Name
Abstract | ||
|---|---|---|
This paper extends the game-theoretic notion of internal regret to the case of on-line potfolio selection problems. New sequential investment strategies are designed to minimize the cumulative internal regret for all possible market behaviors. Some of the introduced strategies, apart from achieving a small internal regret, achieve an accumulated wealth almost as large as that of the best constantly rebalanced portfolio. It is argued that the low-internal-regret property is related to stability and experiments on real stock exchange data demonstrate that the new strategies achieve better returns compared to some known algorithms. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/s10994-005-0465-4 | Machine Learning |
Keywords | Field | DocType |
individual sequences,internal regret,on-line investment,universal Portfolio,EG strategy | Mathematical optimization,Regret,Computer science,Investment strategy,Inversion (meteorology),Project portfolio management,Stock exchange,Portfolio,Game theory | Journal |
Volume | Issue | ISSN |
59 | 1-2 | 0885-6125 |
Citations | PageRank | References |
20 | 1.63 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gilles Stoltz | 1 | 351 | 31.53 |
| GáBor Lugosi | 2 | 1092 | 195.02 |