Ensemble methods for Stock Market Prediction

Bravo, J. M. V. (2023). Ensemble methods for Stock Market Prediction. Paper presented at The 8th Workshop on MIning DAta for financial applications, Turin, Italy.otherunpublishe

Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets

Finding rules for audit opinions prediction through data mining methods

Nowadays data mining, which is used in various accounting and financial applications, has received a great deal of attention. One of these applications is predicting and identifying the audit opinion type. The objective of research is to help auditors identify audit opinions by using a support vector machine from data mining methods. The system receives the data from financial reports and identifies the type of audit opinions. This approach combine support vector machine with a decision tree that can understand and interpret the obtained results. In this paper, a novel approach for rule extraction from support vector machine and decision tree is presented and its application is shown in the prediction of audit opinions. The research result is 30 rules that predict the audit opinions

Finding rules for audit opinions prediction through data mining methods

Nowadays data mining, which is used in various accounting and financial applications, has received a great deal of attention. One of these applications is predicting and identifying the audit opinion type. The objective of research is to help auditors identify audit opinions by using a support vector machine from data mining methods. The system receives the data from financial reports and identifies the type of audit opinions. This approach combine support vector machine with a decision tree that can understand and interpret the obtained results. In this paper, a novel approach for rule extraction from support vector machine and decision tree is presented and its application is shown in the prediction of audit opinions. The research result is 30 rules that predict the audit opinions