Competitive solutions for online financial problems
ACM Computing Surveys (CSUR)
A competitive analysis of the list update problem with lookahead
Theoretical Computer Science
An improvement on El-Yaniv-Fiat-Karp-Turpin's money-making bi-directional trading strategy
Information Processing Letters
Optimal Buy-and-Hold Strategies for Financial Markets with Bounded Daily Returns
SIAM Journal on Computing
SIAM Journal on Computing
On-Line Algorithms Versus Off-Line Algorithms: How Much is it Worth to Know the Future?
Proceedings of the IFIP 12th World Computer Congress on Algorithms, Software, Architecture - Information Processing '92, Volume 1 - Volume I
Online Routing Problems: Value of Advanced Information as Improved Competitive Ratios
Transportation Science
Competitive analysis of financial games
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
Online auctions and generalized secretary problems
ACM SIGecom Exchanges
On the power of lookahead in on-line server routing problems
Theoretical Computer Science
Optimal Algorithms for k-Search with Application in Option Pricing
Algorithmica - Special Issue: European Symposium on Algorithms 2007, Guest Editors: Larse Arge and Emo Welzl
Online Search with Time-Varying Price Bounds
Algorithmica
Can we learn to beat the best stock
Journal of Artificial Intelligence Research
Competitive analysis of on-line securities investment
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
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We answer this question using the competitive ratio as an indicator for the quality of information about the future. Analytical results show that the better the information the better the worst-case competitive ratios. However, experimental analysis gives a slightly different view. We calculate the empirical-case competitive ratios of different variants of a threat-based online algorithm. The results are based on historical data of the German Dax-30 index. We compare our experimental empirical-case results to the analytical worst-case results given in the literature. We show that better information does not always lead to a better performance in real life applications. The empirical-case competitive ratio is not always better with better information, and some a-priori information is more valuable than other for practical settings.