Short term stock selection with case-based reasoning technique

A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions

In this paper, we suggest a new case-based reasoning method for stock price predictions using the knowledge of traders to select similar past patterns among nearest neighbors obtained from a traditional case-based reasoning machine. Thus, this method overcomes the limitation of conventional case-based reasoning, which does not consider how to retrieve similar neighbors from previous patterns in terms of a graphical pattern. In this paper, we show how the proposed method can be used when traders find similar time series patterns among nearest cases. For this, we suggest an interactive prediction system where traders can select similar patterns with individual knowledge among automatically recommended neighbors by case-based reasoning. In this paper, we demonstrate how traders can use their knowledge to select similar patterns using a graphical interface, serving as an exemplar for the target. These concepts are investigated against the backdrop of a practical application involving the prediction of three individual stock prices, i.e., Zoom, Airbnb, and Twitter, as well as the prediction of the Dow Jones Industrial Average (DJIA). The verification of the prediction results is compared with a random walk model based on the RMSE and Hit ratio. The results show that the proposed technique is more effective than the random walk model but it does not statistically surpass the random walk model.

Geometric Case Based Reasoning for Stock Market Prediction

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.