Computer Supported Social Networking For Augmenting Cooperation
Computer Supported Cooperative Work
Stock Selection Using Rule Induction
IEEE Expert: Intelligent Systems and Their Applications
Recommending collaboration with social networks: a comparative evaluation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
NetDriller: A Powerful Social Network Analysis Tool
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
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Making investment decision on various available stocks in the market is a challenging task. Econometric and statistical models, as well as machine learning and data mining techniques, have proposed heuristic based solutions with limited long-range success. In practice, the capabilities and intelligence of financial experts is required to build a managed portfolio of stocks. However, for non-professional investors, it is too complicated to make subjective judgments on available stocks and thus they might be interested to follow an expert's investment decision. For this purpose, it is critical to find an expert with similar investment preferences. In this work, we propose to benefit from the power of Social Network Analysis in this domain. We first build a social network of financial experts based on their publicly available portfolios. This social network is then used for further analysis to recommend an appropriate managed portfolio to non-professional investors based on their behavioral similarities to the expert investors. This approach is evaluated through a case study on real portfolios. The result shows that the proposed portfolio recommendation approach works well in terms of Sharpe ratio as the portfolio performance metric.