TICKER SYMBOL IDENTIFICATION WITH CIMA ON NON-STATIONARY STOCK PRICE DATASET
Abstract
Ticker symbol identification based on stock price data in investor decisions has been proven to be pivotal. Though research exists on stock price forecasting, ticker symbol identification is still a research opportunity. Meanwhile, some temporal-sequential classification methods are available, such as classification-integrated moving average (CIMA) and recurrent neural network (RNN)-based deep learning such as long short-term memory (LSTM), and gated recurrent unit (GRU). Our research aim is to prove that CIMA can perform ticker symbol identification on non-stationary stock price datasets. This research collects ten most well-known stock price dataset from Kaggle and performs pre-processing. Then it designs CIMA with non-stationary data and the benchmark deep learning methods. Both methods are optimized with hyperparameter tuning and model selection between adaptive boosting (AdaBoost) and legacy k-nearest neighbors (KNN). The test results show five non-stationary features in the stock price dataset must go through a differentiation process. Then, AdaBoost has an accuracy of 0.9967 ± 0.001, while KNN has an accuracy of 0.9971 ± 0.001, with no significant difference based on t-test. Meanwhile, AdaBoost has a significantly smaller model size and testing and prediction time than KNN. In benchmarking, CIMA+AdaBoost is superior to the three other methods for accuracy, precision, recall, and f1-score, all of which have a value of 0.996. Our research contribution is ticker symbol identification based on stock price using CIMA on multiple-class sequential classification with non-stationary data. For future research, we advice to perform this method on other stock price data.
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