Technical Patterns and News Sentiment in Stock Markets

Markus Leippold, Qian Wang and Min Yang
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Markus Leippold: University of Zurich; Swiss Finance Institute
Qian Wang: University of Zurich - Department Finance; Inovest Partners AG
Min Yang: Swiss Finance Institute - University of Zurich

No 24-88, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 bottom-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. This study highlights the potential of image recognition methods in market data analysis and underscores the importance of sentiment in technical analysis.

Pages: 41 pages
Date: 2024-08
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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