A major challenge in sentiment analysis is the uneven distribution of data across different sentiment categories. Traditional models struggle to accurately identify fewer common sentiments (minority class) due to the overwhelming presence of more common sentiments (majority class). To tackle this, we introduce the Off-policy Proximal Policy Optimization (PPO) algorithm, specifically designed to handle class imbalance by adjusting the reward mechanism in the training phase, thus favoring the correct classification of minority class instances. Another challenge is effectively integrating the temporal dynamics of stock prices with sentiment analysis results. Our solution is implementing a Transductive Long Short-Term Memory (TLSTM) model that incorporates sentiment analysis findings with historical stock data. This model excels at recognizing temporal patterns and gives precedence to data points that are temporally closer to the prediction point, enhancing the prediction accuracy. Ablation studies confirm the effectiveness of the Off-policy PPO and TLSTM components on the overall model performance. The proposed approach advances the field of financial analytics by providing a more nuanced understanding of market dynamics but also offers actionable insights for investors and policymakers seeking to navigate the complexities of the stock market with greater precision and confidence.

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Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 15 publications

References 19 publications

Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models

Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models

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

Stock Market Prediction With Transductive Long Short-Term Memory and Social Media Sentiment Analysis

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