A decision support system for stock investment recommendations using collective wisdom

• Stock ratings of online stock communities have shown to provide predictive value for investment decisions.
• We develop a decision support system that enables day-to-day usage of these data for investors.
• The system supports customized metrics and decision rules based and automatic portfolio management.
• The portfolios of two test scenarios strongly outperform the market benchmark and comparable public funds, even after risk assessment..

Previous research has shown that user-generated stock votes from online communities can be valuable for investment decisions. However, to support investors on a day-to-day basis, there is a need for an efficient support system to facilitate the use of the data and to transform crowd votes into actionable investment opportunities. We propose a decision support system (DSS) design that enables investors to include the crowd's recommendations in their investment decisions and use it to manage a portfolio. A prototype with two test scenarios shows the potential of the system as the portfolios recommended by the system clearly outperform the market benchmark and comparable public funds in the observation period in terms of absolute returns and with respect to the Reward-to-Variability-Ratio.