Many people have engaged in trading of stocks or other securities, but few are aware of how the actual transaction is executed. The process of transferring the ownership of securities, often in exchange for cash, is called securities settlement. If the parties involved in the transaction have the assets that they are obligated to deliver at the time of settlement, securities settlement is straightforward. But if the securities settlement system is faced with a set of transactions, in which some party fails to meet their obligation, some transactions will fail to settle. Since the receiving party of a transactions that fails to settle may have been depending on the assets from that transaction in order to meet their own obligations, a single settlement fail can have a ripple effect causing many other transactions to fail to settle. Securities settlement optimization is the problem of finding the optimal set of transactions to settle, given the available assets.
In this thesis, we model securities settlement optimization as an integer linear programming problem and evaluate how an optimization solver software solution performs in comparison to a greedy heuristic algorithm on problem instances derived from real settlement data. We find that the solver offers a significant advantage over the heuristic algorithm in terms of reducing settlement fails at the cost of longer execution time. Furthermore, we find that even if the solver is only allowed to run for a couple of minutes it offers a significant advantage compared to the heuristic algorithm. Our conclusion is that using this type of solver for securities settlement optimization seems to be a viable approach, but that further experimentation with other data sets is needed.