Data envelopment analysis (DEA) and financial ratios : a pro-stakeholders’ view of performance measurement for sustainable value creation of the wind energy

Purpose: The purpose of the paper is to explore business performance in a rather sensitive sector that equally combines economic, environmental and social dimensions. The paper investigates the efficiency of wind farm companies, in a framework of pursuing more diverse stakeholders’ interests Design/Methodology/Approach: Ratios and DEA approaches are combined to measure economic efficiency among the DMUs of a sample of wind farms, using data from their financial statements. Findings: Productivity and effectiveness comprise the performance measured by the economic efficiency. We show that by choosing inputs and outputs that are closely related in forming an appropriate financial ratio, it helps to design and explain more fully the impact of a policy intervention aiming at improving economic efficiency. DEA supplements ratios to design, implement and assess a strategy of benchmarking towards bolstering performance, that favors a wider range of stakeholders. Originality/Value: The study provides an in-depth insight into using Data Envelopment Analysis and financial ratios to study economic efficiency. The approach combines economic, social and environmental dimensions (indirectly) of performance, and the composite ratio Return on Total Assets (ROTA). The analysis caters the specific features of the sector renewable energy and their diverse stakeholders.peer-reviewe

Predicting the production of total industry in Greece with chaos theory and neural networks

This paper explores the use of chaos theory, as well as the neural networks, for predicting the Production of Total Industry in Greece. We have found that our data (from 1961 up to 2011) obey to the chaos theory. More specifically, the results from evaluation show that the minimum emending dimension is 4 suggesting chaos with a high dimensionality. We have also found that it is predictable the behavior of this production in the near future. The same results were evaluated using neural network, confirming our prediction.peer-reviewe

Time series prediction with neural networks for the Athens stock exchange indicator

The main aim of this study is to predict the daily stock exchange price index of the Athens Stock Exchange (ASE) using back propagation neural networks. We construct the neural network based on the minimum embedding dimension of the corresponding strange attractor. Multistep prediction for nine days ahead is achieved with this particular network indicating the increased possibility of this technique for immediate forecasts for very timeshort data sets, mostly daily and weekly.peer-reviewe

Analysis of Rattleback Chaotic Oscillations

Rattleback is a canoe-shaped object, already known from ancient times, exhibiting a nontrivial rotational behaviour. Although its shape looks symmetric, its kinematic behaviour seems to be asymmetric. When spun in one direction it normally rotates, but when it is spun in the other direction it stops rotating and oscillates until it finally starts rotating in the other direction. It has already been reported that those oscillations demonstrate chaotic characteristics. In this paper, rattleback’s chaotic dynamics are studied by applying Kane’s model for different sets of (experimentally decided) parameters, which correspond to three different experimental prototypes made of wax, gypsum, and lead-solder. The emerging chaotic behaviour in all three cases has been studied and evaluated by the related time-series analysis and the calculation of the strange attractors’ invariant parameters

Sustainable business growth, value creation and dynamic competitive advantage : the Greek pharmaceutical industry

PURPOSE: The paper assesses the efficiency in the use of inputs and its impact in the value creation measured by the EBITDA return on assets of a company. The latter is utilized to judge whether the companies involved possess a dynamic competitive advantage which creates business value.DESIGN/METHODOLOGY/APPROACH: A two step Data Envelopment Analysis (DEA) was applied. Αn input oriented version of the model was employed, using financial data and ratios as inputs and outputs, concerning the Greek owned pharmaceutical companies which are almost entirely comprised of non listed in the Stock Exchange economic entities. In the 1st stage we measured the economic efficiency with which inputs are used. In the 2nd stage we assessed whether the economic efficiency leads effectively into the creation of a lasting competitive advantage, culminating in creating value (return on assets) above the average. We examined whether the efficiency and effectiveness of business ultimately explain the difference in their financial performance and the degree of value creation which is attributed to the endowment of VRIN resources and the existence of dynamic capabilities.FINDINGS: We found that the efficiency in the use of assets and equity financing explains the EBITDA return on assets, the market value (effectiveness) of equity and eventually the enterprise (EV). Sustainable business growth deciphers the value creation footprint attributed to a tangible dynamic competitive advantage.ORIGINALITY/VALUE: We argue that in the case of non listed companies, the level of value creation is measured by the effectiveness and efficiency in the use of assets and proficiencies employed. It is mirrored in the magnitude of the EBITDA return on assets and ultimately reflected in the enterprise valuation performance using multiples of value drivers such as revenues-sales and EBITDA (earnings).peer-reviewe

A strategic financial management evaluation of private hospitals’ effectiveness and efficiency for sustainable financing : a research study

