Volume 15, Number 2
Optimizing Sector Index Rotation and Rebalancing Frequency with Data Mining: A Case Study on Indian National Stock Exchange
Authors
Tanvi Sharma, Akarsh Srivastava and Eugene Pinsky, Boston University, USA
Abstract
This paper introduces a data-driven investment strategy that leverages data mining techniques to streamline portfolio allocation. Using historical performance data from 15 sectoral indices of the National Stock Exchange of India, the study applies periodic ranking and clustering methods to identify high-performing indices. At predefined intervals—annually, semiannually, quarterly, and monthly—indexes are re-ranked based on historical returns and reassigned to new groups using pattern recognition techniques. An initial investment of $100 is distributed in three dynamically formed groups, each comprising five indices. Returns within each group are reinvested in the same group for subsequent periods, ensuring systematic portfolio evolution. By integrating data mining principles such as ranking algorithms and periodic reassignment, this strategy offers an intuitive yet computationally efficient approach to portfolio management, demonstrating how financial decision making can be optimized through data-driven insights.
Keywords
sector rotation, trading strategy, financial data mining .