Description

Synnax is proud to present the evolution of financial indicators modeling task with introduction of Datathon 2! The task type is identical to the previous competition, only this time we have restructured the comanies data adding updated statistics and enriched the training and testing datasets with an array of macroeconomical indicators parsed for each specific time period, relative to the reporting periods of each individual company (row) in the dataset.

In this competition, Synnax challenges participants to predict the financial metrics for the most recent quarter reported for each company. The dataset categorizes each quarter with 143 financial components, such as TOTAL ASSETS, TOTAL LIABILITIES, EBITDA, along with companies metadata and a rich set of macroeconomical indicators. Features names are encrypted for security. Each quarter is prefixed (Q_0, Q_1, …, Q_4), where _0 denotes the latest quarter in the dataset. The target variables, which were extracted from the Q_0 statistics, are found in the targets_train.csv file.

The Synnax Datathon 2 task is a highly creative endeavor that can be tackled as a conventional machine learning problem. Participants will train models using a variety of features to predict specific targets. Our dataset offers comprehensive financial performance statistics for each company, derived from components of income statements and balance sheets over the last five quarterly and four annual reports. Additional metadata, such as industry, sector, country, and city, provide further insight into the companies' characteristics.

Contestants may also opt for a time series approach, utilizing values from previous quarters to predict future financial outcomes.

Join the competition right now by messaging our Telegram Bot!

Competition Timeframe

  • Start date: 26th June 2024

  • End date: 12.00pm (noon) UTC 25th July 2024

Important note: Synnax has innovated a novel method to assess the probability of default among private companies in the web3 domain, leveraging distributed machine learning efforts from numerous individual contributors. This competition serves as a precursor to the production phase, acquainting participants with the type of data they will encounter and helping to expand our community. Synnax will invite all top-performing participants to join our production pipeline, which includes opportunities for periodic profit sharing.

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