Analysis of associativity among mirror neurons for financial profiling

Abstract

Mirror neurons, observed first in macaque monkeys, are neurons which fire not only on the performance of an action but also during the perception of the same action by some being. This paper presents the application of the concept of mirror neurons in financial profiling. In addition, this concept has been extended to establish associativity among the mirror neurons. This financial application makes use of stock market data from the official Bombay Stock Exchange (BSE) Web site and uses the concepts of artificial neural networks, hierarchical agglomerative clustering, and dimensionality reduction for implementation. The performance of this system has been established using the concept of root mean square error.