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

Abstract

<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>

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

“…Credit card default/fraud detection is a crucial problem that has gotten the attention of machine learning researchers, and a significant number of approaches have been proposed [6]- [9]. However, the problem is still challenging since most credit card data seem to suffer from class imbalance as non-fraud transactions overwhelmingly supersede fraud transactions, making it difficult for many machine learning algorithms to achieve good performance.…”