Journal on Policy and Complex Systems - Fall 2015 , Volume 2 , Number 2
Enhancing Stock Investment Returns with Learning Aggressiveness and Trust Metrics
Zheyuan Su and Mirsad Hadzikadic
Complex Systems Institute , College of Computing and Informatics , University of North Carolina at Charlotte , Charlotte , NC 28223 , USA
Email : zsu2 @ uncc . edu and mirsad @ uncc . edu
Abstract : Trust metrics and learning can be extremely useful in enhancing the overall investment returns . A trust metric is an indication of the degree to which one social actor trusts another , while aggressiveness in learning determines the degree to which one trader decides to mimic another . This paper introduces an agent-based model for finding the optimal level of aggressiveness in learning and the optimal degree of trust in order to optimize the stock-trading returns . The system has been evaluated in the context of the Bank of America stock and S & P 500 performance in the period of 1987 – 2014 . The model significantly outperformed the buy-and-hold strategy on both S & P 500 and the Bank of America stock . In addition , the model can provide relevant information to policy maker regarding interest rate setting and expected investor behaviors .
Keywords : complex adaptive systems , investment , trust metrics , learning aggressiveness
1 . Introduction
Finding best stock investment strategies requires not only rational trading
rules practices but also the faith that market information is reliable . A trust metric is an indication of the degree to which one social actor trusts another . It is hard to pick the right timing for stock selection or to take the correct position in the stock market , because stock intrinsic values are affected by both endogenous and exogenous phenomena . Some investors actually make profit by taking advantage of the asymmetric property of information , which is what happens when one party in a transaction has better information than the other party does . However , actions taken based on asymmetric information are reflected in the
49 doi : 10.18278 / jpcs . 2.2.4