Performance comparison of artificial neural network (ANN) and support vector machines (SVM) models for the stock selection problem: An application on the Istanbul Stock Exchange (ISE) - 30 index in Turkey

Timor, Mehpare

doi:10.5897/ajbm11.1802

Cited by 5 publications

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Kamley¹,

Jaloree²,

Thakur

2016

IJECE

View full text Add to dashboard Cite

<p>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.</p>

show abstract

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

Kamley¹,

Jaloree²,

Thakur

2016

IJECE

View full text Add to dashboard Cite

show abstract

Sentiment Analysis using Naive Bayes Classifier and Information Gain Feature Selection over Twitter

Singh¹,

Gupta²

2020

IJCTT

View full text Add to dashboard Cite

Comparative Analysis of Stock Market Prediction System using SVM and ANN

Shrimalve¹,

Talekar²

2018

IJCA

View full text Add to dashboard Cite

Support vector machines (SVM) and artificial neural networks (ANN) are machine learning methods that find a wide range of applications both in the field of engineering and social sciences. Here, Artificial Neural Networks (ANN) and Support vector machines (SVM) are employed to predict stock market daily trends: ups and downs. The purpose is to study the variation in certain parameters like accuracy, time efficiency of both classifiers (ANN and SVM) on similar datasets in predicting stock market daily trends. In this study, different SVM and ANN models for the problem of stocks prediction which provide maximum accurate results have been applied on different combinations of data sets which obtained from the historical data of various companies and comparative analysis has been presented. The findings show that SVM and ANN models give meaningful performance results for the stock investment.

show abstract

Performance comparison of artificial neural network (ANN) and support vector machines (SVM) models for the stock selection problem: An application on the Istanbul Stock Exchange (ISE) - 30 index in Turkey

Cited by 5 publications

References 46 publications

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

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

Sentiment Analysis using Naive Bayes Classifier and Information Gain Feature Selection over Twitter

Comparative Analysis of Stock Market Prediction System using SVM and ANN

Contact Info

Product

Resources

About