Ensemble system based on genetic algorithm for stock market forecasting

Gonzalez, Rafael; Padilha, Carlos Eduardo de Araújo; Barone, Dante Augusto Couto

doi:10.1109/cec.2015.7257276

Cited by 24 publications

(14 citation statements)

References 29 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%

“…Despite numerous works revealing the dominance of ensemble classifier over single classifier, most of these studies only ensemble a specific type of classifier or regressor for stock-market prediction, such as NN [18][19][20], DT [21,22] and SVM [12,23]. Also, most previous studies [12,19,21,22,[24][25][26][27][28][29][30], on ensemble methods for stock-market predictions adopted the decrease variance approach (boosting or bagging) and experimented with data from one country.…”

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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%

mentioning

confidence: 99%

A comprehensive evaluation of ensemble learning for stock-market prediction

2020

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“…Moreover, Lin et al [19], argued that the SVM could not precisely select a feature subset to contain features that are highly associated with the output, yet uncorrelated with each other [19]. Notwithstanding, according to Gonzalez et al [20], these issues impede the generalisation performance of the SVM [20]. This limitation causes overfitting of the SVM in predicting financial data, due to the higher dimensional, precariousness, and noise associated with financial data.…”

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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“…These variables include short term and long term interest rates, inflation rate, Foreign Direct Investment (FDI), unemployment rate, Gross Domestic Product (GDP), Consumer Price Index (CPI), Industrial Production (IP), Government Consumption (GC), Private Consumption (PC), Gross National Product (GNP), Money Supply, Oil Prices, Exchange Rates etc. [13], [22], [27], [39][40][41][42][43][44][45][46][47][48].…”

Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur³

2016

IJECE

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Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.

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Ensemble system based on genetic algorithm for stock market forecasting

Cited by 24 publications

References 29 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

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

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