Stock price prediction methodology using random forest algorithm and support vector machine

Illa, Pavan Kumar; Parvathala, Balakesavareddy; Sharma, Anand

doi:10.1016/j.matpr.2021.10.460

Cited by 22 publications

(9 citation statements)

References 7 publications

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“…Illa et al 27 used random forest and SVM. They used these procedures to determine whether the cost of a stock will be higher than its cost on a given day to make profitable trading strategies.…”

Section: Machine Learning Basedmentioning

confidence: 99%

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RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

et al. 2023

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.Machine learning models are nowadays becoming ubiquitous in algorithmic trading and investment management. These models are mostly used in the pre-trade analysis phase to determine the buy or sell decisions using various machine learning techniques. We aim to implement a machine learning-driven approach using various technical indicators to predict stock market prices and then accordingly make a decision about buying or selling. First, an effective trading strategy is discussed that selects the potentially profitable stocks, and then the technical indicators such as simple moving average (SMA), exponential moving average (EMA), relative strength index (RSI), and moving average convergence divergence (MACD) are calculated for those potentially profitable stocks. Then supervised machine learning algorithms such as multiple linear regression, support vector machine regression, and decision tree regression are applied, where the close price of the stock is predicted using technical indicators for the next day, and based on that buy or sell signals are generated. The model is then tested on 12 different SNP500 stocks, one for every month in 2018, with the mean squared error (MSE) varying between 30.33 and 48.16 and the root MSE varying between 5.51 and 6.93, where the error is calculated on the difference in the number of days when the stock price actually increases and the predicted number of days for various models.

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Section: Machine Learning Basedmentioning

confidence: 99%

“…The SVM is better than linear regression Stock price prediction methodology using random forest algorithm and support vector machine 27 Random forest and SVM…”

Section: Svrmentioning

confidence: 99%

RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

et al. 2023

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“…Based on these similar works, SVM has shown good performance in the research by [14] and [18] with the accuracies of more than 90%. This research has chosen SVM due to its capability and also good performance in solving various other classification problems [20][21][22]. It is expected that SVM could also produce good results in this sentiment classification problem.…”

Section: B Similar Workmentioning

confidence: 99%

Detection of Harassment Toward Women in Twitter During Pandemic Based on Machine Learning

Mustapha,

Sabri,

Bakar

et al. 2024

IJACSA

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Harassment is an offensive behavior, intimidating and could cause discomfort to the victims. In some cases, the harassments could lead to a traumatic experience to the vulnerable victims. Currently, the harassments towards women in social media have become more daring and are rising. The increasing number of the social media users since the Covid-19 pandemic in 2020 might be one of the factor. Due to the problem, this research aims to assist in detecting the harassment sentiments toward women in Twitter. The sentiment analysis is based on a machine learning approach and Support Vector Machine (SVM) has been chosen due its acceptable performance in sentiment classification. The objective of the research is to explore the capability of SVM in the detection of harassments toward women in Twitter. The research methodology covers the data collection using Tweepy, data preprocessing, data labelling using TextBlob, feature extraction using TF-IDF vectorizer and dataset splitting using the Hold-Out method. The algorithm was evaluated using the Confusion Matrix and the ROC analysis. The algorithm was integrated with the Graphical User Interface (GUI) using Streamlit for ease of use. The implementation of the SVM algorithm in detecting the harassments toward women was successful and reliable as it achieved good performance, with 81% accuracy. The recommendations for the SVM model improvement is to train the dataset of other languages and to collect the Twitter data regularly. The performance of SVM would also be compared with other machine learning algorithms for further validations.

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“…In this particular study, Random Forest is brought into play to grapple with intricate data patterns and gauge the significance of feature attributes. A detailed dissection of Random Forest's ensemble nature, the intricacies of hyperparameter tuning for optimization, and the import of feature importance scores in fathoming the driving forces underpinning PayPal stock price prognostications is conducted [8].…”

Section: Random Forestmentioning

confidence: 99%

Stock price prediction of PayPal by Linear Regression, SVM, Random Forest and LSTM

Zhang

2024

ACE

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This research paper undertakes an extensive and detailed examination of four distinct machine learning models, specifically Linear Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). The stock values of PayPal are forecast employing these techniques, and Python serves as the fundamental tool in facilitating a comprehensive and thorough evaluation of their efficacy in predicting trends in the financial market. In the realm of methodology, this study encompasses a multifaceted approach. Python forms the cornerstone for data analysis, model development, and testing. The data collection process encompasses historical stock price data for PayPal, alongside an array of relevant economic indicators. The core of the study lies in the comparative analysis of the four machine learning models. Each model is rigorously tested against historical data, allowing for a nuanced understanding of their strengths and weaknesses. Linear Regression, remarkably, emerges as the standout performer in terms of predictive accuracy and consistency. This firmly establishes Linear Regression as the optimal choice for forecasting stock prices. The significance distinction of this research extends beyond its findings. It advances the discipline of financial forecasting by illuminating the comparative effectiveness of different models, offering valuable insights for guiding future research initiatives within this domain. The integration of advanced methodologies and the clear-cut conclusion regarding Linear Regression's superiority underscore the pivotal role of this study in enhancing the precision of financial market trend forecasting.

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Stock price prediction methodology using random forest algorithm and support vector machine

Cited by 22 publications

References 7 publications

RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

Detection of Harassment Toward Women in Twitter During Pandemic Based on Machine Learning

Stock price prediction of PayPal by Linear Regression, SVM, Random Forest and LSTM

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