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2020 | OriginalPaper | Chapter

Classification of Stock Market Trends with Confidence-Based Selective Predictions

Authors : Wen Xin Cheng, P. N. Suganthan, Xueheng Qiu, Rakesh Katuwal

Published in: Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing

Publisher: Springer International Publishing

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Abstract

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.

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The stock prices can be downloaded from Yahoo Finance at http://www.finance.yahoo.com/.

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Title: Classification of Stock Market Trends with Confidence-Based Selective Predictions
Authors: Wen Xin Cheng
P. N. Suganthan
Xueheng Qiu
Rakesh Katuwal
Publisher: Springer International Publishing
Book: Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing
Print ISBN: 978-3-030-37837-0

Electronic ISBN: 978-3-030-37838-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-37838-7_9

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