This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm and a novel dataset on COVID-19 quarantines, we find that quarantine periods trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to an annualized loss of approximately 70 percentage points for the representative investor's portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides causal evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which COVID-19 policies affect financial markets.

We survey a large representative sample of retail investors to elicit their memories of stock market investment and return expectations. We then merge the survey data with administrative data of transactions to test a model in which investors form expectations by selectively recalling past experiences similar to the present cue. Our analysis not only uncovers newstylized facts about investor memory, but also provides support for similarity-based recall as a key mechanism of belief formation in ffnancial markets. Market ffuctuations affect investors’ recall: positive market returns cue investors to retrieve episodes of rising markets and recall own performances more positively. Recalled experiences explain a sizable fraction of cross-investor variation in beliefs and dominate actual experiences in explanatory power. We also show that recalled experiences can drive out the explanatory power of recent returns for expected future returns, ruling in a memory-based foundation for return extrapolation.