Overview

CrunchDAO & X Alpha offer this first of a series of $10,000 Bounty to build the most Powerful, Robust and Unbiased AI-Driven VC algorithm.

Read more about the project: https://www.crunchdao.com/live/x-alpha

The evolving landscape of Venture Capital is marked by the automation of both data and investment funds, including Micro VC and solo general partners. This trend is leading to a comprehensive decentralization within the venture class. Against this backdrop, investors and Limited Partners are increasingly in need of a systematic approach to maximize returns across diverse asset classes.

In response to this need, the CrunchDAO is crunching the data of more than 2 million startups and 28 million founders, in order to discover the hidden patterns and relationships that will fuel the next wave of venture capitalism.

Problem Statement

Cross-sectional Approach

The proposed approach allows for a comprehensive analysis of startups by simultaneously examining various data points and trends. This method contrasts with traditional models by integrating diverse data sets and employing advanced statistical techniques to discern both linear and non-linear relationships.

Such a multifaceted view enables more accurate predictions and therefore effective capital allocation, incorporating quantitative risk management strategies not commonly used in the Venture Capital sector.

Supervised Classification Approach

Startups have been categorized, enabling participants to develop supervised learning algorithms. Startups labeled as 1 are expected to achieve higher valuations, while those labeled as 0 are not anticipated to experience significant valuation growth.

Scoring Metric

The F1 score will be used in order to assess the models performance effectively. This metric balances the precision (true positives identified by the algorithm) and recall (accounting for missed opportunities). For the algorithm to demonstrate its effectiveness, it must accurately identify investment opportunities while minimizing false negatives and false positives. The F1 score will provide a comprehensive view of the algorithm's accuracy and reliability.

Competition Phases and Format

In the first phase, participants are required to submit either a Python notebook (.ipynb) or Python script (.py) file. This file should contain the necessary code to build, load, or update their models trained on the data. The code will be executed by the CrunchDAO on the Out-Of-Sample data. Participants can either submit static models, trained only once on the initial training set, or dynamic models that update or retrain themselves on the unseen data, as explained further in the documentation.