Welcome to my personal website!

I am an Associate Professor of Mathematics and Informatics at the Department of Economics of the National and Kapodistrian University of Athens. My current principal interests include but are not limited to Neural Networks, Intelligent Optimization, Numerical Linear Algebra, Linear and Multilinear Algebra and Mathematical Finance. On this site you can find information about my current research, publications, courses taught, and other items that may be of interest to you.

New Special Issue, Call for Papers (Deadline 30 November 2023) :

Special Issue Information:

Dear Colleagues,

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

We welcome submissions that represent cutting-edge research and novel ideas in 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, data mining for financial data analysis
  • Financial forecasting, trading algorithms,
  • Genetic algorithms, heuristics and metaheuristics in finance, portfolio optimization algorithms
  • Fuzzy logic in Financial Modeling, quantum computing for finance
  • Accounting analytics, earnings management, blockchain-based accounting, big data analytics in auditing, cloud accounting and big data
  • Computational risk management, computing and financial management
  • Digital assets and cryptocurrencies, asset pricing models
  • Market analysis algorithms, market simulations, algorithmic trading, hedging strategies
  • Dynamical analysis financial markets, behavioral finance models, financial markets and firm dynamics

New Advances in Computational Finance and Computational Intelligence in Finance

New Advances in Computational Finance and Computational Intelligence in Finance

Copy and paste this code to your website.
Copy and paste this code to your website.

