Classification of Stock Market Trends with Confidence-Based Selective Predictions

Autor:	Rakesh Katuwal, Xueheng Qiu, Wen Xin Cheng, Ponnuthurai Nagaratnam Suganthan
Rok vydání:	2020
Předmět:	Time series classification Time windows Econometrics Economics Stock market Stock price forecasting Ensemble learning Stock price Stock (geology)
Zdroj:	Communications in Computer and Information Science ISBN: 9783030378370 SEMCCO/FANCCO
Popis:	Predicting the trend of stock price movement accurately allows investors to maximize their profits from investments. However, due to the complexity of the stock data, classifiers often make errors, which cause the investors to lose money from failed investments. This study attempts to reduce such risks by focusing on easy-to-classify cases that have the highest chances of success. Therefore, we propose a method which selects only the predictions that have the highest confidence. In an experiment on 50 stocks, each learning model is trained on each stock data and evaluated based on the classification accuracy over a moving time window. The models which have the highest confidence are selected to predict the trend for that stock the next day. The experiment results shows the classification accuracy has improved significantly when the top 10% of predictions were used.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3c3fa604a0bedf8f639796241d5e5261 https://doi.org/10.1007/978-3-030-37838-7_9 Zobrazit plný text záznamu