An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction

Dash, Rajashree; Samal, Sidharth; Dash, Rasmita; Rautray, Rasmita

doi:10.1016/j.asoc.2019.105784

Cited by 56 publications

(26 citation statements)

References 31 publications

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“…Future prediction of stock index price is critical for investors who plan to increase profit and researchers who wish to extract complex stock market data over time series data. Dash et al [171] used TOPSIS and CSA to predict stock index price movement.…”

Section: ) Stock Index Price Movement Predictionmentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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Section: ) Stock Index Price Movement Predictionmentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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“…Many economists and financial analysts have advocated for the presence of financial market nonlinearity and uncertainty [21]. For stock index price movement prediction, a Crow search-based weighted voting classifier ensemble with TOPSIS is suggested in [22]. Stock movements were predicted by looking at the causation between firms rather than the relevance between companies [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Stock movement prediction using hybrid normalization technique and artificial neural network

Kumari¹,

Swarnkar²

2021

IJATEE

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“…Accordingly, all the other results show our approach is performing better in all the datasets. Furthermore, another study proposed an ensemble approach for stock price prediction using historical data [42], which is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS uses crow search based weighted voting classifier ensemble.…”

Section: Gbm-wfe Approach Benchmarkingmentioning

confidence: 99%

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

Nabi

Saeed

Harron

2020

KJAR

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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.

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An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction

Cited by 56 publications

References 31 publications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Stock movement prediction using hybrid normalization technique and artificial neural network

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

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