Recent Publications

Zhang, D., Zhao, Y., Mosić, D., & Katsikis, V. N. (2023). Exact expressions for the Drazin inverse of anti-triangular matrices. Journal of Computational and Applied Mathematics, 428, 115187. WebsiteAbstract
The main contribution of this paper is to develop explicit expressions for the Drazin inverse of two kinds of anti-triangular block complex matrices under new assumptions. Further, we apply our results to obtain new formulae for the Drazin inverse of a 2 × 2 block complex matrix. We present a list of well-known results which are recovered in this paper. We give three examples to illustrate our new explicit expressions.
Mourtas, S. D., Katsikis, V. N., & Sahas, R. (2023). . European Proceedings of Computers and Technology. HMMOCS 2022 International Workshop "Hybrid methods of modeling and optimization in complex systems.
Credit card customers comprise a volatile subset of a banks' client base. As such, banks would like to predict in advance which of those clients are likely to attrite, so as to approach them with proactive marketing campaigns. Neuronets have found great application in many classification problems. Credit card attrition is a poorly investigated subtopic that poses many challenges, such as highly imbalanced datasets. The goal of this research is to construct a feed-forward neuronet that can overcome such obstacles and thus accurately classify credit card attrition. To this end, we employ a weights and structure determination (WASD) algorithm that facilitates the development of a competitive and all around robust classifier whilst accounting for the shortcomings of traditional back propagation neuronets. This is supported by the fact that when compared with some of the best performing classification models that MATLAB's classification learner app offers, the power softplus activated WASD neuronet demonstrated either superior or highly competitive performance across all metrics.
Mourtas, S. D., Kasimis, C., & Katsikis, V. N. (2023). Robust PID controllers tuning based on the beetle antennae search algorithm. Memories - Materials, Devices, Circuits and Systems, 4, 100030. WebsiteAbstract
The core components of both traditional and contemporary control systems are the proportional–integral–derivative (PID) control systems, which have established themselves as standards for technical and industrial applications. Therefore, the tuning of the PID controllers is of high importance. Utilizing optimization algorithms to reduce the mean square error of the controller’s output is one approach of tuning PID controllers. In this paper, an appropriately modified metaheuristic optimization algorithm dubbed beetle antennae search (BAS) is employed for robust tuning of PID controllers. The findings of three simulated experiments on stabilizing feedback control systems show that BAS produces comparable or higher performance than three other well-known optimization algorithms while only consuming a tenth of their time.
Katsikis, V. N., Stanimirović, P. S., Mourtas, S. D., Xiao, L., Stanujkić, D., & Karabašević, D. (2023). Zeroing Neural Network Based on Neutrosophic Logic for Calculating Minimal-Norm Least-Squares Solutions to Time-Varying Linear Systems. Neural Processing Letters. presented at the 2023. Publisher's VersionAbstract
This paper presents a dynamic model based on neutrosophic numbers and a neutrosophic logic engine. The introduced neutrosophic logic/fuzzy adaptive Zeroing Neural Network dynamic is termed NSFZNN and represents an improvement over the traditional Zeroing Neural Network (ZNN) design. The model aims to calculate the matrix pseudo-inverse and the minimum-norm least-squares solutions of time-varying linear systems. The improvement of the proposed model emerges from the advantages of neutrosophic logic over fuzzy and intuitionistic fuzzy logic in solving complex problems associated with predictions, vagueness, uncertainty, and imprecision. We use neutrosphication, de-fuzzification, and de-neutrosophication instead of fuzzification and de-fuzzification exploited so far. The basic idea is based on the known advantages of neutrosophic systems compared to fuzzy systems. Simulation examples and engineering applications on localization problems and electrical networks are presented to test the efficiency and accuracy of the proposed dynamical system.
Alharbi, H., Jerbi, H., Kchaou, M., Abbassi, R., Simos, T. E., Mourtas, S. D., & Katsikis, V. N. (2023). Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks. Mathematics, 11. WebsiteAbstract
The computation of the time-varying matrix pseudoinverse has become crucial in recent years for solving time-varying problems in engineering and science domains. This paper investigates the issue of calculating the time-varying pseudoinverse based on full-rank decomposition (FRD) using the zeroing neural network (ZNN) method, which is currently considered to be a cutting edge method for calculating the time-varying matrix pseudoinverse. As a consequence, for the first time in the literature, a new ZNN model called ZNNFRDP is introduced for time-varying pseudoinversion and it is based on FRD. FourFive numerical experiments investigate and confirm that the ZNNFRDP model performs as well as, if not better than, other well-performing ZNN models in the calculation of the time-varying pseudoinverse. Additionally, theoretical analysis and numerical findings have both supported the effectiveness of the proposed model.
Stanimirović, P. S., Ivanov, B., Stanujkić, D., Katsikis, V. N., Mourtas, S. D., Kazakovtsev, L. A., & Edalatpanah, S. A. (2023). Improvement of Unconstrained Optimization Methods Based on Symmetry Involved in Neutrosophy. Symmetry, 15. WebsiteAbstract
The influence of neutrosophy on many fields of science and technology, as well as its numerous applications, are evident. Our motivation is to apply neutrosophy for the first time in order to improve methods for solving unconstrained optimization. Particularly, in this research, we propose and investigate an improvement of line search methods for solving unconstrained nonlinear optimization models. The improvement is based on the application of symmetry involved in neutrosophic logic in determining appropriate step size for the class of descent direction methods. Theoretical analysis is performed to show the convergence of proposed iterations under the same conditions as for the related standard iterations. Mutual comparison and analysis of generated numerical results reveal better behavior of the suggested iterations compared with analogous available iterations considering the Dolan and Moré performance profiles and statistical ranking. Statistical comparison also reveals advantages of the neutrosophic improvements of the considered line search optimization methods.
Katsikis, V. N., Mourtas, S. D., Stanimirović, P. S., Li, S., & Cao, X. (2023). Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN. Applied Mathematics and Computation, 441, 127700. WebsiteAbstract
It is well known that minimum-cost portfolio insurance (MPI) is an essential investment strategy. This article presents a time-varying version of the original static MPI problem, which is thus more realistic. Then, to solve it efficiently, we propose a powerful recurrent neural network called the linear-variational-inequality primal-dual neural network (LVI-PDNN). By doing so, we overcome the drawbacks of the static approach and propose an online solution. In order to improve the performance of the standard LVI-PDNN model, an adaptive fuzzy-power LVI-PDNN (F-LVI-PDNN) model is also introduced and studied. This model combines the fuzzy control technique with LVI-PDNN. Numerical experiments and computer simulations confirm the F-LVI-PDNN model’s superiority over the LVI-PDNN model and show that our approach is a splendid option to accustomed MATLAB procedures.
Li, X., Lin, C. - L., Simos, T. E., Mourtas, S. D., & Katsikis, V. N. (2022). Computation of Time-Varying {2,3}- and {2,4}-Inverses through Zeroing Neural Networks. Mathematics, 10. WebsiteAbstract
This paper investigates the problem of computing the time-varying {2,3}- and {2,4}-inverses through the zeroing neural network (ZNN) method, which is presently regarded as a state-of-the-art method for computing the time-varying matrix Moore–Penrose inverse. As a result, two new ZNN models, dubbed ZNN23I and ZNN24I, for the computation of the time-varying {2,3}- and {2,4}-inverses, respectively, are introduced, and the effectiveness of these models is evaluated. Numerical experiments investigate and confirm the efficiency of the proposed ZNN models for computing the time-varying {2,3}- and {2,4}-inverses.