Prediction of exchange rates with machine learning

Ahmet Goncu

doi:10.1145/3371425.3371448

Prediction of exchange rates with machine learning

Ahmet Goncu^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019
Editors	Joao Manuel R.S. Tavares
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450376334
DOIs	https://doi.org/10.1145/3371425.3371448
Publication status	Published - 19 Dec 2019
Externally published	Yes
Event	2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019 - Sanya, China Duration: 19 Dec 2019 → 21 Dec 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2019
Country/Territory	China
City	Sanya
Period	19/12/19 → 21/12/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

Foreign exchange rates
Machine learning
Regression estimation

Access to Document

10.1145/3371425.3371448

Cite this

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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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this