The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach

Erçen, Hüseyin İlker; Özdeşer, Hüseyin; Türsoy, Turgut

doi:10.3390/su14095357

Cited by 6 publications

(6 citation statements)

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“…Tables 1–4 summarize the statistical analysis of performance metrics of selective non‐conventional models with given accuracy indicators such as MSE, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Studies like Erçen et al (2022), Flores (1986), Li et al (2020), and Sun et al (2020) used these evaluation criteria to measure the accuracy of forecasting techniques. For a better understanding, the statistics are given in these tables shown in bold.…”

Section: Model Analysismentioning

confidence: 99%

“…Secondly, the study tested for the noise through the application of MODWT and denoised it for further analysis. For comparison purposes, the accurate measurement of these forecasting techniques is an important part, and studies, that is, Erçen et al (2022), Flores (1986), Li et al (2020) and Sun et al (2020), applied these statistical measures to compare the forecasting techniques. So thirdly, to evaluate, compare, and assess the resulting outcomes from these models, accuracy indicators of statistical measures have been used for identifying the best prediction model.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting multi‐frequency intraday exchange rates using deep learning models

Arslan,

Hunjra,

Ahmed

et al. 2024

Journal of Forecasting

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This paper examines the behavior of currencies' intraday exchange rates with mainly focuses on predicting these behaviors through deep learning models. The time series data are used in this study and comprise intraday exchange rate data for seven volatile currencies, recorded at two different frequency intervals: 1 h and 30 min. The data cover the time frame from January 1, 2018, to December 31, 2020. Firstly, wavelet maximal overlap discreet wavelet transform (MODWT) type “haar” is applied in order to identify the noise representing the volatile trend. Then learning models are applied to historical data that includes support vector regression (SVR), recurrent neural network (RNN), and long short‐term memory (LSTM). The main findings of the study provide strong evidence that the deep learning technique (i.e., LSTM) outperforms the other compatible models. This has been confirmed by the statistical measures for accuracy purposes. This study intends to address complex trends exhibited by volatile behavior of intraday exchange rates through non‐conventional learning techniques for the time of need‐based for the currency traders and other stakeholders.

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Section: Model Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting multi‐frequency intraday exchange rates using deep learning models

Arslan,

Hunjra,

Ahmed

et al. 2024

Journal of Forecasting

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“…Ref. [20] used a hybrid machine learning method to examine the impact of macroeconomic policy news on exchange rate fluctuations. Using subjective information, the model was used to test the ability of such an approach to predict future exchange rates in the long term, and the model indicated the significant impact of subjective information on exchange rate fluctuations.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of a Sustainable Economic Development Focus on the Real Exchange Rate in Saudi Arabia

Alabdulwahab

2023

Sustainability

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Saudi Arabia has launched its vision for the next decade, which is branded Vision 2030. Sustainable economic development is the core of the Saudi Vision 2030, which has seventeen sustainability programs covering a wide range of Saudi economic sectors. The aim of this study was to examine the impact of the sustainable economic development focus on the real exchange rate in Saudi Arabia. The ARDL cointegration method was used to assess the existence of the long-run relationships among the variables. The Sustainable Development Index (SDI) was used as a measure of sustainable economic development. Sustainable economic development was found to have a negative impact on the real exchange rate in terms of both the long- and short-run dynamics. Moreover, the GDP was found to have a positive impact on the real exchange rate in the long run and to have the highest coefficient of the model. However, the money supply was found to have no impact on the real exchange rate, while oil rent was found to have a negative impact on the real exchange rate in terms of both the long-run and the first-moment short-run dynamics. Government expenditure was found to be insignificant in the long run and to have a positive significant impact on the real exchange rate in the short-run dynamics. Furthermore, the sustainability impact on the real exchange rate in Saudi Arabia has not been discussed due to the economic structure that relies on oil and the change toward a sustainable economy that occurred in the recent decade. The results provide insight into the potential future challenges faced by the Saudi economy as sustainability programs progress in Saudi Arabia.

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“…Some studies discuss how subtle nudges can influence consumer behavior, often without the consumer's knowledge, and how cognitive biases can affect decision-making in the presence of these nudges [63]. Other articles investigate how machine learning techniques, such as SVM and feature selection, can be applied to neuromarketing research to gain insights into consumer behavior and preferences [64], examine how AI and artificial neural networks can help improve our understanding of decision-making processes in the context of neuroeconomics [65], the use of inverse reinforcement learning (IRL) to explain user behavior on YouTube [66], and the use of prospect theory and hybrid machine learning, which combines traditional econometric models with machine learning algorithms to analyze the impact of macroeconomic factors such as inflation and economic growth on exchange rates [67].…”

Section: ) Thematic Structure Through Co-word Analysismentioning

confidence: 99%

“…Notably, studies investigating technology acceptance and adoption, such as the impact of AI-driven chatbots on consumer interactions [96], as well as the integration of service robots in hospitality, tourism, and restaurant industries [88] [89] [90], have garnered significant attention. • Studies on macroeconomics and experimental economics are scant, with some first studies such as the use of prospect theory and hybrid machine learning, which combine traditional econometric models with machine learning algorithms to analyze the impact of macroeconomic factors such as inflation and economic growth on exchange rates [67].…”

Section: B Key Findings For the Science Mapping And Network Analysismentioning

confidence: 99%

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

Aoujil,

Hanine,

Flores

et al. 2023

IEEE Access

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Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.INDEX TERMS Artificial intelligence; behavioral economics; behavioral finance; consumer behavior; investor behavior; decision making; neuroeconomics; machine learning; bibliometric analysis I. INTRODUCTIONB EHAVIORAL economics is a subfield of economics that examines the decision-making processes of individuals and groups and how they affect economic outcomes. It is an interdisciplinary field that combines elements of psychology, sociology, neuroscience, and economics to provide a more complete understanding of human behavior in the marketplace [1]. It differs from classical economics, which assumes that individuals are rational and always make decisions that maximize their utility. In contrast, behavioral economics

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The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach

Cited by 6 publications

References 50 publications

Forecasting multi‐frequency intraday exchange rates using deep learning models

Forecasting multi‐frequency intraday exchange rates using deep learning models

The Impact of a Sustainable Economic Development Focus on the Real Exchange Rate in Saudi Arabia

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

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