Abstract: The classical uni-directional conversion algorithms are based on the assumption that prices are arbitrarily chosen from the fixed price interval [m,M] where m and M represent the estimated lower and upper bounds of possible prices 0 < m <= M. The estimated interval is erroneous and no attempts are made by the algorithms to update the erroneous estimates. We consider a real world setting where prices are interrelated, i.e., each price depends on its preceding price. Under this assumption, we derive a lower bound on the competitive ratio of randomized non-preemptive algorithms. Motivated by the fixed and erroneous price bounds, we present an update model that progressively improves the bounds. Based on the update model, we propose a non-preemptive reservation price algorithm RP* and analyze it under competitive analysis. Finally, we report the findings of an experimental study that is conducted over the real world stock index data. We observe that RP* consistently outperforms the classical algorithm.

Javeria Iqbal, Iftikhar Ahmad and Asadullah Shah, “Competitive Algorithms for Online Conversion Problem with Interrelated Prices” International Journal of Advanced Computer Science and Applications(IJACSA), 10(6), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100675

@article{Iqbal2019,
title = {Competitive Algorithms for Online Conversion Problem with Interrelated Prices},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0100675},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100675},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Javeria Iqbal and Iftikhar Ahmad and Asadullah Shah},
}