The key to accuracy in supervised machine learning models is the creation of an adequate training dataset. Several datasets were tested and the optimal dataset chosen after benchmarking the accuracy of the model.
Stocksnips identifies key financial sentiment features based on n-grams and computes their weights based on an extremely fast machine learning (data mining) algorithm for solving multiclass classification problems from ultra large data sets.
Big Text Analytics and AI
Stocksnips reads millions of articles using Natural Language processing and extracts relevant financial news snippets. These are attributed to the right company and then scored by Machine Learning models that have been trained to deliver accuracy.
Automated (near) real-time news analysis further expands the universe of quantified data available to security analysts and enables a richer set of analysis for rapid and actionable changes to portfolios or trading strategies.
Stocksnips is a cloud based SAAS service for institutional and retail investors. Technology comprises a highly streamlined and efficient pipeline to process large volume of unstructured data on a continuous real time basis using NoSQL and an extensible database design that can handle any number of new countries and sources, on a scalable AWS server infrastructure, with high performance APIs for client access.