How We Are Different

Professionals and People Just Getting Started

We know that not everyone has an MBA in financial analysis. We are creating a community where someone with no background in quantitative analysis can easily get started. One of our primary missions is to help people without a background in financial modeling to get started. While we start basic, we also have professional-grade modeling tools that allow for more flexibility and individual analyst input. We also believe in a mentoring community where those with more expertise will help those just getting started.

Open-source and transparent

All of our models are open sourced and time-tested. We know that financial models are very dependent on their inputs and assumptions, so we get you to focus on that and then show you the math that calculates the output. No secrets, just better understanding.

From Back-of-the-Envelope to Deep Dive

If you are completely new to financial modeling, we have the tools to get you started. Model 101 is designed to be easy to understand while still providing valuable investment insights. As you learn more, we have additional models that allow you to add in more detail all the way up to models that do a deep dive into the financials of a company.

Automatic Data Aggregation with Crowdsourced Enhancements

We aggregate all the data that we can for you so that you can focus your time on the analysis. We also have a platform where the community can augment the standard financial data with unique time series data that is related to specific companies.

A Standard Set of Models

All too often, analysts are using different models for valuation. This leads to an apples-to-oranges comparisons. By having a standard set of models that are applicable across most equities, analysts can focus on how the inputs and assumptions change the model outputs. This allows for a common framework for having apples-to-apples discussions about valuation.

Unique "Wisdom of the Crowds" Dataset

By allowing users to share their models, we are creating a unique set of data that the community can leverage for additional insights.