Machine learning approaches in stock market prediction: A systematic literature review

Mintarya, Latrisha N.; Halim, Jeta N.M.; Angie, Callista; Achmad, Said; Kurniawan, Aditya

doi:10.1016/j.procs.2022.12.115

Cited by 63 publications

(36 citation statements)

References 15 publications

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“…The efficacy of the ML methods cannot be denied for analyzing the patterns of the less complex and low dimensional datasets. The stock market predictions are more accurate and accessible with ML techniques [25] . For instance, authors in [17] used tree-based ensemble models for predicting three stock exchanges (NYSE, NASDAQ, NSE) and found Extra Tree (ET) as the best classifier.…”

Section: Related Workmentioning

confidence: 99%

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Latif,

Javaid,

Aslam

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Latif,

Javaid,

Aslam

et al. 2024

Heliyon

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“…9,10 While ARIMA has demonstrated its utility in capturing short-to medium-term price trends, it can be difficult to handle the complex dynamics and non-linear patterns often observed in stock markets. To address the shortcomings of conventional SPF systems based on ARIMA approaches, a learning-based approach using machine learning (ML) [11][12][13] and deep learning (DL) techniques was introduced. 14,15 The ML approaches have shown significant promise in understanding the complexities of financial markets, characterized by dynamic interactions among various elements that influence stock prices.…”

Section: Introductionmentioning

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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“…Clustering is a commonly used unsupervised learning method [1,2], widely used in intrusion detection, machine learning, image processing, data mining and other fields. Clustering tries to divide the samples in the data into several categories so that the same category.…”

Section: Introductionmentioning

confidence: 99%

K-means Algorithm Based on Flower Pollination Algorithm and Calinski-Harabasz Index

Aik,

Choon,

Abu

2023

J. Phys.: Conf. Ser.

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Aiming at the problems that the Flower Pollination (FP) algorithm is easy to fall into the local optimum, the searchability is weak, and the k-means algorithm is easily affected by the selection of the initial clustering centre, a k-means algorithm based on the FP algorithm is proposed. Six benchmark functions test the improved FP algorithm. The effectiveness of the k-means algorithm based on the improved FP algorithm was tested and verified with UCI machine learning and artificial datasets. The verification results showed that the improved FP algorithm improved based on ensuring a faster convergence speed. Compared with other algorithms, the performance of this algorithm has been significantly improved in all aspects.

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Machine learning approaches in stock market prediction: A systematic literature review

Cited by 63 publications

References 15 publications

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

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

K-means Algorithm Based on Flower Pollination Algorithm and Calinski-Harabasz Index

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