Analyzing visual technical patterns - a neural network based saliency analysis

We propose a neural network based saliency analysis to identify important information related to investment decisions. We apply our approach to 753 US stocks and 1960 head-and-shoulders patterns. The results show that among all variables that are related to technical patterns, membership value and pattern length as well as firm size have the highest saliency coefficients and are the most relevant to future returns. The results are consistent with previous studies and the practice of technical analysis. Our study suggests that saliency analysis offers much greater advantage when compared with correlation coefficients in revealing the relationships in the stock market. Average investors can use saliency analysis to sort through information and find the more important information to focus on, therefore shortening their learning curve of becoming expert investors.