Geometric Case Based Reasoning for Stock Market Prediction

• Published in: Sustainability Vol. 12; no. 17; p. 7124
• Main Authors: Chun, Se-Hak, Ko, Young-Woong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2020
• Subjects: Bond ratings; Case studies; Cybersecurity; Dow Jones averages; Euclidean geometry; Genetic algorithms; Knowledge; Neural networks; Predictions; Prices; Random walk; Reasoning; Recommender systems; Securities markets; Statistical methods; Stock exchanges; Stock market indexes; Sustainability; Time series
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su12177124

Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p < 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error.