Data Science in Finance and Economics

Scope & Guideline

Fostering a data-centric approach to financial and economic challenges.

Introduction

Welcome to the Data Science in Finance and Economics information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Data Science in Finance and Economics, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN-
PublisherAMER INST MATHEMATICAL SCIENCES-AIMS
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationDATA SCI FINANC ECON / Data Sci. Financ. Econ.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES

Aims and Scopes

The journal 'Data Science in Finance and Economics' aims to bridge the gap between data science methodologies and practical applications in the finance and economics sectors. It focuses on innovative techniques and their implications for real-world financial and economic challenges.
  1. Application of Machine Learning and AI:
    The journal emphasizes the use of machine learning and artificial intelligence techniques for predictive modeling, risk assessment, and financial decision-making processes.
  2. Time Series Analysis and Forecasting:
    A core area of focus is the analysis and forecasting of financial time series data, utilizing various statistical and computational methods to understand market dynamics.
  3. Economic Modeling and Simulation:
    Research often explores economic models and simulations to assess financial systems' behavior under different conditions and policy changes.
  4. Interdisciplinary Approaches:
    The journal encourages interdisciplinary research that combines insights from finance, economics, data science, and behavioral sciences to enhance understanding of financial markets.
  5. Empirical Studies and Case Analyses:
    A significant portion of the publications includes empirical studies that analyze real-world data to validate theoretical models and provide actionable insights.
Recent publications reflect emerging themes and trends that highlight the evolving landscape of finance and economics research, particularly in response to technological advancements and global events.
  1. Integration of Big Data Analytics:
    There is a rising trend in the application of big data analytics in finance, focusing on how large datasets can enhance decision-making and predictive accuracy.
  2. Sustainability and Green Finance:
    Emerging research is increasingly exploring the intersection of finance and sustainability, particularly how financial markets can support renewable energy initiatives and sustainable economic practices.
  3. Behavioral Finance and Investor Sentiment:
    Studies examining the impact of investor sentiment and behavioral factors on market movements are gaining traction, reflecting a growing interest in the psychological aspects of financial decision-making.
  4. Cryptocurrencies and Alternative Assets:
    Research focusing on cryptocurrencies, including their volatility and market dynamics, is becoming a significant theme, driven by the rising interest and investment in digital assets.
  5. Machine Learning in Risk Assessment:
    There is a notable increase in studies employing machine learning techniques specifically for risk assessment and management, indicating a shift towards more advanced analytical frameworks.

Declining or Waning

While the journal covers a broad range of topics, certain themes have shown a decline in publication frequency, indicating a shift in research focus within the community.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in the publication of papers focusing solely on traditional statistical methods without incorporating modern computational approaches, such as machine learning.
  2. General Economic Theories:
    Research centered on generic economic theories without empirical validation or application to current financial challenges has become less prevalent, as there is a growing preference for data-driven studies.
  3. Basic Risk Management Techniques:
    Papers that focus on basic risk management techniques without the integration of advanced analytics or technology are appearing less frequently, suggesting a shift towards more sophisticated methodologies.

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