Forecasting a Stock Trend Using Genetic Algorithm and Random Forest

Abraham, Rebecca; Samad, Mahmoud El; Bakhach, Amer; El-Chaarani, Hani; Sardouk, Ahmad; Nemar, Sam El; Jaber, Dalia

doi:10.3390/jrfm15050188

Cited by 33 publications

(19 citation statements)

References 35 publications

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“…Usually, the Gini index (Gini impurity) is used to judge. Gini index (Gini impurity): indicates that in the sample e probability that a randomly selected sample in the set is wrongly classified, so the smaller the Gini index, the smaller the probability that the selected sample in the set is wrongly classified, that is to say, the higher the purity of the set, the more impure the set is, the more at is, the more impure the split leaf node is [17,18]. Its mathematical formula is shown as follows:…”

Section: Introduction To Random Forest Algorithmmentioning

confidence: 99%

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Gao

Ding

2022

Security and Communication Networks

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The traditional marketing model can no longer meet the needs of users and can not add more benefits to the enterprise, and digital marketing came into being. At present, most of the marketing focus of various enterprises is still mainly on products, and the reflection arc to market changes is long. Therefore, the formulation of marketing activities should always pay attention to changes in user needs and combine corresponding activity planning, product planning, brand building, etc., according to Changes in the target market adjust the content of marketing activities and products in real-time and, at the same time, pay attention to user feedback on products in order to iteratively update products in time, improve product market competitiveness, and optimize the user experience. In this paper, through the study and research of the traditional random forest method and some data processing algorithms, the feature selection and class imbalance problems of random forest are improved, respectively. Through the study of feature selection methods, we can maintain a balance between feature strength and relevance during feature selection and improve the final model classification effect. And through the research and experiment of the imbalanced data classification problem and the random forest algorithm, the method of the random forest model to deal with the imbalanced problem has been improved. After experimental calculation and analysis, it is found that for the effect of the minimum number of samples required for node splitting with different numbers, the best results are obtained when 2 samples are taken as the minimum number of samples required for node splitting, and the average value of the F1 evaluation is 0.1038; for different specifications, the effect of the random forest is the best using the Gini index, and the average value of its F1 evaluation is 0.1033; for the effect analysis of random forests with different numbers of trees, 7 to 10 decision trees are the best, and the F1 evaluation is the best. The average is 0.10175.

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Section: Introduction To Random Forest Algorithmmentioning

confidence: 99%

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Gao

Ding

2022

Security and Communication Networks

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“…Training and optimizing ML models can be computationally intensive, demanding substantial resources and time, especially for large datasets. 45,49 DL approaches. DL models have demonstrated their ability to predict stock prices with high accuracy and often outperform traditional models.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Thus, ML approaches can be used to build highperformance SPF systems without expert knowledge. The traditional ML algorithms, such as ANNs, 11,30,31 k-nearest neighbors (KNN), 32,33 support vector machine (SVM), [34][35][36][37][38][39][40] ensemble models, [41][42][43][44][45][46][47] and BN, 48,49 have been successfully and widely used in SPF systems. Table 2 presents articles on SPF based on ML approaches.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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“…Zhu (2020) used the XGBoost and RF models to predict the monthly returns of 300 stocks listed in the stock exchanges of Shanghai and Shenzhen and reached 78% of accuracy for the former and 72% for the latter. Abraham et al (2022)based on the Genetic Algorithm (GA proposed a combined study of two models: one with the selection of features using a genetic algorithm, and another of ranking using RF. The authors reached 80% of accuracy in the prediction of stock trends listed in the S&P 500 and the CAC40.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

Silva¹,

Montebello

Silva

2023

regea

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Objective: based on a systematic approach using machine learning, this research aims to propose a model of selection and allocation of assets that allows for building profitable and safe portfolios, even in times of insecurity and low predictability.Methodology: we used the machine learning algorithm called random forest to associate the independent variables with a dependent one and learn the probability of positive returns in the month following the data collection. According to the probabilities, the stocks were allocated into long, short, or non-allocated portfolios. Finally, we allocated a share of gold, which is a protection asset much used in times of crisis and uncertainty.Results and contributions: the study reached its goal and demonstrated being possible to build profitable and safe investment portfolios, even in times of greater uncertainty and volatility, as in 2020 due to the Covid-19 pandemic. We found that the model is effective in moments of crisis and also of greater predictability, as in the period from 2016 to 2019 when the stock exchange has an uptrend.Relevance: the relevance of this study points to an unprecedented historical context in Brazil, where uncertainties regarding both the local and world economy have demanded advanced studies of prediction to minimize risks and contribute to results for investors. In addition, we highlight that following a short period of low Selic (2019 to 2021), the Central Bank increased the rate again, raising the interest more in profitable and safer assets than the investment in stocks.

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Forecasting a Stock Trend Using Genetic Algorithm and Random Forest

Cited by 33 publications

References 35 publications

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

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