Uncertainty Linguistic Summarizer to Evaluate the Performance of Investment Funds

Abstract

This chapter proposes a methodology to implement the uncertain linguistic summarizer posed in Liu’s uncertain logic to measure the performance of investment funds in the Colombian capital market. The algorithm extracts a truth value for a set of linguistic summaries, written as propositions in predicate logic, where the terms for the quantifier, subject, and predicate are unsharp. The linguistic summarizer proves to be autonomous, successful, efficient, and close to human language. Furthermore, the implementation has a general scope and could become a data mining tool under uncertainty. The propositions found characterize with plenty of sense the investment funds data. Finally, a Corollary that allows accelerating the obtention of the summaries is presented.