The fundamental analysis assumes the movement in the market is a reflection of real world operation of companies. These companies can either be collaborative or competitive, hence form a dynamic business network. 

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The intriguing task of financial forecasting attracts researchers with both computer science and finance backgrounds. The ways they formalize this task are diversified, and together form a spectrum of perspectives:
- The connectionist perspective
- The portfolio management perspective
- The energy system perspective
- The social network perspective

Spectrum of Perspectives 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

Economists hold the belief that assets in similar sectors will have similar behavior due to the fundamental environment. Corporations that are involved in the same manufacturing chain are also connected in some sense. The mainstream way to dig into connections is data mining techniques. However, textual data can be used to drive the connection discovery as well.

The Connectionist Perspective

When constructing trading strategies, certain constraints may significantly change the effectiveness in practice. Therefore, portfolio management can be formalized as an optimization problem. Machine learning techniques can be actively used in solving this optimization problem when asset prices are fast-changing or weights are allocated across assets sparsely.

The Portfolio Management Perspective

The Energy System Perspective

With plenty of heterogeneous market participants, the simulation suggests that bubbles may easily triple the fundamental price. This puts into serious question about to what extent the market price depends on real world economic scenarios, or market fluctuation is just a reflection of mass sentiment. In this perspective, the excess return comes from the correct reaction to the sticky nature of market fluctuation.

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