It is important to construct financial domain specific form of knowledge representation, such as ontologies, wordlists, concept databases, manually annotated datasets, etc. 

University of Michigan - Ann Arbor

Three promising future directions of NLFF in the near term are as follows:
- Domain specific resources building
- Online predictive model:
- Comprehensive evaluation measurements

Future Directions in NLFF

https://dspace.mit.edu/bitstream/handle/1721.1/116314/10462_2017_9588_ReferencePDF.pdf?sequence=2&isAllowed=y

A Survey of Natural Language Based Financial Forecasting

Domain Specific Resources Building

Online, or real-time algorithms will modify the key variables stored with the model each time a new batch of data comes in. For this reason, online models have very good adaptability, which is necessary for monitoring fast-changing markets.

Online Predictive Model

A main current difficulty to summarize and compare the existing studies is various and incomplete measurements used by researchers. Experiments should cover different kinds of metrics; for example, volatility measured by the stability of trading simulation is closely correlated to Sharpe Ratio. Many efforts are needed to unify these measures.

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