This study uses Shikiho data and ChatGPT to assess the impact of stale, factual information on stock prices, revealing potential market inefficiencies as stock performance is benchmarked against risk factors and suggesting a need for further exploration of public information in price discovery.

This study uses a Convolutional Neural Network to analyze Japanese stock market charts, finding that two-dimensional historical data can predict future returns, revealing unique market characteristics beyond common momentum effects.