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Efficient Stock-Market Prediction Using Ensemble Support Vector Machine - 2020

Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

Research Area:  Machine Learning


Predicting stock-price remains an important subject of discussion among financial analysts and re-searchers. However, the advancement in technologies such as artificial intelligence and machine learning tech-niques 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 in-vestments based on SVM overlooked the over fitting 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) out performed 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). Itcan, therefore, be deduced from the fall outs that the pro-posed (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.


Author(s) Name:  Isaac Kofi Nti, Adebayo Felix Adekoya and Benjamin Asubam Weyori

Journal name:  Open Computer Science

Conferrence name:  

Publisher name:  De Gruyter

DOI:  10.1515/comp-2020-0199

Volume Information: