ADIA Lab Market Prediction Competition
  • The tournament
    • Overview
    • Timeline
    • Evaluation
    • Data
    • Prize
  • Requirements
    • Code Requirements
    • The Submission Code Interface
    • Resource Limit
    • Whitelisted Libraries
  • Participate
    • How to participate
    • Create an account
    • Setup
    • Your working directory
    • A basic but functional solution
    • Testing your code
    • Submitting
    • Get a score on the leaderboard
    • Run in the Cloud Environment
    • Monitoring your Cloud Runs
    • Out-of-Sample Submission Selection
    • Out-of-Sample Evaluation Phase
    • Advanced Material
    • Known Issues
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On this page
  • The objective of the competition
  • The scoring metric
  1. The tournament

Evaluation

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Last updated 1 year ago

The objective of the competition

The goal of the participant is to rank the target variable for each stock in the Adia Lab investment universe, from the highest to the lowest, at each given date.

This doesn't require estimating the exact target value for each investment; rather, it involves identifying which investments are likely to perform better than others. Participants can obtain this information from the various features (or Xs) describing each investment at each date in the provided dataset. The features' meanings are unknown to both CrunchDAO and the participants to prevent bias and facilitate sharing of the anonymized dataset.

The scoring metric

This competition is evaluated on .

Each row in the test set represents the predictions (X) associated with a stock of the universe at a given date and its target (Y).

rs=ρR⁡(X),R⁡(Y)=cov⁡(R⁡(X),R⁡(Y))σR⁡(X)σR⁡(Y)r_{s}=\rho _{\operatorname {R} (X),\operatorname {R} (Y)}={\frac {\operatorname {cov} (\operatorname {R} (X),\operatorname {R} (Y))}{\sigma _{\operatorname {R} (X)}\sigma _{\operatorname {R} (Y)}}}rs​=ρR(X),R(Y)​=σR(X)​σR(Y)​cov(R(X),R(Y))​

Where:

  • ρR(X),R(Y)\rho_{R(X),R(Y)}ρR(X),R(Y)​ denotes the usual , but applied to the ranked variables XXX and YYY;

  • cov⁡(R(X),R(Y))\operatorname {cov}(R(X), R(Y))cov(R(X),R(Y)) is the of the ranked variables;

  • σR\sigma_RσR​ are the of the ranked variables.

Spearman Rank Correlation
Pearson correlation coefficient
covariance
standard deviations