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.

Stock market collects huge amount of data which is uncertain, insufficient or fuzzy in nature.To make predictions for such data is very complicated task and one of the biggest challenges to the AI community. Various traditional and statistical indicators have been proposed for this. However, combination of these tools and techniques requires highly human expertise and so much justification in the area. Stock market behavior is highly suspecible. To increase performance of prediction there is a need of method which can accurately predict stock price and can train multiple records simultaneously. Neural Network is very important tool for stock market prediction. This paper mainly highlights the Neural Network based approach to predict stock market behavior and also helps the stock brokers and investors to invest money in stock market business at the right time.

In these days, stock market forecasting is one of the most interesting issues, which has gained a more attention due to vast profits. To precisely predict the price of share and making profits has been always challenging task since the longest period of time. This has engrossed the interest and attention of stock brokers, economists and applied researchers. Traditional methods like Fundamental analysis, Technical analysis, and Regression methods are not suitable for this task because these tools and techniques are based on totally different analytical approaches and requiring highly expertise and justification in the area. In this sequence, Association Rule Mining is one of the most interesting research areas for finding the associations, correlations among items in a database. It can discover all useful patterns from stock market dataset. The aim of this research study is to help stock brokers, investors so that they can earn maximum profits for each trading.

<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>

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