The scope of this dissertation is to infer the character of the forces controlling complex systems modeled by Langevin equations, by recourse to information-theory quantifiers. We evaluate in detail the permutation entropy (PE) and the permutation statistical complexity (PSC) measures for two classes of similarity of stochastic models characterized by drifting and reversion properties, respectively, employing them as a framework for the inspection of real series. We found new relevant model parameters for PE and PSC measures as compared to standard entropy measures. We determine the PE and PSC curves according to these parameters for different permutation orders n and infer the limiting measures for arbitrary large order. Although the PSC measure presents a strongly scaledependent behavior, a key n-invariant outcome arises, enabling one to identify the nature (drifting or reversion) of the deterministic sources underlying the complex signal. We conclude by investigating the presence of local trends in stock price series.