Stelios Bekiros

European University Institute / IPAG / AUEB · Economics / Finance

Recently, the number of machine learning models used to classify cry signals of healthy and unhealthy newborns has been significantly increasing. Various works have already reported encouraging classification results; however, fine-tuning of the hyper-parameters of machine leaning algorithms is still an open problem in the context of newborn cry si...

Fractional order maps are a hot research topic; many new mathematical models are suitable for developing new applications in different areas of science and engineering. In this paper, a new class of a 2D fractional hyperchaotic map is introduced using the Caputo-like difference operator. The hyperchaotic map has no equilibrium and lines of equilibr...

The multi-agent-based supply chain network is a dynamic system consisting of multiple subchains connected by information flows, material flows and capital flow, etc. The consensus of multi-agent systems is often applied to the cooperation between subchains and inventory management in supply chain networks. Considering the ubiquitous external distur...

The main purpose of this paper is to examine the Fractal Market Hypothesis (FMH) on family business, sustainability, shariah, green, technology, and global (all stocks) markets in European zone. By using the method of bootstrapped wavelet leaders, we found that there is strong evidence in favour of the FMH. Specifically, the analyses of singularity...

It is well known that the satellite attitude motion exhibits the chaotic phenomenon. This article addresses the challenging problem of chaotic attitude synchronization and anti-synchronization for master-slave satellites under unknown moments of inertia and disturbance torques. First, a fixed-time adaptive synchronization controller is designed by...

This article presents an indirect neural-based finite-time integral sliding mode control algorithm for the reference trajectory tracking guidance of Mars entry vehicle under uncertainties. The proposed controller is developed as a combination of finite-time integral sliding mode controller and indirect neural identification. The finite-time integra...

A dynamical model linking stress, social support, and health has been recently proposed and numerically analyzed from a classical point of view of integer-order calculus. Although interesting observations have been obtained in this way, the present work conducts a fractional-order analysis of that model. Under a periodic forcing of an environmental...

A novel observer-based control policy based on an interval type-3 fuzzy logic system is developed to tackle the main limitations of fuzzy-based controllers in sense of approximation of uncertainties and analyzing nonlinear complex systems without detailed dynamics model information. For this purpose, a novel scheme is proposed that includes online...

In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward...

This article is devoted to the determination of numerical solutions for the two-dimensional time–spacefractional Schrödinger equation. To do this, the unknown parameters are obtained using the Laguerre wavelet approach. We discretize the problem by using this technique. Then, we solve the discretized nonlinear problem by means of a collocation meth...

When cryptocurrency markets generate billions of dollars, it becomes interesting to forecast variation in volume of transactions for better trading and for better management of blockchain platforms. This study investigates how kernel choice influences the forecasting performance of the support vector regression (SVR) in predicting cryptocurrency tr...

In this article, a neural integral sliding mode control strategy is presented for the finite-time fault-tolerant attitude tracking of rigid spacecraft subject to unknown inertia and disturbances. First, an integral sliding mode controller was developed by originally constructing a novel integral sliding mode surface to avoid the singularity problem...

This paper proposes a novel neural adaptive fixed-time control approach for the attitude stabilization and vibration suppression of flexible spacecraft. First, the neural network (NN) was introduced to identify the lumped unknown term involving uncertain inertia, external disturbance, torque saturation, and elastic vibrations. Then, the proposed co...

We consider directional volatility connectedness among energy markets and financial markets over time and frequencies simultaneously during the period 2007–2018. We utilize and expand Barunik and Krehlik (J Financ Econom 16:271-296, 2018) connectedness measurements using HVAR in order to achieve a better perspective of energy markets. Our results i...

Due to the vital role of financial systems in today’s sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning...

Since the variable-order fractional systems show more complex characteristics and more degrees of freedom due to time-varying fractional derivatives, we introduce a variable-order fractional Hopfield-like neural network in this paper. First, the properties and dynamical behavior of the system are studied. The variable-order derivative’s effects on...

Mathematical modeling can be utilized to find out how the coronavirus spreads within a population. Hence, considering models that can precisely describe natural phenomena is of crucial necessity. Besides, although one of the most significant benefits of mathematical modeling is designing optimal policies for battling the disease, there are a few st...

Nowadays, deep learning architectures are promising artificial intelligence systems in various applications of biomedical engineering. For instance, they can be combined with signal processing techniques to build computer-aided diagnosis systems used to help physician making appropriate decision related to the diagnosis task. The goal of the curren...

Over the last years, distributed consensus tracking control has received a lot of attention due to its benefits, such as low operational costs, high resilience, flexible scalability, and so on. However , control methods that do not consider faults in actuators and control agents are impractical in most systems. There is no research in the literatur...

