[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124146-en":3,"doc-seo-124146-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124146,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Leveraging Machine Learning for Exchange Rate Prediction - A Business and Financial Management Perspective in Nigeria","Exchange rate forecasting supports foreign trade valuation, risk assessment, reserve management, and monetary policy decision-making, yet exchange rates fluctuate unpredictably and demand robust predictive methods. This study applies four machine learning algorithms to forecast Nigeria’s exchange rate against the US dollar using hourly and daily time-series data. Logistic Regression, SVM, Random Forest, and XGBoost are trained and evaluated with standard error metrics to compare forecasting accuracy. Results show Random Forest achieves the lowest prediction errors across both frequencies, while XGBoost ranks second.","Leveraging Machine Learning for Exchange Rate Prediction: A Business and Financial Management Perspective in Nigeria  \nAdedeji Daniel Gbadebo1  \n1Department of Accounting Science, Walter Sisulu University, Mthatha, South Africa,  \n[agbadebo@wsu.ac.za](agbadebo@wsu.ac.za)  \n\n| ARTICLE DETAILS | ABSTRACT |\n| --- | --- |\n| History Received:\u003Cbr>August 27, 2024\u003Cbr>Revised:\u003Cbr>November 20, 2024\u003Cbr>Accepted:\u003Cbr>December 03, 2024\u003Cbr>Published:\u003Cbr>January 01, 2025\u003Cbr>Keywords\u003Cbr>Machine Learning Logistic Regression Support Vector Machine Random Forest Xgboost Algorithms Exchange Rate\u003Cbr>This is an open-access article distributed under the Creative Commons Attribution License4.0\u003Cbr> | Purpose\u003Cbr>The continuous availability of historical data for asset prices propelled more attention of researchers to use analytical algorithms to study the evolution of prices. This paper aims to use four machine learning algorithms to forecast the exchange rates in Nigeria.\u003Cbr>Methodology\u003Cbr>The paper employs Logistic Linear Regression, Support Vector Machine, Random Forest, and XGBoost algorithms to predict the univariate time series of Nigeria's exchange rate against the US dollar, using both hourly and daily data.\u003Cbr>Findings\u003Cbr>The findings indicate that the Random Forest (RF) model outperforms other approaches in predicting Nigeria’s exchange rate against the US dollar, demonstrating the lowest prediction errors (MAE, MSE, RMSE, and MAPE) . RF remains the most accurate model across both hourly and daily frequencies, with XGBoost emerging as the second-best performer.\u003Cbr>Conclusions\u003Cbr>This study applies machine learning models to enhance exchange rate prediction, demonstrating that the exchange rate series is not sensitive to data periodicity. The findings provide valuable insights for stakeholders in the foreign exchange market, aiding policymakers in selecting the most accurate forecasting techniques. |\n\nCorresponding author’s email address: [agbadebo@wsu.ac.za](agbadebo@wsu.ac.za)  \n1. Introduction  \nThe exchange rate plays a crucial role in determining the value of foreign trade (Nag & Mitra, 2002). Along with other economic indicators such as money supply, consumer price index, interest rates, and inflation, the currency exchange rate is one of the most significant determinants ofa country's overall economic health. Due to continuous demand pressures, exchange rates often fluctuate, leading to economic implications, including drastic effects on the prices of goods. Exchange rate volatility influences the growth of international trade markets, impacting economies worldwide. Additionally, exchange rates assist both private and government-owned businesses in evaluating risk and maximizing returns.  \nThe literature identifies several factors that contribute to sudden fluctuations in exchange rates. Similar to stocks and other financial assets, exchange rates respond to macroeconomic factors and monetary policies (Hausman & Wongswan, 2011; Chatrath et al., 2014; Ben-Omrane & Savaser, 2016; Boudt et al., 2019; Indriawana et al., 2021) . Given that exchange rate movements are unpredictable, accurately forecasting exchange rates is of great importance to various stakeholders (Ca’ Zorzi et al., 2017) . Predicting exchange rate returns is crucial for financial stability, reserve management, market regulation, and the overall monetary policy framework. Accurate predictions provide valuable insights for various stakeholders in the foreign exchange market, enabling them to make informed decisions. Such predictions facilitate comparisons between forecasting techniques, allowing stakeholders to identify the most accurate and effective method. The primary objective of this study is to evaluate and compare the accuracy and performance of different machine learning models in predicting exchange rates. This study employs clustering algorithms as an effective analytical tool to examine the evolution of exchange rates in Nigeria.  \nThe continuous availability of hi","cbCaifVgRXC4lW9m","https://ap.wps.com/l/cbCaifVgRXC4lW9m","pdf",1014396,1,16,"English","en",105,"# Introduction\n## Exchange rate importance and volatility\n## Study objective and approach\n# Literature Review\n# Methodology\n## Forecasting models and accuracy measures\n# Results\n## Summary statistics and stationarity tests\n## Forecast accuracy evaluation\n# Conclusions","[{\"question\":\"What is the purpose of this study?\",\"answer\":\"To evaluate and compare the accuracy and performance of four machine learning models for predicting Nigeria’s exchange rate against the US dollar.\"},{\"question\":\"Which machine learning algorithms are used for the exchange rate forecasting?\",\"answer\":\"Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and XGBoost are used to model the univariate time series.\"},{\"question\":\"How does Random Forest perform compared with other models?\",\"answer\":\"Random Forest outperforms the other approaches and produces the lowest prediction errors (MAE, MSE, RMSE, and MAPE) for both hourly and daily data, with XGBoost as the second-best performer.\"}]","Leveraging Machine Learning for Exchange Rate Prediction - 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