Learning with Less: Machine Learning from Partial Market Information

Financial market data provide incomplete views of complex underlying interactions, raising questions about what machine learning models can infer and which information is useful for downstream tasks. We investigate how the availability, granularity, and representation of market observations shape learning in financial market microstructure. Our focus is on the relationship between observed information, learned representations, and their relevance to prediction and decision-making. We consider how useful structure may be extracted from partial observations while accounting for uncertainty about information that is not directly available. A central question is when additional detail provides meaningful value and when a more compact representation may be sufficient for the task of interest. This perspective connects learning under partial observability with the broader problem of aligning representations with downstream objectives. The research aims to clarify the opportunities and limitations of learning from incomplete market information and to inform the development of models whose representations are appropriate to both the available observations and their intended use.