{
  "fetched_at": "2026-10-20T00:26:30Z",
  "source_url": "https://www.kaggle.com/competitions/become-a-kaggle-master-iasd-hw2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "We provide four files\n\n## Files\n\n*   **train.csv** - the training set. It contains features (columns 1 to 22 corresponding to the return of the 22 factors) + the expected return (column called FundReturn)\n*   **test.csv** - the test set containing only the returns of the 22 factors\n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **column_description.csv** - a file giving the description of each column",
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    {
      "name": "Description",
      "content": "# Context\nThis is homework 2 of the course <a href=\"https://www.lamsade.dauphine.fr/~ebenhamou/beccome_a_kaggle_master/index.html\">Become a Kaggle Master</a>\nThis course is part of the <a href=\"https://dauphine.psl.eu/en/training/masters-degrees/computer-science/m2-artificial-intelligence-systems-data\">IASD Master 2 in Machine Learning, Artificial Intelligence, and Data Science</a> program, jointly run by <a href=\"https://dauphine.psl.eu/en/\">Université Paris Dauphine</a>, <a href=\"https://www.ens.psl.eu/en\"> ENS</a>  and <a href=\"https://mines-paristech.eu/\"> Mines ParisTech</a>.\n\n# Goal\nThe competition's goal is to decode a financial strategy thanks to an investment universe. This strategy is the aggregated performance of hedge funds performances. To decode this strategy, you are asked to infer each day the percentage invested in 22 factors and provide the corresponding predicted returns. The investable factors are:\n- 6 equity factors: US, Japan, Europe, UK, and Emerging markets\n- 7 interest rates factors: European and US High yield bonds, European and US Investment grade bonds indexes and 1 emerging market bond index\n- 6 foreign exchange factors representing exposure to dollar\n- 3 commodity indexes",
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    },
    {
      "name": "Evaluation",
      "content": "The evaluation is the RMSE (mean columnwise root mean squared error) as described in <a href=\"https://stats.stackexchange.com/questions/487038/what-is-mcrmse-mean-columnwise-root-mean-squared-error\">stackexchange</a>\n\n\nYou are asked to provide in your submission **2 columns**\n- **Date**: the date for the predicted return\n- **FundReturn**: the predicted return of your model\n\n**Make sure you have all columns provided as a submission not respecting this format will be considered invalid**",
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      "hasIsPublished": false,
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    },
    {
      "name": "rules",
      "content": "You cannot use external data.",
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      "hasIsPublished": false,
      "postTitle": "",
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      "mimeType": "",
      "hasMimeType": false
    }
  ]
}