Purpose: The purpose of the study is to evaluate the performance οf private hospitals and identify conditions that secure sustainable financing οf the sector. Design/Methodology/Approach: The Data Envelopment Analysis (DEA) was used as the main tool to measure efficiency and effectiveness among fifteen (15) major private hospitals in Greece. Audited financial statement data were analyzed as a basis for the assessment of their performance. Αn input oriented model was applied due to the fact that assets and employee expenses are more likely to be under the control of management in private hospitals, compared to revenues and CFFO. The latter were used as outputs that represent measures of effectiveness and efficiency respectively which secure sustainability. We opted for the Variable Return to Scale (VRS) version of DEA (in connection with the CRS one), since hospital are systems extremely depended on the human capital and the knowledge management, as a means of creating value and are characterized by non-linear dynamics. Findings: The great majority of the hospitals in the sample exhibit increasing and decreasing returns scale. Inefficiencies found to emanate from a non-optimal scale of the hospitals rather, than from management’s lack of capability to transform inputs to outputs. Practical Implications: The study aspires to frame options and help management to make informed choices that promote sustainable development of the private sector, which are also applicable to the public one. It is essential for public authorities to judge the meaningful performance of the private hospitals, to administer accordingly the level of its subsidies through public insurance funds, the claw back and rebate policies in a period of fiscal austerity and act accordingly to attract or deter the inflow of scalable private funds in healthcare to promote human wellbeing. Originality/Value: Performance differences, can be leveraged to guide improvements in the operation of the private hospitals and reforms in the health care system.peer-reviewe

Examination of Chaotic Structures in Semiconductor or Alloy Voltage Time-Series: A Complex Network Approach for the Case of TlInTe2

This paper proposes a method for examining chaotic structures in semiconductor or alloy voltage oscillation time-series, and focuses on the case of the TlInTe2 semiconductor. The available voltage time-series are characterized by instabilities in negative differential resistance in the current–voltage characteristic region, and are primarily chaotic in nature. The analysis uses a complex network analysis of the time-series and applies the visibility graph algorithm to transform the available time-series into a graph so that the topological properties of the graph can be studied instead of the source time-series. The results reveal a hybrid lattice-like configuration and a major hierarchical structure corresponding to scale-free characteristics in the topology of the visibility graph, which is in accordance with the default hybrid chaotic and semi-periodic structure of the time-series. A novel conceptualization of community detection based on modularity optimization is applied to the available time-series and reveals two major communities that are able to be related to the pair-wise attractor of the voltage oscillations’ phase portrait of the TlInTe2 time-series. Additionally, the network analysis reveals which network measures are more able to preserve the chaotic properties of the source time-series. This analysis reveals metric information that is able to supplement the qualitative phase-space information. Overall, this paper proposes a complex network analysis of the time-series as a method for dealing with the complexity of semiconductor and alloy physics

Chaotic Behavior of Random Telegraph Noise in Nanoscale UTBB FD-SOI MOSFETs

International audienceLow frequency noise in nanoscale fully depleted ultra-thin body and buried oxide n-MOSFETs is not as random (stochastic) as it appears to be. The fluctuation of the drain current in such nano-devices, under certain bias conditions, exhibits complex random telegraph noise (RTN) that is usually observed in chaotic systems. Indeed, the deterministic chaotic nature of this noise-like variation is confirmed by means of the combined calculation of established nonlinear dynamics metrics. Specifically, the correlation dimension, the phase portrait of the reconstructed chaotic attractor, and the Lyapunov spectrum, conclude for a hyperchaotic behavior of the complex RTN with fractal characteristics

Investigating Dynamical Complexity and Fractal Characteristics of Bitcoin/US Dollar and Euro/US Dollar Exchange Rates around the COVID-19 Outbreak

This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic’s impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization (WHO) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events

A Universal Physics-Based Model Describing COVID-19 Dynamics in Europe

The self-organizing mechanism is a universal approach that is widely followed in nature. In this work, a novel self-organizing model describing diffusion over a lattice is introduced. Simulation results for the model’s active lattice sites demonstrate an evolution curve that is very close to those describing the evolution of infected European populations by COVID-19. The model was further examined against real data regarding the COVID-19 epidemic for seven European countries (with a total population of 290 million) during the periods in which social distancing measures were imposed, namely Italy and Spain, which had an enormous spread of the disease; the successful case of Greece; and four central European countries: France, Belgium, Germany and the Netherlands. The value of the proposed model lies in its simplicity and in the fact that it is based on a universal natural mechanism, which through the presentation of an equivalent dynamical system apparently documents and provides a better understanding of the dynamical process behind viral epidemic spreads in general—even pandemics, such as in the case of COVID-19—further allowing us to come closer to controlling such situations. Finally, this model allowed the study of dynamical characteristics such as the memory effect, through the autocorrelation function, in the studied epidemiological dynamical systems