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Finding technical trading rules in h...

Liu, Cheng.

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Finding technical trading rules in high-frequency data by using genetic programming.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Finding technical trading rules in high-frequency data by using genetic programming./
Author:	Liu, Cheng.
Description:	27 p.
Notes:	Source: Masters Abstracts International, Volume: 54-01.
Contained By:	Masters Abstracts International54-01(E).
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1568873
ISBN:	9781321330380

Finding technical trading rules in high-frequency data by using genetic programming.
Liu, Cheng.

Finding technical trading rules in high-frequency data by using genetic programming. - 27 p.

Source: Masters Abstracts International, Volume: 54-01.

Thesis (M.S.)--University of Southern California, 2014.

This item must not be sold to any third party vendors.

I use genetic programming to find technical trading rules of S&P 500 index, using one-minute high frequency intraday data during about one and half year. The model in this paper also considers short sell when necessary. Without or with very low transaction fee, the model finds several rules that provide positive excess return, i.e. return over return of passive strategy (buy and hold). While when the transaction cost is high enough, there is no rule that can generate positive excess return. And when transaction cost is greater, it is very hard to apply the model to high frequency data.

ISBN: 9781321330380Subjects--Topical Terms:

515831
Mathematics.

Finding technical trading rules in high-frequency data by using genetic programming.
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020 $a 9781321330380
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035 $a AAI1568873
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100 1 $a Liu, Cheng. $3 3168442
245 1 0 $a Finding technical trading rules in high-frequency data by using genetic programming.
300 $a 27 p.
500 $a Source: Masters Abstracts International, Volume: 54-01.
500 $a Includes supplementary digital materials.
500 $a Adviser: Sergey Lototsky.
502 $a Thesis (M.S.)--University of Southern California, 2014.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a I use genetic programming to find technical trading rules of S&P 500 index, using one-minute high frequency intraday data during about one and half year. The model in this paper also considers short sell when necessary. Without or with very low transaction fee, the model finds several rules that provide positive excess return, i.e. return over return of passive strategy (buy and hold). While when the transaction cost is high enough, there is no rule that can generate positive excess return. And when transaction cost is greater, it is very hard to apply the model to high frequency data.
590 $a School code: 0208.
650 4 $a Mathematics. $3 515831
650 4 $a Economics, Finance. $3 626650
650 4 $a Computer Science. $3 626642
690 $a 0405
690 $a 0508
690 $a 0984
710 2 $a University of Southern California. $b Applied Mathematics. $3 1266611
773 0 $t Masters Abstracts International $g 54-01(E).
790 $a 0208
791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1568873