Studies in France and U.S. showed that in order to forecast a business bankruptcy there can be used accounting methods (quantitative and analytical - use in comparative analyses in time to estimate the future development of the company) and banking methods (which suggests early detection of vulnerability andbankruptcyrisk through synthetic risk notes obtained based on statistical methods of discriminating analysis for the determination of a function score). The calculation of the score function requires knowledge of certain rates that allow detection of a business bankruptcyriskand to take timely corrective action. A note (Z) called the score is assigned to the company, being a linear combination of rates, and the value score, firms are classified as vulnerable, bankrupt and healthy. Most score functions constructed to detect the likely state ofbankruptcyof enterprises used the statistical technique discriminating analysis, which is especially good when you want to extract from the multitude of financial ratios calculated on the explanation to a very large extent the riskbankruptcyof the company (Dragotă 2003: 274). Several researchers and financial organizations were preoccupied with developing a method for predicting the riskofbankruptcy from a group rate linked with health or to the weakness of enterprises (Stancu, 1996: 385). The method used is the statistical technique of discriminating analysis of the financial characteristics (calculated by using rates) businesses to the normal operation and management of the economic and financial difficulties. Most methods for the analysis of insolvency risk, based on the score function which is used to determine approximately where a company will go bankrupt or will have bad economic results in the next review period (two years). Thus, the methods ofriskassessment - scoring known internationally as bankruptcy (Anglo-Saxon school and Continental school), attention focused on Altman and Conan-Holder model. Also, the models developed nationally by the Romanian school of economic and financial analysis model stands Robu-Mironiuc. Based on the three modelsof analysis shall be carried bankruptcyrisk analysis based on information released by the profit and loss and balance Turism-Covasna company, listed on the Bucharest Stock Exchange (http://www.bvb.ro).
Rating models are very important for the bank, but they shouldn’t be like a credit machine, be- cause these models fail in non-sensitive treatment, resp. they are unable to respond flexibly to complex business processes and specific business environment. Practical experiences from the bank’s market are confirming this hypothesis, because personnel of many banks are not able to individualize rating results to client in appropriate level. To maintain objectivity, it should be added that this process requires an optimal settings of managing parameters of the bank’s credit policy, considerable professional experiences of credit specialists, proper evaluation of information, intuition, or even luck, because many aspects of the future development can’t be assumed and predicted (for example, extreme increase of competition, unexpected and strong shock in the industry, special events etc.).
Economically, it is useful for the economic agents to determine those indicators that can prevent insolvency based on the analysis of the data in the annual financial statements. The specialized literature presents for this purpose models like: the Z score recommended by E. Altman, the Canon Holder model, the model of William Beaver, the model of the Central Bank of France and the Cematt model. More recently, commercial banks have bankruptcyriskassessment methods, such as: the method of the Romanian Commercial Bank, the method of the Romanian Development Bank – Groupe Societe Generale, the method of Transilvania Bank, the method of Raiffeisen Bank, which are methods tailored for Romanian economy, with obvious results in the diagnosis ofbankruptcy.
We now intend to develop a scoring function similar to Altman’s on a sample of 60 Romanian companies listed on the Romanian stock exchange to highlight both their financial strength but also their ability to meet the obligations. This way we took into account a total of seven economic and financial indicators for the activity of the companies (total assets – Activ total, sales – CA, operating profit - EBIT, net cash flow from operating activities - CF, net profit - PN, total liabilities – Datorii totale and average market value - CB.
Altman created his model in 1968, and he determined this model’s cut of point at 1.23. It means that a cut-off point (Z) less than 1.23 indicates that the company is bankrupt; if the value of Z is between 1.23 and 2.90, the company faces to uncertain future; and if the value of Z higher than 2.90, the company is solvent (Virág et al., 2013). The riskiest firms where we expected insolvency on the basis of business failure models were companies 1, 2, 4 and 5. This hypothesis was confirmed except company 2 that were listed in the grey zone in all the three years. In case of company 3 we supposed insolvency according to the traditional models, however, according to Altman’s model this company was in grey zone at this time. Surprisingly, the company 8 was classified in uncertainty zone by Altman’s model, although it had not been expected on the basis of comparative financial analysis. All the companies not mentioned in this paragraph were classified in solvent category by the model. So thus company 9, too, about which we did not have definite expectation. Using financial data of Canadian industry companies Gordon Springate created his bankruptcyforecasting model based on discriminant analysis in 1978 (Boritz and Kennedy- Sun, 2007). The model cut off point value is 0.862, consequently, if Z value is less than 0.862 the company is classified as insolvent. We expected insolvency for the control group (companies 2 and 5) and for companies 1 and 4. Our expectations were confirmed. For company 2, however, the model supported our assumption only in the year 2010. For company 9, which we defined uncertain and risky based on financial analysis, the model indicated insolvency in 2011 and 2012. Although Altman’s model did not show bankruptcy for this company, it noteworthy that it showed poor financial figure and ratios in last three years. But in case of company 3 – which can be regarded as disadvantageous on the basis of capital structure and liquidity – the model shows solvency in the examined three years. Another surprising conclusion is that the Springate model indicates insolvency in 2010 for company 8, although financial analysis produced excellent results for this firm in the whole investigated interval.
