Abstract
In a classical k-min search problem, an online player wants to buy k units of an asset with the objective of minimizing the total buying cost. The problem setting allows the online player to view only a single price quotation at each time step. A price quotation is the price of one unit of an asset. After receiving the price quotation, the online player has to decide on the number of units to buy. The objective of the online player is to buy the required k units in a fixed length investment horizon. Online algorithms are proposed in the literature for k-min search problem; however, these algorithms are risk averse in nature. We propose a risk aware k-min search algorithm which allows the online player to manage her risk level. The proposed algorithm is evaluated against the benchmark algorithm based on a real-world scenario using DAX30 data set. The proposed algorithm achieved up to 36.67% better results than the corresponding benchmark algorithm.