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Type of Document	Master's Thesis
Author	Pratap, Amrit
URN	etd-05272004-115820
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-05272004-115820
Title	Maximum drawdown of a Brownian motion and AlphaBoost: a boosting algorithm
Degree	Master of Science
Option	Computer Science
Advisory Committee	Advisor Name Title Yaser Abu-Mostafa Committee Chair
Keywords	maximum drawdown sterling ratio boosting machine learning computational finance
Date of Defense	2004-05-28
Availability	unrestricted
Abstract We study two problems, one in the field of computational finance and the other one in machine learning. Firstly we study the Maximal drawdown statistics of the Brownian random walk. We give the infinite series representation of its distribution and consider its expected value. For the case when drift is zero, we give an exact expression of the expected value and for the other cases, we give an infinite series representation. For all the cases, we compute the limiting behavior of the expected value. Secondly, we propose a new algorithm for boosting, AlphaBoost, which does better than AdaBoost in reducing the cost function. We study its generalization properties and compare it to AdaBoost. However, this algorithm does not always give better out-of-sample performance.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access    thesis.pdf 569.05 Kb 00:02:38 00:01:21 00:01:11 00:00:35 00:00:03   thesis.ps.gz 248.92 Kb 00:01:09 00:00:35 00:00:31 00:00:15 00:00:01