Complex systems and ‘‘Spatio -Temporal Anti-Compliance Coordination’’ In cyber-physical networks: A critique of the Hipster Effect, bankruptcy prediction and alternative risk premia

Abstract

The Hipster Effect is a group of evolutionary ‘‘Diffusive Learning’’ processes of networks of individuals and groups (and their communication devices) that form Cyber-Physical Systems; and the Hipster Effect theory has potential applications in many fields of research. This study addresses decision-making parameters in machine-learning algorithms, and more specifically, critiques the explanations for the Hipster Effect, and discusses the implications for portfolio management and corporate bankruptcy prediction (two areas where AI has been used extensively). The methodological approach in this study is entirely theoretical analysis. The main findings are as follows: (i) the Hipster Effect theory and associated mathematical models are flawed; (ii) some decision-making and learning models in machine-learning algorithms are flawed; (iii) but regardless of whether or not the Hipster Effect theory is correct, it can be used to develop portfolio management models, some of which are summarised herein; (iv) the [1] corporate bankruptcy prediction model can also be used for portfolio-selection (stocks and bonds).