Abstract
A universalization of a parameterized investment strategy is an online algorithm whose average daily performance approaches that of the strategy operating with the optimal parameters determined offline in hindsight. We present a general framework for universalizing investment strategies and discuss conditions under which investment strategies are universalizable. We present examples of common investment strategies that fit into our framework. The examples include both trading strategies that decide positions in individual stocks, and portfolio strategies that allocate wealth among multiple stocks. This work extends Cover's universal portfolio work. We also discuss the runtime efficiency of universalization algorithms. While a straightforward implementation of our algorithms runs in time exponential in the number of parameters, we show that the efficient universal portfolio computation technique of Kalai and Vempala involving the sampling of log-concave functions can be generalized to other classes of investment strategies.
| Original language | English (US) |
|---|---|
| Title of host publication | Automata, Languages and Programming - 29th International Colloquium, ICALP 2002, Proceedings |
| Pages | 888-900 |
| Number of pages | 13 |
| State | Published - Dec 1 2002 |
| Event | 29th International Colloquium on Automata, Languages, and Programming, ICALP 2002 - Malaga, Spain Duration: Jul 8 2002 → Jul 13 2002 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 2380 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 29th International Colloquium on Automata, Languages, and Programming, ICALP 2002 |
|---|---|
| Country | Spain |
| City | Malaga |
| Period | 7/8/02 → 7/13/02 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Fast universalization of investment strategies with provably good relative returns. / Akcoglu, Karhan; Drineas, Petros; Kao, Ming-Yang.
Automata, Languages and Programming - 29th International Colloquium, ICALP 2002, Proceedings. 2002. p. 888-900 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2380 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Fast universalization of investment strategies with provably good relative returns
AU - Akcoglu, Karhan
AU - Drineas, Petros
AU - Kao, Ming-Yang
PY - 2002/12/1
Y1 - 2002/12/1
N2 - A universalization of a parameterized investment strategy is an online algorithm whose average daily performance approaches that of the strategy operating with the optimal parameters determined offline in hindsight. We present a general framework for universalizing investment strategies and discuss conditions under which investment strategies are universalizable. We present examples of common investment strategies that fit into our framework. The examples include both trading strategies that decide positions in individual stocks, and portfolio strategies that allocate wealth among multiple stocks. This work extends Cover's universal portfolio work. We also discuss the runtime efficiency of universalization algorithms. While a straightforward implementation of our algorithms runs in time exponential in the number of parameters, we show that the efficient universal portfolio computation technique of Kalai and Vempala involving the sampling of log-concave functions can be generalized to other classes of investment strategies.
AB - A universalization of a parameterized investment strategy is an online algorithm whose average daily performance approaches that of the strategy operating with the optimal parameters determined offline in hindsight. We present a general framework for universalizing investment strategies and discuss conditions under which investment strategies are universalizable. We present examples of common investment strategies that fit into our framework. The examples include both trading strategies that decide positions in individual stocks, and portfolio strategies that allocate wealth among multiple stocks. This work extends Cover's universal portfolio work. We also discuss the runtime efficiency of universalization algorithms. While a straightforward implementation of our algorithms runs in time exponential in the number of parameters, we show that the efficient universal portfolio computation technique of Kalai and Vempala involving the sampling of log-concave functions can be generalized to other classes of investment strategies.
UR - http://www.scopus.com/inward/record.url?scp=49449113888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49449113888&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:49449113888
SN - 3540438645
SN - 9783540438649
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 888
EP - 900
BT - Automata, Languages and Programming - 29th International Colloquium, ICALP 2002, Proceedings
ER -