The Effect of Feature Selection on the Performance of Long Short-Therm Memory Neural Network in Stock Market Predictions

Botunac, Ive; Panjkota, Ante; Matetić, Maja

doi:10.2507/31st.daaam.proceedings.081

Cited by 3 publications

(20 citation statements)

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“…From Table 3, we can conclude that the correlation criteria, RF, PCA, and AE approaches are the most widely applied feature analysis techniques for various stock market predictions. For the datasets in Botunac et al (2020); Kumar et al 2016;Yuan et al 2020;Labiad et al 2016;Haq et al 2021), RF provides good performance in terms of high accuracy and low error values. Meanwhile, PCA provides satisfactory results in Nabi et al (2019); Shen and Shafiq 2020;Siddique and Panda 2019;Singh and Khushi 2021;Ampomah et al 2020;Qolipour et al 2021;Iacomin 2015;Ampomah et al 2021;Das et al 2019;Tang et al 2018).…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…These features include daily stock information (open, high, low, close, volume (OHLCV) data), technical and economic indicators, and financial news. In Botunac et al (2020), Tsai and Hsiao (2010), Ni et al (2011), the application of a feature selection method was found to produce more effective predictions than the use of prediction models alone. Therefore, various feature selection techniques that are applied in the stock market and their specific performance must be reviewed to further improve predictions.…”

Section: Methodsmentioning

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 2016Nabi et al 2019;Yuan et al 2020;Shen and Shafiq 2020;Haq et al 2021;Sun et al 2019;Hao 2017, 2020;Gunduz et al 2017;Siddique and Panda 2019;Singh and Khushi 2021;Ampomah et al 2020Ampomah et al , 2021Qolipour 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: Survey Methodsmentioning

confidence: 99%

“…The authors used the SVM-RFE method to retrieve the importance scores of all 60 features and then chose the top 80% of the features (i.e., 48 features) as input features for the SVM, RF, and ANN models to predict the direction of monthly stock returns. In Botunac et al (2020), RFE was proposed as a feature selection method to find the effective features from five basic features and nine technical indicators of various stocks for the LSTM fore-casting model. As RFE generated unclear scores for all features in the preliminary experiments, the authors also applied other feature importance methods, such as linear regression, decision tree, and RF regression.…”

Section: Recursive Feature Elimination (Rfe)mentioning

confidence: 99%

“…Furthermore, the results showed that the RF method can suggest a certain number of indicators that provide better prediction results than other feature selection methods. In Botunac et al (2020), RF was also utilized to determine the importance scores of 14 features to predict the closing prices of Apple, Microsoft, and Facebook. Another research (Yuan et al 2020) proposed RF as a feature selection method and a prediction model (RF-RF) to perform stock price trend prediction; the proposed approach achieved the best performance among all the integrated models in the study.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

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Survey of feature selection and extraction techniques for stock market prediction

2023

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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.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Survey Methodsmentioning

confidence: 99%

Section: Recursive Feature Elimination (Rfe)mentioning

confidence: 99%

Section: Random Forest (Rf)mentioning

confidence: 99%

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Survey of feature selection and extraction techniques for stock market prediction

2023

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Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Priyatno,

Ramadhan Sudirman,

Musridho

2024

IJECE

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Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.

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Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach

Botunac,

Bosna,

Matetić

2024

Information

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Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market.

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The Effect of Feature Selection on the Performance of Long Short-Therm Memory Neural Network in Stock Market Predictions

Cited by 3 publications

References 0 publications

Survey of feature selection and extraction techniques for stock market prediction

Survey of feature selection and extraction techniques for stock market prediction

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach

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