It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in th...

The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich dynamics and some other complex formations in biochemical reactions. In the present work, the synchronization problem of the glycolysis reaction-diffusion model is handled and examined. In addition, a novel convenient control law is designed in a linear...

Identifying parameters of financial and economic models with chaotic dynamics is an important, yet daunting challenge because of the complexities there exist in these chaotic systems. Although several studies have been devoted to understanding the mechanism of financial systems, the application of most state-of-the-art methods to these systems is c...

In this study, we present an improved computer-aided-diagnosis (CAD) system to distinguish between normal heart sound and one affected with murmur. The proposed system is based on nonlinear characteristics of the original heart sound high frequency oscillations. Specifically, the original signal is decomposed by discrete wavelet transform (DWT) and...

We examine long memory (self-similarity) in digital currencies and international stock exchanges prior and during COVID-19 pandemic. Specifically, ARFIMA and FIGARCH models are respectively employed to evaluate long memory parameter in returns and volatility. The dataset contains 45 cryptocurrency markets and 16 international equity markets. The t-...

Nowadays, advances in different fields of technology have increased demands for reliable controllers. Uncertainty, disturbances, and limitations in control inputs are inevitable with most systems. Hence, considering them in designing a practical controller seems indispensable to any system. We propose an adaptive, robust, and finite time control te...

This paper evaluates the appropriateness of a Linear Market Model (LMM) which allows for systematic covariance (beta) risk. The performance of LMM will be compared against two extensions, a comparison having yet to be undertaken in the literature. The first extension is the Time-varying Linear Market Model (Tv-LMM) which allows for time-varying sys...

This study aims to understand the dynamics of credit and business cycle interactions at the aggregated and disaggregated (sectors and industries) levels in the Indian context. We explore both parametric and non-parametric time-series approach to date the major turning points and calculate the lead-lag measures. We also test for synchronizing credit...

Control of supply chains with chaotic dynamics is an important, yet daunting challenge because of the limitations and constraints there are in the amplitude of control efforts. In real-world systems, applying control techniques that need a large amplitude signal is impractical. In the literature , there is no study that considers the control of sup...

In the present study, a new neural network-based terminal sliding mode technique is proposed to stabilize and synchronize fractional-order chaotic ecological systems in finite-time. The Chebyshev neural network is implemented to estimate unknown functions of the system. Moreover, through the proposed Chebyshev neural network observer, the effects o...

Credit risk and business failure classification and prediction are a major topic in financial risk management and corporate finance decision making. In this work, an adaptive sequential-filtering learning system for credit risk modeling. It is basically a three-stage sequential system for credit risk and business failure classification is presented...

A correction to this paper has been published: https://doi.org/10.1007/s00500-021-05882-3

Recently, intelligent control techniques have received considerable attention. In most studies, the systems’ model is assumed to be without any delay, and the effects of faults and failure in actuators are ignored. However, in real practice, sensor malfunctioning, mounting limitation, and defects in actuators bring about faults, failure, delay, and...

Blockchain is a related FinTech asset but it is not the same technology. Basically, Blockchain is a decentralized and distributed digital ledger used to record Bitcoin transactions. The goal of this work is to employ multi-scale analysis to examine self-similarity in EDC Blockchain digital asset. Specifically, market technical data are examined; na...

Although most of the early research studies on fractional-order systems were based on the Caputo or Riemann–Liouville fractional-order derivatives, it has recently been proven that these methods have some drawbacks. For instance, kernels of these methods have a singularity that occurs at the endpoint of an interval of definition. Thus, to overcome...

Disturbances are inevitably found in almost every system and, if not rejected, they could jeopardize the effectiveness of control methods. Thereby, employing state-of-the-art observers could improve the reliability and performance of controllers dramatically. Motivated by this, we develop a new finite-time method for controlling and synchronising f...

Keyword: H 2 / H ∞ control optimal control type-2 fuzzy interference robust tracking control chaos control financial systems a b s t r a c t Due to the importance of consumed control energy in financial systems, employing an optimal controller for these systems could be beneficial. Also, the presence of disturbances is undeniable in most of these s...

In this paper, by considering the Caputo-like delta difference definition, a fractional difference order map with chaotic dynamics and with no equilibria is proposed. The complex dynamical behaviors associated with fractional difference order maps are analyzed employing the phase portraits, bifurcations diagrams, and Lyapunov exponents. The complex...

Since December 2019, the new coronavirus has raged in China and subsequently all over the world. From the first days, researchers have tried to discover vaccines to combat the epidemic. Several vaccines are now available as a result of the contributions of those researchers. As a matter of fact, the available vaccines should be used in effective an...

