Risk profile problem for stock portfolio optimization

Ming Yang Kao, Andreas Nolte, Stephen R. Tate

Research output: Contribution to journalArticlepeer-review


In this paper we study the problem of determining an optimal investment strategy for investors with different attitudes toward the trade-offs of risk and profit. The probability distribution of the return values of the stocks that are considered by the investor are assumed to be known, while the joint distribution is unknown. The problem is to find the best investment strategy in order to minimize the probability of losing a certain percentage of the invested capital based on different attitudes of the investors toward future outcomes of the stock market. We show that for portfolios made up of two stocks, we can exactly and quickly solve the problem of finding an optimal portfolio for aggressive or risk-averse investors, using an algorithm based on a fast greedy solution to a maximum flow problem. However, an investor looking for an average-case guarantee (so is neither aggressive or risk-averse) must deal with a more difficult problem. In particular, we show that computing the distribution function associated with the average-case bound is #P-complete. On the positive side, we show how to use random sampling techniques similar to those for high-dimensional volume estimation to provide approximate answers. When k>2 stocks are considered, we show that a simple solution based on the same flow concepts as our 2-stock algorithm would imply that P = NP, so is highly unlikely. We give approximation algorithms for this case as well as exact algorithms for some important special cases.

Original languageEnglish (US)
Pages (from-to)228-234
Number of pages7
JournalConference Proceedings of the Annual ACM Symposium on Theory of Computing
StatePublished - Dec 3 2000

ASJC Scopus subject areas

  • Software

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