Sold by: Brain
Deployed on AWS
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.
Overview
Brain Machine Learning Stock Ranking
Overview
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.
Historical Trial
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.
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Vendor refund policy
No refunds are offered for this product, for more information please contact us at support@braincompany.co
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Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Additional details
You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
Brain Machine Learning Stock Ranking (Historical Trial) | All historical revisions | All future revisions | Not included | Not included |
Resources
Vendor resources
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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.
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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.
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