Predicting Market Performance with Hybrid Model

Usmani, Mehak; Ebrahim, Mansoor; Adil, Syed Hasan; Raza, Kamran

doi:10.1109/iceest.2018.8643327

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Like many other studies [18,19,21,23,49], this study adopts three bases line ML algorithms, namely DT, SVM and NN, based on their superiority for ensemble learning in financial analysis.…”

Section: Predictive Modelsmentioning

confidence: 99%

A comprehensive evaluation of ensemble learning for stock-market prediction

2020

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“…Like many other studies [18,19,21,23,49], this study adopts three bases line ML algorithms, namely DT, SVM and NN, based on their superiority for ensemble learning in financial analysis.…”

Section: Predictive Modelsmentioning

confidence: 99%

A comprehensive evaluation of ensemble learning for stock-market prediction

2020

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“…The chromosomes (C h ) value was calculated as expressed by Eq. (10). Where signifies the number of features in the dataset (D set ) and denotes the number of classifiers.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Usmani et al [10,11] proposed a hybrid machine learning algorithm based on SVM, Single Layer Perceptron (SLP), Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Autoregressive Integrated Moving Average (ARIMA) for predicting the Karachi Stock Exchange (KSE) index. Similarly, Chen and Hao [12] applied Feature sources, Sunyani, Ghana; Department of Computer Science, Sunyani Technical University, Sunyani, Ghana; ORCID: 0000-0001-9257-4295 Adebayo Felix Adekoya: Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana; ORCID: 0000-0002-5029-2393 Benjamin Asubam Weyori: Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana; ORCID: 0000-0001-5422-4251 [12] FWSVM and FWKNN MAPE = 0.27 Yes Market index RMSE = 0.0070 [7] SVM Accuracy = 89% No Stock-price [13] SVM Weighted SVM (FWSVM) and Feature Weighted K-Nearest Neighbour (FWKNN) algorithms for predicting the SSE Composite Index.…”

Section: Introductionmentioning

confidence: 99%

Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

Nti

Adekoya

Weyori

2020

Open Computer Science

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AbstractPredicting stock-price remains an important subject of discussion among financial analysts and researchers. However, the advancement in technologies such as artificial intelligence and machine learning techniques has paved the way for better and accurate prediction of stock-price in recent years. Of late, Support Vector Machines (SVM) have earned popularity among Machine Learning (ML) algorithms used for predicting stock price. However, a high percentage of studies in algorithmic investments based on SVM overlooked the overfitting nature of SVM when the input dataset is of high-noise and high-dimension. Therefore, this study proposes a novel homogeneous ensemble classifier called GASVM based on support vector machine enhanced with Genetic Algorithm (GA) for feature-selection and SVM kernel parameter optimisation for predicting the stock market. The GA was introduced in this study to achieve a simultaneous optimal of the diverse design factors of the SVM. Experiments carried out with over eleven (11) years’ stock data from the Ghana Stock Exchange (GSE) yielded compelling results. The outcome shows that the proposed model (named GASVM) outperformed other classical ML algorithms (Decision Tree (DT), Random Forest (RF) and Neural Network (NN)) in predicting a 10-day-ahead stock price movement. The proposed (GASVM) showed a better prediction accuracy of 93.7% compared with 82.3% (RF), 75.3% (DT), and 80.1% (NN). It can, therefore, be deduced from the fallouts that the proposed (GASVM) technique puts-up a practical approach feature-selection and parameter optimisation of the different design features of the SVM and thus remove the need for the labour-intensive parameter optimisation.

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“…The findings show that RF and GBT are superior to SVM for their selected dataset. Usmani, Ebrahim, Adil & Raza (2019) also predicted the performance of the Karachi Stock Exchange (KSE) as merged into Pakistan Stock Exchange (PSX) with a proposed Hybrid model. The methods employed were Support Vector Machine, Radial Basis Function (RBF), and ANN.…”

Section: Papers Reviewedmentioning

confidence: 99%

Intelligent Models for Stock Price Prediction

Ansah

Denwar

Appati

2022

Journal of Information Technology Research

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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.

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Predicting Market Performance with Hybrid Model

Cited by 10 publications

References 10 publications

A comprehensive evaluation of ensemble learning for stock-market prediction

A comprehensive evaluation of ensemble learning for stock-market prediction

Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

Intelligent Models for Stock Price Prediction

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