Hedge Fund Activism is a proactive investment strategy to earn better returns in stock price through involving the corporate governance of target firms . Annual profits generated from this approach are far better than that from the traditional “buy and wait” approach. Due to this reason, more and more hedge fund companies are coming to engage in this business. However, this approach involves complicated multi-criteria decision making processes. Target firm selection is the first critical decision that fund managers need to make. According to the financial literatures, most fund managers picking targets mainly based on their past experience which is quite subjective. After selecting a list of target firms, fund managers have to decide which action they would take to push the stock price in a least costly way. This is evident that IS can help, but no IS researcher has worked on this before. In this study, we focus on the target selection process only and aim to use Bayesian Networks to support this complicated and uncertain decision making process. Since fund managers have to evaluate and select target firms by conveying necessary uncertain information among other fund managers, a multi-agent system is constructed to present that scenario.