Performance and learning in an ambiguous environment: A study of cryptocurrency traders

We investigate the performance and learning ability of traders in an environment governed by ambiguity, such as the cryptocurrency market. Using a profit decomposition methodology, we find significant cross-sectional and temporal heterogeneity in performance. Traders do not learn to progressively increase the magnitude of returns; however, they are able to improve on their ability to realise profits as a mechanism of adaptation to survive through ambiguity. This adaptation increases as traders progress through their career. Moreover, we find evidence in support of the gambler's fallacy. We argue that learning in ambiguous environments has limitations, allowing traders primarily to survive.