This paper introduces a fractional-order financial risk system for the first time. Employing well-known tools and analyses such as bifurcations diagrams and spectral entropy, the dynamical behaviors of the system associated with fractional derivative are investigated. The impacts of the fractional derivative on the system’s behavior and its dynamic...

Mathematical modelling plays an indispensable role in our understanding of systems and phenomena. However, most mathematical models formulated for systems either have an integer order derivate or posses constant fractional-order derivative. Hence, their performance significantly deteriorates in some conditions. For the first time in the current pap...

In this novel research, through dynamical analysis, we introduce for the first time a fractional-calculus based artificial macroeconomic model, actually implemented in the Laboratory via a new hardware set up. Firstly, we propose a new model of a discrete-time macroeconomic system where fractional derivatives are incorporated into the system of equ...

Background Like common stocks, Bitcoin price fluctuations are non-stationary and highly noisy. Due to attractiveness of Bitcoin in terms of returns and risk, Bitcoin price prediction is attracting a growing attention from both investors and researchers. Indeed, with the development of machine learning and especially deep learning, forecasting Bitco...

COVID-19 is a novel coronavirus affecting all the world since December last year. Up to date, the spread of the outbreak continues to complicate our lives, and therefore, several research effort s from many scientific areas are proposed. Among them, mathematical models are an excellent way to understand and predict the epidemic outbreaks evolution...

The analysis of infant cry signals is becoming an attractive field of research in biomedical physics and engineering for better understanding of the pathologies and appropriate medial diagnosis. The main purpose of the current study is to characterize infant normal and pathological cry signals by studying their respective oscillations by means of a...

This chapter is about the synchronization of fractional-order memristive neural networks. At first, some basic concepts of fractional calculus are presented, and the model of fractional-order memristive neural networks is described. Then a robust adaptive controller is designed for the synchronization of these systems. The proposed robust adaptive...

Forecasting the future price of crude oil, which has an important role in the global economy, is considered as a hot matter for both investment companies and governments. However, forecasting the price of crude oil with high precision is indeed a challenging task because of the nonlinear dynamics of the crude oil time series, including chaotic beha...

The human immunodeficiency virus (HIV), as one of the most hazardous viruses, causes destructive effects on the human bodies’ immune system. Hence, an immense body of research has focused on developing antiretroviral therapies for HIV infection. In the current study, we propose a new control technique for a fractional-order HIV infection model. Fir...

A novel approach to solve optimal control problems dealing simultaneously with fractionaldifferential equations and time delay is proposed in this work. More precisely, a set of globalradial basis functions are firstly used to approximate the states and control variables in the problem.Then, a collocation method is applied to convert the time-delay...

Economic systems, due to their substantial effects on any society, are interesting research subject for a large family of researchers. Despite all attempts to study economic and financial systems, studies on discrete-time macroeconomic systems are rare. Hence, in the current study, we aim to investigate dynamical behavior and synchronization of the...

We develop a new robust control scheme for a non-holonomic spherical robot. To this end, the mathematical model of a pendulum driven non-holonomic spherical robot is first presented. Then, a recurrent neural network-based robust nonsingular sliding mode control is proposed for stabilization and tracking control of the system. The designed recurrent...

The COVID-19 pandemic has seriously affected world economies. In this regard, it is expected that information level and sharing between equity, digital currency, and energy markets has been altered due to the pandemic outbreak. Specifically, the resulting twisted risk among markets is presumed to rise during the abnormal state of world economy. The...

Modeling and analysis of financial systems have been interesting topics among researchers. The more precisely we know dynamic of systems, the better we can deal with them. This way, in this paper, we investigate the effect of market confidence on a financial system from the perspective of fractional calculus. Market confidence, which is a significa...

In this paper, we revisit the stylized facts of bitcoin markets and propose various approaches for modeling the dynamics governing the mean and variance processes. We first provide the statistical properties of our proposed models and study in detail their forecasting performance and adequacy by means of point and density forecasts. We adopt two lo...

It is crucial for investors to manage their investment risk. This paper examines the dynamic equicorrelation relationship between Bitcoin and four major investment assets, namely, US stock (S&P 500), US dollar, Treasury bonds and gold futures. Our empirical analysis reveals an asymmetric causality between Bitcoin and other asset classes. The result...

The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and...

Using annual data for the period 1980-2014, the study explores the impact of crude oil imports and real exchange rate on India’s current account performance in a current account balance model. Utilizing the recent cointegration econometric tools, the findings contrary to the theoretical prediction revealed that crude oil import significantly improv...

