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.

• 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

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.