We develop a machine learning (ML) approach to establish new insights into how memory affects ffnancial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts’ mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts’ mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts’ belief formation, including the temporal contiguity effect and selective forgetting.