Technical Patterns and News Sentiment in Stock Markets

Abstract

This paper explores the effectiveness of technical patterns in predicting asset prices and market movements, emphasizing the role of news sentiment. We employ an image recognition method to detect technical patterns in price images and assess whether this approach provides more information than traditional rule-based methods. Our findings indicate that many model-based patterns yield significant returns in the US market, whereas top-type patterns are less effective in the Chinese market. The model demonstrates high accuracy in training samples and strong out-of-sample performance. Our empirical analysis concludes that technical patterns remain effective in recent stock markets when combined with news sentiment, offering a profitable portfolio strategy. Moreover, we find patterns better predict returns for firms with high momentum, institutional ownership, and prior patterns in US, while in China, they are more effective for small firms with high momentum and institutional ownership. This study highlights the potential of image recognition methods in market data analysis and underscores the importance of sentiment in technical analysis.