In general, previous studies[1], [2]mostly focus on helping customers find a set of “best” possible products from a pool of given products. Finding top-k profitable products is common in many real-life applications like finding profitable laptops in a new laptop company, finding profitable delivery services in a new cargo delivery company, finding profitable shares in stock market and e-advertisements in a web page etc. This paper we propose a solution to a real life application by identifying top-k profitable stocks, since has not been studied before is undertaken. Given a set of stocks in the existing market, a set of k “best” possible stocks are found such that these new stocks are not dominated by the stocks in the existing stock market. Hence, the user can decide which stocks to be bought for making better profit. Two problem instances of finding top-k profitable stocks are found addressed in this paper. An extensive performance study using both synthetic and real data sets is reported to verify the effectiveness and efficiency of proposed algorithms.