Venture capital portfolio prediction competition
CrunchDAO & X Alpha offer this $10,000 Bounty to build the most Powerful, Robust and Unbiased AI-Driven VC algorithm.
About X Alpha
X Alpha deploys capital in early and mid-stage disruptive companies across the US and western Europe.
For the first time ever, a leading industry expert with a 15-year proven track record collaborates with a Community of more than 5,000 data scientists and 600 PhDs. This first of a kind partnership aims to create a groundbreaking AI-driven prototype for venture capital.
VC firms often make investment decisions based on insufficient information and heavily depend on human intuition and biased decision-making.
The CrunchDAO Machine Learning Community will identify trends, relationships and hidden patterns leading to replicable, reproducible and unbiased Alpha-generating process for Venture Capital.
A world of Data: the end of serendipity and luck in early stage Venture capital.
Chance
VC make biased bets on companies with the hope of achieving substantial returns.
Data
2,500,000 Gigabytes of data created each day lead to explainable & programmable Alpha.
Early stage
Investing early yields the highest compounded returns.
Difficulties
Early stage is also the most difficult segment to invest in due to few or no data points on companies.
rarity
Only 3,5% of venture capital funds claim to leverage some data. Most of them are using the same data mainly for screening purposes.
Speed
An AI-Driven VC will invest 15x more with a predictable success rate while reaching uncrowded deals faster than the competition.
The dataset
This bounty represents the initial phase of a series of iterations, collectively known as "Rally". Each Rally features a short submission phase (up to one month), aimed at enhancing our Data-Generating Process. The steps involved in each Rally include the release of a new dataset version, model submission, out-of-sample scoring, result analysis, and the subsequent release of the next dataset version. This process will be repeated until the desired level of accuracy is achieved.
We will release only a fraction of the data for a number of reasons: first, some of the data is kept on-hold for the next "Rallies" and for out-of-sample scoring. Secondly, the size of the dataset is challenging (1.5 TB): it would require significant computing resources not readily available to most community members.
monitored
Employees
Dataset Size
Data
will you beat the whalers?
Uncertainty
Traditional Venture Capital, like the historical whaling industry, navigates high uncertainty, where success can't be predicted with certainty, akin to the unpredictable outcomes of whaling expeditions.
distribution
Both whaling and VC exhibit a 'long tail' effect in returns, where a few highly successful ventures overshadow numerous others with minimal gains or losses.
the Game
Our Goal?
Harnessing AI to Surpass Traditional Tactics and Redefine the Whaling Narrative with a purely systematic approach.
$10,000
1 month
One month to submit your best supervised machine learning model to categorize Startups that are likely to perform an upround (Pre-Seed to Seed, to Serie A, to Serie B, to Serie C etc.)
Problem statement
We categorized startups to enable 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.
F1-Score
To assess the AI's performance effectively, we will compute its F1 score. 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.
"We used to have coffees on University road in Palo Alto with entrepreneurs, make a deal around a cappuccino..."