Stock Investment Decision Making: A Social Network Approach

Abstract

The realm of the stock market has always been appealing to individuals because of its beneficial potential. Finding an appropriate set of stocks for investment to ultimately gain more return and face less risk, compared to other selections, attracts many people, whether domain-experts or not. There exist several financial theories and approaches that deal with the issue of return and risk. However, a significant obstacle, which still remains, is to apply those theories in the real world since it is sometimes unattainable task to complete. To cope with this impediment, machine learning and data mining techniques have been utilized, and their notable power has thoroughly been proven. In this paper, we introduce an automated system, which collects information about the history of stocks in the market and suggests particular stocks to invest in. We argue that stocks do social by having the relationships and connections between them influenced by external factors mostly. In other words, the stocks are actors that dynamically change camps and socialize based on the situation of the company, the news, the market status, the economy, etc. Utilizing social network theory and analysis, we first build the network of stocks in the market, and then cluster stocks into distinct groups according to the similarities of their return trends, in order to comply with diversification strategy. This allows us to propose stocks from different clusters to individuals. To examine the effectiveness of the proposed approach, we conducted experiments on stocks of the S&P 500 market by constructing portfolios for our selected stocks as well as for a well-known benchmark in the area. The result of this study shows that the proposed portfolios have higher Sharpe Ratio compared to the benchmark.