Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm

Bazrkar, Mohammad Javad; Hosseini, Seyed Ali

doi:10.1007/s10614-022-10273-3

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…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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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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“…Notably, such endeavors can aid investors in devising astute investment frameworks, furnish market participants with mechanisms to preclude market-related risks, and equip policymakers with crucial benchmarks to ensure the smooth operation of the national economy [ 3 ]. Especially for investors, reducing errors in forecasting stocks can reduce investment risk and increase profitability [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing stock volatility prediction with the AO-GARCH-MIDAS model

Liu,

Choo,

Tunde

et al. 2024

PLoS ONE

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Research has substantiated that the presence of outliers in data usually introduces additional errors and biases, which typically leads to a degradation in the precision of volatility forecasts. However, correcting outliers can mitigate these adverse effects. This study corrects the additive outliers through a weighting method and let these corrected values to replace the original outliers. Then, the model parameters are re-estimated based on this new return series. This approach reduces the extent to which outliers distort volatility estimates, allowing the model to better adapt to market conditions and improving the accuracy of volatility forecasts. This study introduces this approach for the first time to generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) models, so as to establish an additional outliers corrected GARCH-MIDAS model (AO-GARCH-MIDAS). This pioneering approach marks a unique innovation. The research employs a diverse array of evaluation methods to validate the model’s robustness and consistently demonstrates its dependable performance. Findings unequivocally reveal the substantial influence of outliers on the model’s predictive capacity, with the AO-GARCH-MIDAS model exhibiting consistent superiority across all evaluation criteria. Additionally, while the GARCH model showcases stronger estimation capabilities compared to the GARCH-MIDAS model, the latter demonstrates heightened predictive prowess. Notably, regarding variable selection, the results underscore the greater predictive informational value inherent in realized volatility over other low-frequency factors.

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“…It was found that the optimized BRBES has better performance and better potential in stock price prediction [4]. Bazrkar et al proposed a support vector machine (SVM) trained on existing stock data and used the particle swarm optimization (PSO) algorithm to improve the prediction accuracy [7]. Studies have found that the prediction accuracy of the optimized SVM model is above 90% [7].…”

Section: Introductionmentioning

confidence: 99%

“…Bazrkar et al proposed a support vector machine (SVM) trained on existing stock data and used the particle swarm optimization (PSO) algorithm to improve the prediction accuracy [7]. Studies have found that the prediction accuracy of the optimized SVM model is above 90% [7]. At present, when using machine models for stock price prediction, researchers generally prefer to use SVM and pay more attention to technical indicators, while Harikrishnan et al found that the usage of hybrid indicators and Long-Short Term Memory (LSTM) model is becoming a new trend [8].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction based on Multiple Regression Models

Li¹

2023

HSET

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Over the past two years, global stock markets have gradually recovered and new investors have entered the market. While there are many factors affecting stock prices and the stock market is changing rapidly, the way to accurately predict stock prices has become the focus of investors. This paper will use the concept of machine learning to predict the stock prices of three listed companies based on three different regression models (i.e., OLS, Ridge and XGBoost). According to the analysis, the OLS model and the Ridge model are very accurate in predicting stock prices, especially in the low and middle price ranges. In contrast to these typical linear regression models, the XGBoost model is not as accurate in predicting stock prices and even has a significant prediction bias in the high price range. These results will enable subsequent research to make better choices when selecting models for forecasting, especially for data sets with different characteristics.

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Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm

Cited by 19 publications

References 22 publications

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

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

Enhancing stock volatility prediction with the AO-GARCH-MIDAS model

Stock Price Prediction based on Multiple Regression Models

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