Journal of Financial Data Science

Aims & Scope

The journal represents both an acknowledgment of, and embracing of, the increasing role that data science and related fields of inquiry play in the financial industry and quantitative asset management.

It covers research activity in the areas of data science⁠—big data analytics, artificial intelligence, machine learning, and cognate areas⁠—as they are applied in asset management.

Within the journal, you will find high-quality articles from industry-leading experts in the field.

The new ideas the articles present aim to challenge the traditional way of thinking about finance, the economy, and investing; providing readers with actionable solutions to the practical problems faced by the investment management community.

View Aims & Scope

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Metrics & Ranking

Journal Rank

Year	Value
2024	12433

Journal Citation Indicator

Year	Value
2024	185

SJR (SCImago Journal Rank)

Year	Value
2024	0.459

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	16

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Business, Management and Accounting, Computer Science, Decision Sciences and Economics, Econometrics and Finance, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Deep Reinforcement Learning for Trading

  Citation: 148

  Authors: Zihao, Stefan, Stephen

• Deep Learning for Portfolio Optimization

  Citation: 87

  Authors: Zihao, Stefan, Stephen

• Industry Return Predictability: <i>A Machine Learning Approach</i>

  Citation: 66

  Authors: David E., Jack K., Jun, Guofu

• Dynamic Replication and Hedging: <i>A Reinforcement Learning Approach</i>

  Citation: 66

  Authors: Petter N., Gordon

• The Best Way to Select Features? Comparing MDA, LIME, and SHAP

  Citation: 62

  Authors: Xin, Ernest P.

• Deep Hedging of Derivatives Using Reinforcement Learning

  Citation: 47

  Authors: Jay, Jacky, John, Zissis

• Enhancing Time-Series Momentum Strategies Using Deep Neural Networks

  Citation: 46

  Authors: Bryan, Stefan, Stephen

• A Backtesting Protocol in the Era of Machine Learning

  Citation: 37

  Authors: Rob, Campbell R., Harry

• Deep Reinforcement Learning for Option Replication and Hedging

  Citation: 34

  Authors: Jiayi, Muyang, Petter N., Gordon, Yixuan, Bofei

• Machine Learning for Active Portfolio Management

  Citation: 27

  Authors: Söhnke M., Jürgen, Giuliano De, Mehrshad

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