Several software products are used to defend computers from cyber-attackers. Antivirus software, antispyware and firewalls are examples to some of these tools based on periodic assessmentof the target computer by comparing computers' software to the known published vulnerabilities. Continuous Monitoring Systems (CMS) monitor systems in a near real time process aimed at detecting vulnerabilities and notifying security managers. Contemporary systems use vulnerabilities databases which are continually updated as new vulnerabilities are detected and a scoring algorithm which predicts potential business damages. This work focuses on measuring the confidentiality impacts on the overall risk score. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to unauthorized ones. Evaluating confidentiality impacts on business risk will be based on an algorithm which compares the actual access users are gaining, to the rules defined by the authorization system. The proposed CMS evaluates business risk scores relating to the actual technical configuration. This model focuses on measuring confidentiality potential losses related to known vulnerabilities. According to the proposed model each time a system is breached, systems' risk score is re-evaluated to reflect the impacts of the new breach.
The methods andmodels for forecastingbankruptcyof organizations, i.e. for the bankruptcyriskassessment at their inception beginning of the century as insolvency prediction models did not have the expected effect on the economic environment, due to difficult way of determining it. Their calculation meant a laborious arithmetic effort, difficult to interpret for non-specialists. With time due to the evolution of the )T environment and the evolution of these methods, the uses of these prediction methods are becoming more accessible to those interested. Gheorghe Negoescu believes that the riskofbankruptcy is inevitable related to profit variability as determined to an average profitability of an earlier period, or to the variation of the turnover [ ].
According to Silva (1997), the first study to predict business failure was performed by Fitzpatrick in 1932. In that study, the author used indicators of solvent companiesand compared them with insolvent companies (total of sample = 38 companies). Fitzpatrick concluded that the ratios extracted from accounting statements can provide important information about the riskof insolvency ofcompanies. The most significant ratios in the differentiation of the companies were liquid assets over liabilities and net profit on net assets. Subsequently, after Fitzpatrick, further studies have emerged, as is the case of Beaver in 1966 and Altman in 1968.
43 Similarly, an increase in the amount of liabilities of a firm, increases its financial leverage, which in turn intensifies the riskof possible transactions and the exposure to bankruptcy. On the other hand, as long as the market capitalization increases, that is the market value of the company’s outstanding shares, the default probability of the company decreases. Moreover, in CreditGrades model, the default probability of a firm is also a decreasing function of preferred equity and minotary interest. Preferred Equity is a class of ownership in a company that has a higher claim on its earnings and assets than common equity. In this case, the number of preferred shares increases the total number of shares so that, it reduces the amount of debt per share, and consequently the default probability. The minority interest, also known as non-controling interest, represents a percentage of ownership in a company by less than 50% of the outstanding shares, with a voting right. It includes part of the profit or loss and net assets of the subsidiary. Then, an increase in the minority interest of a company, will also reduce the amount of debt per share, and consequently its default probability.
Both banks and insurance companies deal and manage risk on a daily basis as a matter of their business field. As a consequence of this, their activity must depend on accurate internal riskmodels. In order to prevent events with greater impact in the financial environment, supervisors and remaining regulatory competent authorities make sure that insurance companies properly allocate their capital and that banks do the same, as well as they "establish more stable, long- term sources of funding". – cf. Deutsche Bank Research (2011). The Bank for International Settlements (BIS) establishes guidelines and strategies for banks, insurance companiesand pen- sion funds to follow in their businesses. These guidelines allow the different institutions to rely on their own internal modelsand assumptions, as an alternative to Standard Approaches, to determine their capital requirements. Although referring to different business modelsand dif- ferent regulatory measures, the financial supervision for both banks and insurance companies is made through a risk-based approach and implies thorough reporting to supervisory authorities. As a matter of fact, the concern to regulate the financial system as a whole and increase a set of best practices in financial and investment institutions from banks (Basel Accords) to insurance companies (Solvency Projects) is not new. Even before the sub-prime crisis and subsequent disruption of the global financial system these institutions were already subject to tight supervision and obliged to apply the regulatory standards in force within their governance. Notwithstanding, some experts point out the previous regulatory framework as a contributor to the recent financial crisis – see § 2.3 of European Actuarial Consultative Group (2013). There are also relevant differences that must be taken into account as referred in European Actuarial Consultative Group (2013). First, banks have to deal mainly with capital and liquidity while insurance companies consider capital andrisk. Also, banks and insurance companies are not evolving at the same pace – cf. p. 3.
