The present invention describes a novel system and method of aggregating and predicting stock rankings. A financial data model based on a neighborhood model, this invention allows users to predict the trend of a continuous time series, given the knowledge of other similar time series. It also solves another proximal problem of Rank Aggregation, which is, given a set of rankings based on some parameters, to come up with an optimal ranking that procures the earning capability of a ticker as the primary pivot. To achieve this, each ticker is projected as a point on a high dimensional space.
The system and method then uses a ranking optimization method to predict the ranking of each stock based on percentage change in price. The current invention facilitates investors trading by using a novel methodology to predict stock rankings and providing a neighborhood of related stocks, while having an easy to use interface.
Advantages:
- Ranks stock tickers registered at NASDAQ based on different market parameters, or within a given a sector, or as charts where instead of the price of a ticker its rank is shown at different hours of the day
- Predicts pricing trends
- Provides recommendation based on portfolio and budget, and short term prediction with reason (i.e. why we have put ticker X at rank 1)
- The entire web interface (including the visualizations) will be implemented using HTML5/CSS3 so that it stays accessible from any mobile device (including Apple devices)
