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http://dx.doi.org/10.5392/JKCA.2020.20.10.702

Fair Performance Evaluation Method for Stock Trend Prediction Models  

Lim, Chungsoo (한국교통대학교 전자공학과)
Publication Information
The Journal of the Korea Contents Association / v.20, no.10, 2020 , pp. 702-714 More about this Journal
Abstract
Stock investment is a personal investment technique that has gathered tremendous interest since the reduction in interest rates and tax exemption. However, it is risky especially for those who do not have expert knowledge on stock volatility. Therefore, it is well understood that accurate stock trend prediction can greatly help stock investment, giving birth to a volume of research work in the field. In order to compare different research works and to optimize hyper-parameters for prediction models, it is required to have an evaluation standard that can accurately assess performances of prediction models. However, little research has been done in the area, and conventionally used methods have been employed repeatedly without being rigorously validated. For this reason, we first analyze performance evaluation of stock trend prediction with respect to performance metrics and data composition, and propose a fair evaluation method based on prediction disparity ratio.
Keywords
Stock Price Trend Prediction; Performance Evaluation Method for Stock Trend Prediction; Machine Learning;
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