Heritage managers and curators often need to prioritize and make choices about how to best to use the available resources to protect collections, buildings, monuments and sites. This means that questions such as: What to do first? What are the priorities of the heritage asset in its specific context? How to optimize the use of available resources to maximize the benefits of the cultural heritage over time? must be answered. To do this, the identification of the risks of the cultural property in study is necessary, using the ten agents of deterioration defined by the Canadian Conservation Institute . Each agent of deterioration can manifest in 1 or more within 3 types ofrisk characterized by frequency of occurrence and severity of their effect on collections (Type 1 - Rare and Catastrophic; Type 2 - Sporadic and Severe; Type 3 - Continual and Gradual) .
The relationship between Aedes density and the intensity of dengue transmission remains unclear [47,58–60]. Although dengue viruses cannot circulate if mosquito vectors are not present, the vector density of adult female A. aegypti necessary for dengue viruses to become endemic or epidemic remains unknown. In Noumea, entomological indices (HI, BI and API) were not correlated with the incidence rate of dengue, they were sometimes lower during epidemic than during non epidemic periods and lowest values were measured during the largest outbreak in 2009. The fact that these usual entomological surveillance indices (particularly API) are good indicators of adult density in Noumea suggests that the mosquito density threshold under which dengue viruses cannot spread widely may be very low and has never been reached up to now. Moreover, mosquito populations are influenced by human behaviours and meteorological variables alone cannot account for their geographical distribution and abundance [14,61]. At the domestic level, A. aegypti populations are also influenced by global trends in urbanization, socioeconomic conditions, and vector control efforts. For instance, the outbreak predicted in 2002 with a probability close to 90% did not occur. A possible explanation is that strong vector control policies (e.g. increased efforts to reduce mosquito breeding sites and undertake human population education, development of perifocal spraying of insecticides) were undertaken in New Caledonia at the time of large dengue outbreaks in the other Pacific French overseas territories (French Polynesia in 2001, Wallis and Futuna in 2002). A relaxation in vector control efforts at the end of 2002 may have allowed the resurgence of dengue in the East coast and the spread of the virus through the archipelago during the next year.
A symbiotic evolution-based fuzzy-neural diagnostic system for common acute abdominal pain presents a symbiotic evolution-based fuzzy-neural diagnostic system (SE-FNAAPDS) for diagnosis of common acute abdominal pain (AAP) without professional medical examination . The computer-assisted diagnostic system is formatted a multiple-choice symptom questionnaire, with a prompt/help menu to assist user in obtaining accurate symptom data using nothing more technologically sophisticated than a medical-type thermometer and stethoscope. Compared to traditional methods, diagnostic decisions from SE-FNAAPDS shows 94% agreement with professional human medical diagnosis and less CPU time for system construction. The presented method is useful as a core module for more advanced computer-assisted diagnostic systems, and for direct application in AAP diagnosis .
Richardson with SOR, Chebyshev with Gauss-Seidel and Chebyshev with SOR. The iterative schemes were applied to Banded system, Tridiagonal systems and SPD system with varying dimensions. The Krylov subspace methods: GMRES, QMR, MINRES and BiCGSTAB converged to an approximate solutions less than or equal to the dimension of the coefficient matrix for each identified systems of linear equations. Again, Chebyshev and Richardson acceleration methods were the fastest convergence methods in terms of number of iterations. Again, Residual smoothing and the accelerated gradient schemes should be used for large and sparse systems of linear equations. The acceleration processes were very efficient when solving large and sparse systems of linear equation and therefore useful especially for systems resulting from the solution of partial differential equations.
