Brain Machine Learning proprietary platform is exploited to generate a daily stock ranking based on the predicted future returns of a universe of 1000 stocks on five time horizons: 2,3, 5, 10 and 21 days (other time horizons could be developed and tested upon request). The model implements specific machine learning techniques to combine a variety of features with a series of techniques aimed at mitigating the well-known overfitting problem for financial data with a low signal to noise ratio.
Brain Machine Learning proprietary platform is exploited to generate a daily stock ranking based on the predicted future returns of a universe of 1000 stocks on five time horizons: 2,3, 5, 10 and 21 days (other time horizons could be developed and tested upon request).
The model implements a voting scheme of machine learning classifiers that non linearly combine a variety of features with a series of techniques aimed at mitigating the well-known overfitting problem for financial data with a low signal to noise ratio.
Highlights
Enhance quantitative models and long short strategies by adding a multi-factor ranking that non-linearly combines stock specific market data (price, volume, fundamentals) with market regimes indicators and calendar anomalies using advanced Machine Learning techniques.
Rely on a robust Machine Learning approach that incorporates a series of techniques to mitigate the overfitting problem that often plagues financial data with a low signal to noise ratio. The model uses a dynamic universe that is rebalanced each year to avoid survivorship bias.
Discover signal value and explore model data with free trial access to approximately 10 years of daily rankings history.
Feed Details
Brain Machine Learning Stock Rankings are generated daily and based on the predicted future returns of a dynamic universe of the largest 1,000 US stocks across five time horizons: 2,3, 5, 10 and 21 days. The universe is updated yearly.
Model inputs include stock specific features such as fundamentals and price-volume related metrics, market data such as volatility and other financial stress indicators, and calendar related signals such as day or month anomalies.
The dataset contains historical data from January 2010 that can be freely accessed for 2 months. For a live feed please contact us at support@braincompany.co and we will make accessible a customized version of the product on AWS Data Exchange according to Client requirements.
Disclaimer
The content of this dataset is not to be intended as investment advice.
The material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provvaluee investment advisory or other services by Brain.
Brain makes no guarantees regarding the accuracy and completeness of the information expressed in the dataset.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
This listing has one pricing dimension: Product Access, measured in units, offered at no cost as a trial. You subscribe once to gain access, with no tiered or usage-based charges. The trial gives you daily stock ranking data covering the largest US and European stocks across multiple time horizons. More than 10 years of history are available for testing, delivered through a dedicated S3 bucket. Since this is a single free access dimension, there are no add-ons, quantity tiers, or scaling charges to weigh. To move beyond the trial, you contact the vendor.
Top-of-mind questions for buyers
What does one unit of Product Access grant me during the trial?
One unit gives you subscriber access to the daily stock ranking dataset. You receive daily rankings for the largest US and European stocks across time horizons of 2, 3, 5, 10, and 21 days. The unit is access itself, not a per-stock or per-record charge.
How is the ranking data delivered to me under this access?
The data is delivered at daily frequency through a dedicated S3 bucket. You access the files there once subscribed. More than 10 years of history are available for testing, so you can evaluate the rankings across past market conditions.
Does my cost change as I access more data or more time horizons?
No. This listing has a single free access dimension with no usage-based or tiered charges. Accessing more history, more stocks, or additional time horizons does not add cost. To move beyond the trial data, you contact the vendor directly.
braincompany.co
Helpful?
Vendor refund policy
No refunds are offered for this product, for more information please contact us at support@braincompany.co
How can we make this page better?
Tell us how we can improve this page, or report an issue with this product.
Give us feedbackReport a problem with this product or seller
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA).
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Brain Machine Learning proprietary platform is exploited to generate a daily stock ranking based on the **predicted future returns for the following 10 trading days** for a universe of the largest 1000 US stocks.
The model implements a voting scheme of machine learning classifiers that non linearly combine a variety of features with a series of techniques aimed at mitigating the well-known overfitting problem for financial data with a low signal to noise ratio.
Brain Machine Learning proprietary platform is exploited to generate a daily stock ranking based on the **predicted future returns for the following 21 trading days** for a universe of the largest 1000 US stocks.
The model implements a voting scheme of machine learning classifiers that non linearly combine a variety of features with a series of techniques aimed at mitigating the well-known overfitting problem for financial data with a low signal to noise ratio.
Brain Machine Learning proprietary platform is exploited to generate a daily stock ranking based on the **predicted future returns for the following 5 trading days** for a universe of the largest 1000 US stocks.
The model implements a voting scheme of machine learning classifiers that non linearly combine a variety of features with a series of techniques aimed at mitigating the well-known overfitting problem for financial data with a low signal to noise ratio.
Brain Machine Learning proprietary platform is exploited to generate a daily stock ranking based on the **predicted future returns for the following 2 trading days** for a universe of the largest 1000 US stocks.
The model implements a voting scheme of machine learning classifiers that non linearly combine a variety of features with a series of techniques aimed at mitigating the well-known overfitting problem for financial data with a low signal to noise ratio.