Ultimate Prediction of Stock Market Price Movement

Nabi, Rebwar M.; Saeed, Soran; Harron, Habibolah Bin; Fujita, Hamido

doi:10.3844/jcssp.2019.1795.1808

Cited by 5 publications

(16 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al ( 2022 ) Technical indicators Fundamental indica- tors PCC Broad learning system 4 stocks from Shanghai Stock Exchange 8. Nabi et al ( 2019 ) Basic features 9 different methods 15 different classifiers 10 stocks from NASDAQ 9. Yuan et al ( 2020 ) Technical indicators, Fundamental indica-tors RFE, RF SVM RF ANN Chinese A-share stocks 10.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…In Kumar et al ( 2016 ), linear correlation (LC) and rank correlation (RC) methods were deployed together with a proximal support vector machine (PSVM) model as the LC-PSVM and RC-PSVM to obtain the optimal feature subset from an original set of 55 tech- nical indicators for 12 different stock indices. Two studies, (Alsubaie et al 2019 ) and (Nabi et al 2019 ), also used an LC method with different classifiers to predict the direction of stock markets.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

“…The study in Ampomah et al ( 2021 ) also applied PCA together with feature scaling techniques, namely, standardization and min–max scaling, to find the optimal feature set from 40 technical indicators to predict the direction of seven stocks from the NYSE, NASDAQ, and NSE markets. Another study (Nabi et al 2019 ) applied nine different feature selection algorithms combined with 15 different classifiers to predict the monthly direction of 10 companies from NAS- DAQ. As a simple and efficient algorithm, PCA was found to be the best feature extraction algorithm, providing the highest accuracy for all combinations with ML models and different stocks according to the experiments.…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

“…Subsequently, we excluded 83 articles that did not mention the feature selection methods applied. Therefore, we obtained 32 relevant papers (27 in journals (Alsubaie et al 2019 ; Aloraini 2015 ; Li et al 2022 ; Kumar et al 2016 , 2021b ; Nabi et al 2019 ; Yuan et al 2020 ; Shen and Shafiq 2020 ; Haq et al 2021 ; Sun et al 2019 ; Chen and Hao 2017 , 2020 ; Gunduz et al 2017 ; Siddique and Panda 2019 ; Singh and Khushi 2021 ; Ampomah et al 2020 , 2021 ; Qolipour et al 2021 ; Das et al 2019 ; Tang et al 2018 ; Chong et al 2017 ; Bhanja and Das 2022 ; Xie and Yu 2021 ; Dami and Esterabi 2021 ; Gunduz 2021 ; Barak et al 2017 ; Farahani and Hajiagha 2021 ), and 5 in conference proceedings (Botunac et al 2020 ; Cai et al 2012 ; Labiad et al 2016 ; Rana et al 2019 ; Iacomin 2015 ). Figure 2 illustrates the article selection method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Survey of feature selection and extraction techniques for stock market prediction

2023

View full text Add to dashboard Cite

In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

show abstract

Section: Analysis and Discussionmentioning

confidence: 99%

Section: Feature Selection Methodsmentioning

confidence: 99%

Section: Feature Extraction Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Survey of feature selection and extraction techniques for stock market prediction

2023

View full text Add to dashboard Cite

show abstract

“…Feature engineering for search advertising recognition was investigated by researchers in [27]. Researchers in [28] proposed feature engineering for stock prediction but only considered the binary classification. They found significant improvement in prediction performance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

Nabi

Saeed

Harron

2020

KJAR

Self Cite

View full text Add to dashboard Cite

The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature.

show abstract

Forecasting relative returns for S&P 500 stocks using machine learning

Htun,

Biehl,

Petkov

2024

Financ Innov

View full text Add to dashboard Cite

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes in the stock market overall. Nonetheless, machine learning (ML) techniques that use historical data have been applied to make such predictions. Previous studies focused on a small number of stocks and claimed success with limited statistical confidence. In this study, we construct feature vectors composed of multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days. We apply this approach to all S&P 500 companies for the period 2017–2022. We assess performance using accuracy, precision, and recall and compare our results with a random choice strategy. We observe that the LSTM classifier outperforms RF and SVM, and the data-driven ML methods outperform the random choice classifier (p = 8.46e−17 for accuracy of LSTM). Thus, we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.

show abstract

Ultimate Prediction of Stock Market Price Movement

Cited by 5 publications

References 12 publications

Survey of feature selection and extraction techniques for stock market prediction

Survey of feature selection and extraction techniques for stock market prediction

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

Forecasting relative returns for S&P 500 stocks using machine learning

Contact Info

Product

Resources

About