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JRFM
Special Issues
Machine Learning, Economic Forecasting, and Financial Markets
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Machine Learning, Economic Forecasting, and Financial Markets

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Markets".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1609

Special Issue Editor

Dr. SeyedSoroosh Azizi

E-Mail Website
Guest Editor
Department of Economics, DePaul University, Chicago, IL, USA
Interests: machine learning; economic forecasting; financial markets; economic development

Special Issue Information

Dear Colleagues,

This Special Issue delves into the intricate realm of economic forecasting and financial markets, exploring the dynamic interplay between economic predictions and the functioning of financial markets. This collection of articles offers valuable insights into the methodologies, challenges, and implications of economic forecasting in the context of financial market dynamics. From analyzing the impact of macroeconomic indicators on stock market performance to forecasting exchange rates and exploring the role of technological advancements in shaping financial market trends, the research presented in this Special Issue sheds light on the complex relationship between economic forecasts and financial market behavior.

Dr. SeyedSoroosh Azizi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • economic forecasting
  • financial markets
  • macroeconomic indicators
  • stock market performance
  • exchange rates
  • technological advancements
  • financial market trends

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Published Papers (1 paper)

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Research

15 pages, 2283 KiB  
Article
Forecasting Covered Call Exchange-Traded Funds (ETFs) Using Time Series, Machine Learning, and Deep Learning Models
by Chigozie Andy Ngwaba
J. Risk Financial Manag. 2025, 18(3), 120; https://doi.org/10.3390/jrfm18030120 - 25 Feb 2025
Viewed by 1329
Abstract
This study explores the application of time series, machine learning (ML), and deep learning (DL) models to predict the prices and performance of covered call ETFs. Utilizing historical data from major covered call ETFs like QYLD, XYLD, JEPI, JEPQ, and RYLD, the research [...] Read more.
This study explores the application of time series, machine learning (ML), and deep learning (DL) models to predict the prices and performance of covered call ETFs. Utilizing historical data from major covered call ETFs like QYLD, XYLD, JEPI, JEPQ, and RYLD, the research assesses the predictive accuracy and reliability of different forecasting approaches. It compares traditional time series methods, including ARIMA and Heterogeneous Autoregressive Model (HAR), with advanced ML techniques such as Random Forests (RF) and Support Vector Regression (SVR), as well as DL models like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Model performance is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Results indicate that the DL models are effective at identifying the nonlinear patterns and temporal dependencies in the price movements of covered call ETFs, outperforming both traditional time series and ML techniques. These findings enhance the existing financial forecasting literature and offer valuable insights for investors and portfolio managers aiming to improve their strategies using covered call ETFs. Full article
(This article belongs to the Special Issue Machine Learning, Economic Forecasting, and Financial Markets)
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