Comparative Analysis Based on Machine Learning Model Predicting Exchange Rate Fluctuations

Ziqian Niu ()
Additional contact information
Ziqian Niu: Massey University, College of Information Science

A chapter in Proceedings of the 2024 2nd International Conference on Management Innovation and Economy Development (MIED 2024), 2024, pp 471-480 from Springer

Abstract: Abstract The foreign exchange market has long been a focal point in the financial sector, attracting considerable attention. On a macro level, accurately predicting exchange rate trends is crucial for shaping effective economic and financial policies. For micro-entities, effectively managing and mitigating foreign exchange risks is a key challenge. Thus, forecasting foreign exchange trends holds significant importance across various fields. This article will use the method of model comparison to bring actual data to analyze the three models in the field of foreign exchange forecasting. The three different machine learning models, namely the Linear Regression Model (LSTM), Random Forest Model, and Long Short-term Memory Model, are widely used in the field of artificial intelligence, to predict the foreign exchange rate in a certain period. And choose the best foreign exchange prediction model while comparing their respective advantages. From the perspective of model prediction performance on the same set of foreign exchange data, the LSTM model became the optimal prediction model with the smallest mean square error (2.54e-05) among the three models.

Keywords: Machine learning; exchange rate; LSTM Model; Random Forest Model; Linear Regression Model (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-542-3_55

Ordering information: This item can be ordered from
http://www.springer.com/9789464635423

DOI: 10.2991/978-94-6463-542-3_55

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().