Prediction of exchange rates with machine learning

Ahmet Goncu

doi:10.1145/3371425.3371448

Prediction of exchange rates with machine learning

Ahmet Goncu^*

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

5 Atıf (Scopus)

Özet

In this study a macroeconomic model is considered to predict the next month’s monthly average exchange rates via machine learning based regression methods including the Ridge, decision tree regression, support vector regression and linear regression. The model incorporates the domestic money supply, real interest rates, Federal Funds rate of the USA, and the last month’s monthly average exchange rate to predict the next month’s exchange rate. Monthly data with 148 observations from the US Dollar and Turkish Lira exchange rates are considered for the empirical testing of the model. Empirical results show that the Ridge regression offers accurate estimation for investors or policy makers with relative errors less than 60 basis points. Policy makers can obtain point estimates and confidence intervals for analyzing the effects of interest rate cuts on the exchange rates.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings of 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019
Editörler	Joao Manuel R.S. Tavares
Yayınlayan	Association for Computing Machinery
ISBN (Elektronik)	9781450376334
DOI'lar	https://doi.org/10.1145/3371425.3371448
Yayın durumu	Yayınlandı - 19 Ara 2019
Harici olarak yayınlandı	Evet
Etkinlik	2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019 - Sanya, China Süre: 19 Ara 2019 → 21 Ara 2019

Yayın serisi

Adı	ACM International Conference Proceeding Series

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019
Ülke/Bölge	China
Şehir	Sanya
Periyot	19/12/19 → 21/12/19

Bibliyografik not

Publisher Copyright:
© 2019 Association for Computing Machinery.

Finansman

Our thanks to the Research Institute of Quantitative Finance at Xi’an Jiaotong Liverpool University and the Hedge Fund Research Center at Shanghai Advanced Institute of Finance, Shanghai Jiaotong University in support of this research project.

Finansörler	Finansör numarası
Hedge Fund Research Center at Shanghai Advanced Institute of Finance
Shanghai Jiao Tong University

Belgeye Erişim

10.1145/3371425.3371448

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@inproceedings{aabfc51ebc6f4d1895e594be5580531b,

title = "Prediction of exchange rates with machine learning",

abstract = "In this study a macroeconomic model is considered to predict the next month{\textquoteright}s monthly average exchange rates via machine learning based regression methods including the Ridge, decision tree regression, support vector regression and linear regression. The model incorporates the domestic money supply, real interest rates, Federal Funds rate of the USA, and the last month{\textquoteright}s monthly average exchange rate to predict the next month{\textquoteright}s exchange rate. Monthly data with 148 observations from the US Dollar and Turkish Lira exchange rates are considered for the empirical testing of the model. Empirical results show that the Ridge regression offers accurate estimation for investors or policy makers with relative errors less than 60 basis points. Policy makers can obtain point estimates and confidence intervals for analyzing the effects of interest rate cuts on the exchange rates.",

keywords = "Foreign exchange rates, Machine learning, Regression estimation",

author = "Ahmet Goncu",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019 ; Conference date: 19-12-2019 Through 21-12-2019",

year = "2019",

month = dec,

day = "19",

doi = "10.1145/3371425.3371448",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

editor = "Tavares, {Joao Manuel R.S.}",

booktitle = "Proceedings of 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019",

}

Goncu, A 2019, Prediction of exchange rates with machine learning. in JMRS Tavares (ed.), Proceedings of 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019., a8, ACM International Conference Proceeding Series, Association for Computing Machinery, 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019, Sanya, China, 19/12/19. https://doi.org/10.1145/3371425.3371448

Prediction of exchange rates with machine learning. / Goncu, Ahmet.
Proceedings of 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019. ed. / Joao Manuel R.S. Tavares. Association for Computing Machinery, 2019. a8 (ACM International Conference Proceeding Series).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Prediction of exchange rates with machine learning

AU - Goncu, Ahmet

PY - 2019/12/19

Y1 - 2019/12/19

N2 - In this study a macroeconomic model is considered to predict the next month’s monthly average exchange rates via machine learning based regression methods including the Ridge, decision tree regression, support vector regression and linear regression. The model incorporates the domestic money supply, real interest rates, Federal Funds rate of the USA, and the last month’s monthly average exchange rate to predict the next month’s exchange rate. Monthly data with 148 observations from the US Dollar and Turkish Lira exchange rates are considered for the empirical testing of the model. Empirical results show that the Ridge regression offers accurate estimation for investors or policy makers with relative errors less than 60 basis points. Policy makers can obtain point estimates and confidence intervals for analyzing the effects of interest rate cuts on the exchange rates.

AB - In this study a macroeconomic model is considered to predict the next month’s monthly average exchange rates via machine learning based regression methods including the Ridge, decision tree regression, support vector regression and linear regression. The model incorporates the domestic money supply, real interest rates, Federal Funds rate of the USA, and the last month’s monthly average exchange rate to predict the next month’s exchange rate. Monthly data with 148 observations from the US Dollar and Turkish Lira exchange rates are considered for the empirical testing of the model. Empirical results show that the Ridge regression offers accurate estimation for investors or policy makers with relative errors less than 60 basis points. Policy makers can obtain point estimates and confidence intervals for analyzing the effects of interest rate cuts on the exchange rates.

KW - Foreign exchange rates

KW - Machine learning

KW - Regression estimation

UR - http://www.scopus.com/inward/record.url?scp=85077816179&partnerID=8YFLogxK

U2 - 10.1145/3371425.3371448

DO - 10.1145/3371425.3371448

M3 - Conference contribution

AN - SCOPUS:85077816179

T3 - ACM International Conference Proceeding Series

BT - Proceedings of 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019

A2 - Tavares, Joao Manuel R.S.

PB - Association for Computing Machinery

T2 - 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019

Y2 - 19 December 2019 through 21 December 2019

ER -

Prediction of exchange rates with machine learning

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap