Special Issue "New Advances in Computational Finance and Computational Intelligence in Finance"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 456

Special Issue Editors

Dr. Vasilios N. Katsikis
E-Mail Website
Guest Editor
Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Athens, Greece
Interests: linear and multilinear algebra; numerical linear algebra; neural networks; intelligent optimization; mathematical finance
Special Issues, Collections and Topics in MDPI journals
Dr. Xinwei Cao
E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Swansea University, Swansea SA18EN, Wales, UK
Interests: portfolio optimization; big data; fintech management and decision making; fraud detection
Dr. Shuai Li
E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Swansea University, Swansea SA18EN, Wales, UK
Interests: neural networks; nonlinear optimization; optimal control; robotic planning
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Dr. Dimitris Balios
E-Mail Website
Guest Editor
Department of Economics, Division of Business Economics and Business Administration - Finance, National and Kapodistrian University of Athens, Sofokleous, 1, 10559 Athens, Greece
Interests: accounting analytics; earnings management; financial accounting; auditing; accounting standards
Prof. Dr. Theodore E. Simos
grade E-Mail Website
Guest Editor
1. Laboratory of Applied Mathematics for Solving Interdisciplinary Problems of Energy Production, Ulyanovsk State Technical University, Severny Venetz street 32, 432027 Ulyanovsk, Russia
2. Digital Industry REC, South Ural State University, 76, Lenin Aven., 454080 Chelyabinsk, Russia
3. Section of Mathematics, Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: numerical analysis; scientific computing; applied numerical analysis; computational chemistry; computational material sciences; computational physics; parallel algorithm and expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the broad topics of "Computational Finance" and "Computational Intelligence in Finance," presenting original research on the application of computational methods and machine intelligence techniques for modeling in finance.

We welcome submissions advancing cutting-edge research and novel ideas for computational methods with practical applications in finance, as well as computational intelligence methods as an alternative to statistical and econometric approaches to financial market analysis.

Contributions primarily focused on the following topics are encouraged:

  • Artificial intelligence, machine learning and big data in finance and data mining for financial data analysis;
  • Financial forecasting and trading algorithms;
  • Genetic algorithms, heuristics and metaheuristics in finance and portfolio optimization algorithms;
  • Fuzzy logic in financial modeling and quantum computing for finance;
  • Accounting analytics, earnings management, blockchain-based accounting, big data analytics in auditing and cloud accounting and big data;
  • Computational risk management and computing and financial management;
  • Digital assets and cryptocurrencies and asset pricing models;
  • Market analysis algorithms, market simulations, algorithmic trading and hedging strategies;
  • Dynamical analysis of financial markets, behavioral finance models and financial markets and firm dynamics.

Dr. Vasilios N. Katsikis
Dr. Xinwei Cao
Dr. Shuai Li
Dr. Dimitris Balios
Prof. Dr. Theodore E. Simos
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 1800 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

  • fuzzy logic in financial modeling
  • quantum computing for finance
  • artificial intelligence in finance
  • machine learning and big data in finance
  • financial forecasting
  • trading algorithms
  • heuristics and metaheuristics in finance
  • genetic algorithms in finance
  • data mining for financial data analysis
  • digital assets and cryptocurrencies
  • asset pricing models, libration and simulation
  • computational risk management
  • computing and financial management
  • accounting analytics
  • earnings management
  • blockchain-based accounting
  • big data analytics in auditing
  • cloud accounting and big data
  • portfolio optimization algorithms
  • market analysis algorithms
  • market simulations
  • algorithmic trading
  • hedging strategies
  • dynamical analysis of financial markets
  • behavioral finance models
  • financial markets and firm dynamics

Published Papers (1 paper)

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Research

Article
Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach
Mathematics 2022, 10(13), 2160; https://doi.org/10.3390/math10132160 - 21 Jun 2022
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Abstract
With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected [...] Read more.
With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs). At the same time, this computational finance model is solved with a nonlinear activated Beetle Antennae Search (NABAS) algorithm, which is a variant of the meta-heuristic optimization algorithm named Beetle Antennae Search (BAS) algorithm. Firstly, the fraud detection model is transformed into an optimization problem of minimizing loss function and using the NABAS algorithm to find the optimal solution. NABAS has only one search particle and explores the space under a given gradient estimation until it is less than an “Activated Threshold” and the algorithm is efficient in computation. Then, the random under-sampling with AdaBoost (RUSBoost) algorithm is employed to comprehensively evaluate the performance of NABAS. In addition, to reflect the superiority of NABAS in the fraud detection problem, it is compared with some popular methods in recent years, such as the logistic regression model and Support Vector Machine with Financial Kernel (SVM-FK) algorithm. Finally, the experimental results show that the NABAS algorithm has higher accuracy and efficiency than other methods in the fraud detection of public datasets. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Portfolio Insurance through Error-Correction Neural Networks
Authors: Vasilios N. Katsikis1,a,∗, Spyridon D. Mourtas1,b, Theodore E. Simos2,3,4,5,6,c, Dimitris Balios7,d
Affiliation: 1Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece, etc.
Abstract: Minimum-cost portfolio insurance (MCPI) is a well-known investment strategy that tries to limit the losses a portfolio may incur as stocks decrease in price without requiring the portfolio manager to sell those stocks. In this research, we define and study the continuous-time MCPI (CTMCPI) problem as a time-varying linear programming (TVLP) problem. More precisely, to address the CTMCPI problem using real-world datasets, three different time-varying quadratic programming neural network (NN) solvers are converted to TVLP solvers. These solvers are the zeroing NN (ZNN), the linear-variational-inequality primal–dual NN (LVI-PDNN), and the simplified LVI-PDNN (S-LVI-PDNN). The experiment findings illustrate and compare the performances of the ZNN, LVI-PDNN and S-LVI-PDNN in three various portfolio configurations, as well as indicating that the TVLP solvers are an excellent alternative to the traditional approaches. To promote and contend the outcomes of this research, we created two MATLAB repositories for the interested user, that are publicly accessible on GitHub.

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