Short-Term Stock Price Prediction by Supervised Learning of Rapid Volume Decrease Patterns

Jangmin OH

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.8    pp.1431-1442
Publication Date: 2022/08/01
Publicized: 2022/05/20
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2021EDP7243
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
trading volume,  volume pattern,  moving average,  neural network,  stock price prediction,  

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Recently several researchers have proposed various methods to build intelligent stock trading and portfolio management systems using rapid advancements in artificial intelligence including machine learning techniques. However, existing technical analysis-based stock price prediction studies primarily depend on price change or price-related moving average patterns, and information related to trading volume is only used as an auxiliary indicator. This study focuses on the effect of changes in trading volume on stock prices and proposes a novel method for short-term stock price predictions based on trading volume patterns. Two rapid volume decrease patterns are defined based on the combinations of multiple volume moving averages. The dataset filtered using these patterns is learned through the supervised learning of neural networks. Experimental results based on the data from Korea Composite Stock Price Index and Korean Securities Dealers Automated Quotation, show that the proposed prediction system can achieve a trading performance that significantly exceeds the market average.

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