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 are in Artificial Intelligence, Computational Optimization, Computational Finance, Matrix Analysis and Applied Linear Algebra. 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 31 December 2022) :

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

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Recent Publications

Mourtas, S. D., & Katsikis, V. N. (2022). Exploiting the Black-Litterman framework through error-correction neural networks. Neurocomputing, 498, 43-58. WebsiteAbstract
The Black-Litterman (BL) model is a particularly essential analytical tool for effective portfolio management in financial services sector since it enables investment analysts to integrate investor views into market equilibrium returns. In this research, we define and study the continuous-time BL portfolio optimization (CTBLPO) problem as a time-varying quadratic programming (TVQP) problem. The investor’s views in the CTBLPO problem are regarded as a forecasting problem, and they are generated by a novel neural network (NN) model. More precisely, employing a novel multi-function activated by a weights-and-structure-determination for time-series (MAWTS) algorithm, a 3-layer feed-forward NN model, called MAWTSNN, is proposed for handling time-series modeling and forecasting problems. Then, using real-world datasets, the CTBLPO problem is approached by two different TVQP NN solvers. These solvers are the zeroing NN (ZNN) and the linear-variational-inequality primal–dual NN (LVI-PDNN). The experiment findings illustrate and compare the performances of the ZNN and LVI-PDNN in three various portfolio configurations, as well as indicating that the MAWTSNN is 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.
Mosić, D., Stanimirović, P. S., & Katsikis, V. N. (2022). Properties of the CMP inverse and its computation. Computational and Applied Mathematics, 41(4), 131. presented at the 2022. Publisher's VersionAbstract
This manuscript aims to establish various representations for the CMP inverse. Some expressions for the CMP inverse of appropriate upper block triangular matrix are developed. Successive matrix squaring algorithm and the method based on the Gauss–Jordan elimination are considered for calculating the CMP inverse. As an application, the solvability of several restricted systems of linear equations (RSoLE) is investigated in terms of the CMP inverse. Illustrative examples and examples on randomly generated large-scale matrices are presented.
Simos, T. E., Katsikis, V. N., Mourtas, S. D., Stanimirović, P. S., & Gerontitis, D. (2022). A higher-order zeroing neural network for pseudoinversion of an arbitrary time-varying matrix with applications to mobile object localization. Information Sciences, 600, 226-238. WebsiteAbstract
The hyperpower family of iterative methods with arbitrary convergence order is one of the most used methods for estimating matrix inverses and generalized inverses, whereas the zeroing neural network (ZNN) is a type of neural dynamics developed to solve time-varying problems in science and engineering. Since the discretization of ZNN dynamics leads to the Newton iterative method for solving the matrix inversion and generalized inversion, this study proposes and investigates a family of ZNN dynamical models known as higher-order ZNN (HOZNN) models, which are defined on the basis of correlation with hyperpower iterations of arbitrary order. Because the HOZNN dynamical system requires error function powers, it is only applicable to square error functions. In this paper, we extend the original HOZNN dynamic flows to arbitrary time-dependent real matrices, both square and rectangular, and sign-bi-power activation is used to investigate the finite-time convergence of arbitrary order HOZNN dynamics. The proposed models are theoretically and numerically tested under three activation functions, and an application in solving the angle-of-arrival (AoA) localization problem demonstrates the effectiveness of the proposed design.
Kornilova, M., Kovalnogov, V., Fedorov, R., Zamaleev, M., Katsikis, V. N., Mourtas, S. D., & Simos, T. E. (2022). Zeroing Neural Network for Pseudoinversion of an Arbitrary Time-Varying Matrix Based on Singular Value Decomposition. Mathematics, 10. WebsiteAbstract
Many researchers have investigated the time-varying (TV) matrix pseudoinverse problem in recent years, for its importance in addressing TV problems in science and engineering. In this paper, the problem of calculating the inverse or pseudoinverse of an arbitrary TV real matrix is considered and addressed using the singular value decomposition (SVD) and the zeroing neural network (ZNN) approaches. Since SVD is frequently used to compute the inverse or pseudoinverse of a matrix, this research proposes a new ZNN model based on the SVD method as well as the technique of Tikhonov regularization, for solving the problem in continuous time. Numerical experiments, involving the pseudoinversion of square, rectangular, singular, and nonsingular input matrices, indicate that the proposed models are effective for solving the problem of the inversion or pseudoinversion of time varying matrices.
