In cities across the world, especially under dense settings, transportation network companies (TNCs) account for a non-trivial mode share. With the introduction of shared autonomous vehicles (SAVs), ridehailing will become more common. When multiple TNCs or SAV fleets compete for customers, fares, wait times, and other performance metrics are affected. This study simulates competition between two SAV operators for the Bloomington, Illinois region with a profit-maximization objective. Fare strategies such as a time-of-day (TOD) factor, zone- based surge pricing (ZSP), and a combination of the two are simulated. Customers are assumed to choose operators based on low fares, after accounting for bias in operator preference. Results from a large-scale agent-based simulator called POLARIS suggest that the implementation of the TOD and ZSP simultaneously appears to be advantageous for fleet operators, as it helps increase their profit. While dynamic ridesharing is beneficial for passengers by reducing their fares, it leads to losses for SAV operators. Fleet size and, consequently, coverage for request assignment impacts profit.