Datasets
We prepared a boilerplate primer to make it easier for you to jump into action! Feel free to use it as a starting point and tinker on it to get better results!
Train Dataset
The train dataset contains financial data points for 13,104 publicly traded companies based on their quarterly and annual financial reports. This dataset is compiled using the 5 latest quarterly reports and 4 latest annual reports, and reflects financial components extracted from their corresponding balance sheet and income statements.
Test Dataset
The test dataset contains 3227 companies with their corresponding features in the same format as the train dataset.
The ordering is as follows:
Other columns represent metadata per each company.
Columns starting with:
Q_0
are only present in targets_train.csv: contain financial components for the latest (closest to today) quarterQ_1
are a part of X_train.csv and X_test.csv: contain financial components of the quarter which went beforeQ_0
Q_4
are a part of X_train.csv and X_test.csv: contain financial components of the furthest reported quarter, 4 quarters beforeQ_0
Y_0
are a part of X_train.csv and X_test.csv: contain financial components from the latest annual reportY_3
are a part of X_train.csv and X_test.csv: contain financial components from the furthest annual report, 3 years beforeY_0
Each quarter and each year (except for Q_0
) contains 146 financial components, please refer to the data_dictionary.txt for details.
There are 16 targets (train_targets.csv) which represent the latest financial data points for each company. Participants need to train model(s) which will map the historical financial performance of the companies (X_train.csv) to their latest financial indicators.
Files
X_train.csv - training features
targets_train.csv - training targets
X_test.csv - testing features
sample_submission.csv - a sample submission file in the correct format
data_dictionary.txt - detailed data points description
Please refer to data_dictionary.txt for detailed columns description.
Last updated