Khan, A. T., Cao, X., Brajevic, I., Stanimirovic, P. S., Katsikis, V. N., & Li, S. (2022). Non-linear Activated Beetle Antennae Search: A novel technique for non-convex tax-aware portfolio optimization problem. Expert Systems with Applications, 116631. WebsiteAbstract
The non-convex tax-aware portfolio optimization problem is traditionally approximated as a convex problem, which compromises the quality of the solution and converges to a local-minima instead of global minima. In this paper, we proposed a non-deterministic meta-heuristic algorithm called Non-linear Activated Beetle Antennae Search (NABAS). NABAS explores the search space at the given gradient estimate measure until it is smaller than a threshold known as “Activation Threshold”, which increases its convergence rate and avoids local minima. To test the validity of NABAS, we formulated an optimization-based tax-aware portfolio problem. The objective is to maximize the profit and minimize the risk and tax liabilities and fulfill other constraints. We collected stock data of 20 companies from the NASDAQ stock market and performed a simulation using MATLAB. A comprehensive comparison is made with BAS, PSO, and GA algorithms. The results also showed that a better-optimized portfolio is achieved with a non-convex problem than a convex problem.
Khan, A. T., Cao, X., Li, S., Katsikis, V. N., Brajevic, I., & Stanimirovic, P. S. (2022). Fraud detection in publicly traded U.S firms using Beetle Antennae Search: A machine learning approach. Expert Systems with Applications, 191, 116148. WebsiteAbstract
In this paper, we present a fraud detection framework for publicly traded firms using an optimization approach integrated with a meta-heuristic algorithm known as Beetle Antennae Search (BAS). Existing techniques include human resources, like financial experts and audit teams, to determine the ambiguities or financial frauds in the companies based on financial and non-financial ratios. It is a laborious task, time-consuming, and prone to errors. We designed an optimization problem to minimize the loss function based on a non-linear decision function combined with the maximization of recall (Sensitivity and Specificity). We solved the optimization problem iteratively using the BAS. It is a nature-inspired algorithm and mimics the beetle’s food-searching nature. It includes a single searching particle to find an optimal solution to the optimization problem in n-dimensional space. We used a benchmark dataset collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs) for the simulation. It includes 28 raw financial variables and the data collected between 1991–2008. For the comparison, we evaluated the performance of BAS with the recently proposed approach using RUSBoost. We also compared it with some additional algorithms, i.e., Logit and SVM-FK. The results showed that BAS is comparable with these algorithms and outperformed them in time consumption.
Katsikis, V. N., Mourtas, S. D., Stanimirović, P. S., Li, S., & Cao, X. (2022). Time-varying mean–variance portfolio selection problem solving via LVI-PDNN. Computers and Operations Research, 138, 105582. presented at the 2022. Publisher's VersionAbstract
It is widely acclaimed that the Markowitz mean–variance portfolio selection is a very important investment strategy. One approach to solving the static mean–variance portfolio selection (MVPS) problem is based on the usage of quadratic programming (QP) methods. In this article, we define and study the time-varying mean–variance portfolio selection (TV-MVPS) problem both in the cases of a fixed target portfolio’s expected return and for all possible portfolio’s expected returns as a time-varying quadratic programming (TVQP) problem. The TV-MVPS also comprises the properties of a moving average. These properties make the TV-MVPS an even greater analysis tool suitable to evaluate investments and identify trading opportunities across a continuous-time period. Using an originally developed linear-variational-inequality primal–dual neural network (LVI-PDNN), we also provide an online solution to the static QP problem. To the best of our knowledge, this is an innovative approach that incorporates robust neural network techniques to provide an online, thus more realistic, solution to the TV-MVPS problem. In this way, we present an online solution to a time-varying financial problem while eliminating static method limitations. It has been shown that when applied simultaneously to TVQP problems subject to equality, inequality and boundary constraints, the LVI-PDNN approaches the theoretical solution. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods.
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