Crowdsourced Investment Research through Tournaments

Abstract

Traditionally, the development of investment strategies has required domain-specific knowledge as well as access to restricted datasets. This has meant that investment opportunities are not researched by the majority of data scientists, because they lack either or both of these requirements. In this article, the authors discuss the merits of tournaments as a crowdsourcing paradigm for investment research. Tournaments can overcome the two research barriers (domain-specific knowledge and data barriers), hence enabling the wide population of data scientists to contribute to the development of investment strategies. Key Points: 1. There are four flaws with the development of investment strategies: domain-specific knowledge barrier, budgetary constraints and confidentiality restrictions, inability to monetize the value of data, and backtest overfitting. 2. Tournaments offer a solution for overcoming the four flaws associated with developing investment strategies. 3. The modern investment process suggested allows data scientists without an investment background to contribute forecasts to a systematic asset manager.