A new metric for individual stock trend prediction. (June 2019)

• Record Type: Journal Article
• Authors: Liu, Guang; Wang, Xiaojie
• Is Part Of: Engineering applications of artificial intelligence. Volume 82(2019)
• Page Start: 1
• Page End: 12
• Publication Date: 2019-06
• Subjects: Stock trend prediction -- Model evaluation -- Classification -- Machine learning -- Artificial intelligence; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.03.019
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Individual stock trend prediction is extremely valuable for investment management. Previous studies mainly focused on proposing effective approaches to make profits. However, there is an ineffectiveness in model evaluation due to the inconsistency between model's performance and profitability. We name this inconsistency profit bias. In order to address the profit bias in model evaluation, this paper proposes a new effective metric, Mean Profit Rate (MPR). The effectiveness of metric is measured based on the correlation between the metric value and profit of the model. Experiments on five stock daily index data among four countries show that MPR outperforms the classification metrics in correlating to profit. In view of these findings, we suggest that MPR is a more effective metric than the classification metrics in stock trend prediction.