Pattern Recognition Software Plus Confirming News Sentiment?

Can pattern recognition software, combined with news sentiment, identify profitable short-term stock trades? In their July 2024 paper entitled "Technical Patterns and News Sentiment in Stock Markets", Markus Leippold, Qian Wang and Min Yang test the ability of pattern recognition software (convolutional neural network) to find profitable technical reversal patterns within U.S. and Chinese stock candlestick charts. They consider four pairs of technical patterns: double tops and bottoms; head and shoulders and inverted head and shoulders; broadening tops and bottoms; and, triangle tops and bottoms. They use Bollinger bands to find local maximums and minimums, with the standard deviation multiplier set at 1.1 based on parameter tuning. They augment pattern recognition with news sentiment from Refinitiv for U.S. stocks since 2003 and from Tonglian for Chinese stocks since 2014 during the 10 trading days around each pattern. They identify combined tops as double tops, head and shoulders, broadening tops or triangle tops coupled with negative news and combined bottoms as double bottoms, inverted head and shoulders, broadening bottoms or triangle bottoms coupled with positive news. They first consider each technical pattern as an independent event and measure abnormal returns for holding intervals of 1, 5, 10, 21 and 42 days after a signal. They then examine the performance of a portfolio of events for a 1-day holding interval. They use U.S. stock data from 1992 to 1999, enhanced via two data augmentation strategies, for pattern recognition software training and validation. They then apply the trained software to U.S. stock data from 2000 through 2021 and Chinese stock data from 2005 through 2021. Combined pattern-sentiment test periods are shorter based on availability of sentiment data. Using price series for all U.S. common stocks and Chinese A-shares and news sentiment data as described through 2021, they find that: