Pricing and Trading of Data Options on Data-as-a-Service Platforms

In current Data-as-a-Service (DaaS) platforms, data marketplaces are the primary mechanism by which data owners deliver data products and services to users. However, data owners incur substantial costs to collect or produce data before selling it, exposing them to economic risks due to market price fluctuations. Options trading, used in traditional commodity markets, can alleviate sellers' financial burdens with forward contracts at fixed option prices and pre-paid option premiums. To our knowledge, the application of option trading in data marketplaces has not been thoroughly studied or implemented. In this work, we advocate for the first data options marketplace on a DaaS platform that mitigates price fluctuation risks and reduces market entry barriers. Designing such a marketplace involves several key challenges, including the easy replication of data and the difficulty in assessing data quality before production. To address these challenges, we first design a data quality prediction method based on sellers' reputation. Following this, we model and quantify the competitive relationships among buyers. On this basis, we model the interactions between sellers and buyers in the data options market as a two-stage Stackelberg game, focusing on maximizing sellers' profit. We formally derive the perfect subgame equilibrium for option trading and derive each seller's optimal pricing strategy. Numerical experiments demonstrate the superiority of data options trading over conventional data trading methods in DaaS platforms.