Digital Finance Template
Write in a clean editor, then format for Digital Finance in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Digital Finance format
Digital Finance is a peer-reviewed journal published by Springer Nature, covering Blockchain Technology Applications and Security, Complex Systems and Time Series Analysis, Financial Markets and Investment Strategies.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Digital Finance 12, 45–58 (2023).
Formats any DOI in Digital Finance style. No sign-up. |
| Publishes research in | Blockchain Technology Applications and Security Complex Systems and Time Series Analysis Financial Markets and Investment Strategies Stock Market Forecasting Methods Market Dynamics and Volatility |
| ISSN | 2524-6186 |
| Citation impact (2-yr) | 1.76 |
| h-index | 19 |
| i10-index | 39 |
| Total citations | 1,605 |
| Article processing charge | $2,990 |
| Top institutions publishing here | Humboldt-Universität zu Berlin |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Digital Finance per year
Citation impact of Digital Finance by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Digital Finance
Abstract This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine learning appears well suited to support FP&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be…
Abstract Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms…