Change search
ReferencesLink to record
Permanent link

Direct link
Predicting High Frequency Exchange Rates using Machine Learning
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Att förutsäga högfrekventa växelkurser med maskinlärning (Swedish)
Abstract [en]

This thesis applies a committee of Artificial Neural Networks and Support Vector Machines on high-dimensional, high-frequency EUR/USD exchange rate data in an effort to predict directional market movements on up to a 60 second prediction horizon. The study shows that combining multiple classifiers into a committee produces improved precision relative to the best individual committee members and outperforms previously reported results. A trading simulation implementing the committee classifier yields promising results and highlights the possibility of developing a profitable trading strategy based on the limit order book and historical transactions alone.

Abstract [sv]

Denna uppsats tillämpar en kommitté av artificiella neuronnät och stödvektormaskiner på hög-dimensionell, högfrekvent EUR/USD växelkursdata i ett försök att förutsäga marknadsriktning på en upp till 60 sekunders tidshorisont. Studien visar att en kommitté bestående av flera klassificerare ger bättre precision än de bästa enskilda kommittémedlemmarna och överträffar tidigare rapporterade resultat. En handelssimulering där kommittén tillämpas ger lovande resultat och framhåller möjligheten att utveckla en lönsam handelsstrategi baserad på enbart limit order book och historiska transaktioner.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2016:19
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-187753OAI: diva2:932585
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Available from: 2016-06-08 Created: 2016-05-29 Last updated: 2016-06-08Bibliographically approved

Open Access in DiVA

fulltext(1698 kB)1385 downloads
File information
File name FULLTEXT02.pdfFile size 1698 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Mathematical Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 1385 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 332 hits
ReferencesLink to record
Permanent link

Direct link