for 1 < q < 0 and θ > 0. Here θ is the shape parameter and q is the probability that the duration D is greater than one, i.e., q = P [D > 1]. Returning to the unemployment example, if unemployment spells have θ > 1, the duration dependence supports the “reservation wage” theory. However, if unemployment spells have θ < 1, the duration dependence supports the “damaged goods” theory; applying lifetime studies terminology, the distribution has increasing failure rate for θ > 1, decreasing failure rate for 0 < θ < 1 and reduces to the geometric distribution when θ = 1. If W is a continuous Weibull rv, then a type I discrete Weibull rv can be derived by time discretization D = [W ] + 1, where [W ] denotes the integer part of W . Stein and Dattero (1984) introduced a type II discrete Weibull and a type III was proposed by Padgett and Spurrier (1985). Type II has a serious limitation because the support is bounded. The estimation of parameters is diﬃcult in type III. In a detailed study, Bracquemond and Gaudoin (2003), recommended the use of type I discrete Weibull. The rest of the paper is organized as follows. Section 5.2 provides a brief review of estimation methods. In Section 5.3, the cdf and the moments for the proportions estimator and for a more general class, are derived. Based on the study of this class, a new shape parameter estimator is proposed. In Section 5.4, and through simulation experiments, we compare the performance of the new estimator with the method of moments and with the method of proportions. Finally, Section 5.5 presents an empirical application from a quantitative risk management context.
Information collected through the identification and classification of risks will be available for the calculations. Actual methods are structured to calculate the frequency of occurrence of the risk, barriers efficiency and, in the second part of the study, to assess the accident severity. Random processes modeled using PRA techniques involve the use of probabilistic models. 
Although accuracy gradually decreases, the model presents a very high predictive ability for all periods, which may lead us to question the accuracy of the model mainly for periods t-4 and t-5. The use of the 3.8% cut-off may be one of the reasons that could justify these very high values for all years. It is extremely important to remember that this cut-off was selected because it minimized both types of classification errors taking into account a specific sample. Therefore, given that the sample of the present study is quite different from that used by Ohlson (1980), both in terms of the number of countries and the period under analysis, 3.8% is a very small cut-off and may have influenced the results presented above. Another reason has to do with the year in which companies' financial distress are declared. Looking at the insolvent and liquidating companies in my sample, which make up the group ofcompanies in financial distress, there are many cases where companies are declared as distressed in a given year but already had evidence or had already been in difficulty during previous years. This is because many times, such insolvency or liquidation processes take years, which means that these companies will only be declared as distressed later than the actuality. This can justify high accuracy during all periods under analysis. The O-score model with a 50% cut-off also has high predictive ability, predicting financial distress up to three years before.
The Statistical Package for Social Scientists (SPSS) software (version 15) was used in testing whether or not the means of dependent variables were significantly different among groups. The total % yield of nitrogen, phosphorus and potassium of the stored urine over the 6-month study period were analysed. This was indicative of when the urine could be used for crops that require proportionally high percentage of nitrogen, phosphorus or potassium. The significant difference in yield of NPK between male and female urines was also established for each month over the 6 months study period. If the overall ANOVA was significant and a factor had more than two levels, a post-hoc multiple comparisons follow up test was carried out using Least Significance Difference (LSD) or Duncan’s Multiple Range Test (DMRT). In all cases, significance was determined at the 95% confidence level. One-way analysis of variance was performed to assess the differences among means, with a significance level of 5% (p< 0.05).
As pointed out previously in section 2.2.4 simple GARCH models also present some other problems. Black (1976) found evidence that stock returns are negatively correlated with changes in returns volatility. This means that volatility tends to increase in response to bad news and decrease in response to good news. GARCH models do not account for that since they only assume that the magnitude and not the positive or negative sign of excess returns influences the conditional volatility. Another limitation is that non-negativity constraints may be violated due to the fact that volatility is not constant over time and they can create difficulties in estimating GARCH models. Engle and Bollerslev (1986) focused on studying the persistence of shocks and their impact on conditional variance. If they persist indefinitely, there is the risk that they have significant impact in long lived capital goods (Poterba and Summers, 1986). These are the three main drawbacks of simple GARCH models according to Nelson (1991), thus in order to address to the drawbacks he adopted an exponential GARCH model.
In a growing concern for the world energy consumption, photovoltaic energy sources are a reliable renewable energy alternative. This thesis is built upon the premise that the forecast of photovoltaic power production can increase performance of local electric network through an ecient network management. The work developed proposes a power production forecast structure based on a grid-connected photovoltaic system in the University of Algarve. The proposed forecast structure is composed of two non-linear dynamic forecastingmodelsand one non-linear static model. Articial Neural Networks were used in the development of these models which are intended to forecast solar irradiance and air temperature using Radial Basis Functions with 5 minutes time steps within a prediction horizon of 4 hours. The static model on the structure was created to estimate the power generated by the photovoltaic system and it was optimized through comparison between several network architectures (MLP and RBF) and several seasonal models, as well as a annual model.