Supporting Large Scale Collaboration and Crowd-Based Investigation in Economics: A Computational Representation for Description and Simulation of Financial Models

Abstract

Finance should be studied as a hard science, where scientific methods apply. When a trading strategy is proposed, the underlying model should be transparent and defined robustly to allow other researchers to understand and examine it thoroughly. Any reports on experimental results must allow other researchers to trace back to the original data and models that produced them. Like any hard sciences, results must be repeatable to allow researchers to collaborate and build upon each other’s results. Large-scale collaboration, when applying the steps of scientific investigation, is an efficient way to leverage crowd science to accelerate research in finance. Unfortunately, the current reality is far from that. Evidence shows that current methods of investigation in finance in most cases do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of financial decisions at all levels, from personal investment choices to overreaching global economic policies, rely on some variation of try-and-error and are mostly non-scientific by definition. We lack transparency for procedures and evidence, proper explanation of market events, predictability on effects, or identification of causes. There is no clear demarcation of what is inherently scientific, and as a consequence, the line between fake and true is blurred. In this research, we advocate the use of a next-generation investigative approach leveraging forces of human diversity, micro-specialized crowds, and proper computer-assisted control methods associated with accessibility, reproducibility, communication, and collaboration. This thesis is structured in three distinctive parts. The first part defines a set of very specific cognitive and non-cognitive enablers for crowd-based scientific investigation: methods of proof, large-scale collaboration, and a domain-specific computational representation. These enablers allow the application of procedures of structured scientific investigation powered by crowds, a “collective brain in which neurons are human collaborators”. The second part defines a specialized computational representation to allow proper controls and collaboration in large-scale in the field of economics. A computational representation is a role-based representation system based on facets, contributions, and constraints of data, and used to define concepts related to a specific domain of knowledge for crowd-based investigation. The third and last part performs an end-to-end investigation of a non-trivial problem in finance by measuring the actual performance of a momentum strategy in technical analysis, applying formal methods of investigation developed over the first and second part of this research