In the present article, as a new approach, a fuzzy disturbance observer is combined with an active controller for the synchronization of fractional-order time-delayed systems. Since the type-2 fuzzy logic system shows better performances than the type-1 fuzzy logic system on handling the uncertainties and disturbances, the type-2 fuzzy disturbance...

We embed non-fundamental house price expectation shocks and endogenous mortgage defaults into a DSGE model with a housing and banking sector. We use our DSGE set-up to study the impact of variations in house price expectations upon macroeconomic dynamics and their implications for monetary and macroprudential policies. Model simulations show that a...

We examined the dynamic linkages among money market interest rates in the so-called “BRICS” countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates covering the period from January 2005 to August 2019. A long-run relationship among...

Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory...

We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to...

Due to the remarkable boost in cryptocurrency trading on digital blockchain platforms, the utilization of advanced machine learning systems for robust prediction of highly nonlinear and noisy data, gains further popularity by individual and institutional market agents. The purpose of our study is to comparatively evaluate a plethora of Artificial I...

A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lea...

In this study, a new optimization algorithm, called King, is introduced for solving variable order fractional optimal control problems (VO-FOCPs). The variable order fractional derivative is portrayed in the Caputo sense through the dynamics of the system as variable order fractional differential equation (VO-FDE). To this end, firstly, the VO-FOCP...

This paper reports the results of a study that investigates the causal interactions among the entities energy consumption, democracy, income, and CO2 emissions in Bangladesh. Bootstrapping causality and time–frequency domain causality methods were adopted to examine the causal co-movements between the variables, using data series for a period of mo...

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. T...

New developments in the Information and Communications Technology industry have substantially increased the importance of the internet over the last decade. As a result, the finance sector has developed its technological capability to be able to compete in an online marketplace with other financial services providers and to be able to serve their c...

New developments in the Information and Communications Technology industry have substantially increased the importance of the internet over the last decade. As a result, the finance sector has developed its technological capability to be able to compete in an online marketplace with other financial services providers and to be able to serve their c...

Several studies have established the predictive power of the yield curve i.e. the difference between long and short-term bond rates and the role of asymmetries in the term structure of bond yields with respect to real economic activity. Using an extensive dataset, comprising 3-month, 1-year, 5-year and 10-year constant maturity Treasury bonds for t...

Several studies have established the predictive power of the yield curve i.e. the difference between long and short-term bond rates and the role of asymmetries in the term structure of bond yields with respect to real economic activity. Using an extensive dataset, comprising 3-month, 1-year, 5-year and 10-year constant maturity Treasury bonds for t...

Past research indicates that forecasting is important to understand the price dynamics across assets. We explore the potentiality of multi‐scale forecasting in the crude oil market by employing a wavelet multi‐scale analysis on returns and volatilities of Brent and WTI crude oil indices between 1 January 2001 and 1 May 2015. The analysis is based o...

The multi-fractal chaotic dynamics of Islamic and Green crypto-currency series are investigated for the first time in econophysics literature. Specifically, we decompose and analyse the temporal signals of prices, returns, volume and volatility of Islamic and Green cryptos vis-à-vis conventional ones in a comparative manner. We introduce a multi-st...

We employ a time-scale multi-fractal decomposition approach to investigate the properties of Bitcoin prices and volume at different sampling rates using high-frequency data. We provide evidence of multi-fractality at all rates. The big data-driven analysis combined with statistical testing shows evidence of dominant multi-fractal traits within the...

We identify the network structure of spillovers and time-varying spillover intensities across European sovereign credit markets proposing a novel Copula-Granger causality based structural vector auto-regressive (SVAR) approach. Via the proposed framework, we examine the topological and time-varying spillover and contagion between 13 European credit...

This paper investigates power-law correlations, chaos, and randomness in prices of family business, green (low Carbon), Islamic (Shariah), and common stock indices from the European zone. Specifically, the estimations of nonlinear patterns are performed in empirical mode decomposition domain to obtain time-scale computed values. The main findings f...

Long memory, information content, information generation, and randomness are evaluated in various industrial sectors from Casablanca Stock Exchange (CSE), Dow Jones, and S&P500. Then, three formal statistical tests are performed to check presence of differences. It is found that Dow Jones and S&P500 industrial sectors which are developed markets ar...

We explore the robustness, efficiency and accuracy of the multi-scale forecasting in crude oil markets. We adopt a novel hybrid wavelet auto-ARMA model to detect the inherent nonlinear dynamics of crude oil returns with an explicitly defined hierarchical structure. Entropic estimation is employed to calculate the optimal level of the decomposition....

Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimizati...