Algorithmic Finance

Aims & Scope

Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance.

It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	0.2
2024	0.30

SJR (SCImago Journal Rank)

Year	Value
2024	0.107

Quartile

Year	Value
2024	Q4

Journal Rank

Year	Value
2024	28863

Journal Citation Indicator

Year	Value
2024	2

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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
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Economics, Econometrics and Finance and Mathematics, 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.

• Classification-based financial markets prediction using deep neural networks

  Citation: 100

  Authors: Matthew, Diego, Jin Hoon

• Forecasting prices from level-I quotes in the presence of hidden liquidity

  Citation: 30

  Authors: Marco, Josh, Sasha

• Cluster formation and evolution in networks of financial market indices

  Citation: 25

  Authors: Leonidas

• Trump tweets and the efficient Market Hypothesis

  Citation: 22

  Authors: Jeffery A., David H., William J.

• Markets are efficient if and only if P = NP

  Citation: 22

  Authors: Philip Z.

• Nonlinear support vector machines can systematically identify stocks with high and low future returns

  Citation: 19

  Authors: Ramon, Fernando, Charles

• Stock chatter: Using stock sentiment to predict price direction

  Citation: 18

  Authors: Michael, W. Nick, Padmini

• Market sentiment and exchange rate directional forecasting

  Citation: 18

  Authors: Vasilios, Theophilos, Periklis, Konstantinos

• The synchronized and long-lasting structural change on commodity markets: Evidence from high frequency data

  Citation: 18

  Authors: David, Nicolas

• A big data approach to analyzing market volatility

  Citation: 18

  Authors: Kesheng, E. Wes, Ming, David, Oliver

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