Theory and use case of game-theoretic lexical link analysis

Abstract

We demonstrate a machine learning method, namely lexical link analysis (LLA), which can be used to discover high-value information from financial data. LLA is an unsupervised learning method that does not require manually labeled training data. We also demonstrate how to form LLA in a game-theoretic framework. We show that with game theory: high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information and at the same time generates high social welfare, therefore containing higher intrinsic value. We show the results of LLA of two sets of financial data validating and correlating with the ground truth.

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