scholarly journals Corporate rating forecasting using Artificial Intelligence statistical techniques

2019 ◽  
Vol 16 (2) ◽  
pp. 295-312 ◽  
Author(s):  
Daniel Caridad ◽  
Jana Hančlová ◽  
Hosn el Woujoud Bousselmi ◽  
Lorena Caridad y López del Río
Keyword(s):  
Artificial Intelligence ◽  
Credit Rating ◽  
Financial Health ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Rating Agencies ◽  
Large Samples ◽  
Public Data ◽  
High Level

Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.

scholarly journals Moody’s Ratings Statistical Forecasting for Industrial and Retail Firms

Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 154
Author(s):  
Lorena Caridad y López del Río ◽  
María de los Baños García-Moreno García ◽  
José Rafael Caro-Barrera ◽  
Manuel Adolfo Pérez-Priego ◽  
Daniel Caridad y López del Río
Keyword(s):  
Artificial Intelligence ◽  
Financial Crisis ◽  
Credit Rating ◽  
Conflicts Of Interest ◽  
Rating Agencies ◽  
Public Data ◽  
Rating Process ◽  
Rating Model ◽  
External Check

Long-term ratings of companies are obtained from public data plus some additional nondisclosed information. A model based on data from firms’ public accounts is proposed to directly obtain these ratings, showing fairly close similitude with published results from Credit Rating Agencies. The rating models used to assess the creditworthiness of a firm may involve some possible conflicts of interest, as companies pay for most of the rating process and are, thus, clients of the rating firms. Such loss of faith among investors and criticism toward the rating agencies were especially severe during the financial crisis in 2008. To overcome this issue, several alternatives are addressed; in particular, the focus is on elaborating a rating model for Moody’s long-term companies’ ratings for industrial and retailing firms that could be useful as an external check of published rates. Statistical and artificial intelligence methods are used to obtain direct prediction of awarded rates in these sectors, without aggregating adjacent classes, which is usual in previous literature. This approach achieves an easy-to-replicate methodology for real rating forecasts based only on public available data, without incurring the costs associated with the rating process, while achieving a higher accuracy. With additional sampling information, these models can be extended to other sectors.

Long-term variations in individual particle types in the aerosol of Phoenix, Arizona, as determined using automated electron microprobe analysis and multivariate statistical techniques

1993 ◽  
Vol 51 ◽  
pp. 742-743
Author(s):  
Karen A. Katrinak ◽  
James R. Anderson ◽  
Peter R. Buseck
Keyword(s):  
Electron Microprobe ◽  
Fine Fraction ◽  
Coarse Fraction ◽  
Motor Vehicles ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
X Ray ◽  
Mineral Particles ◽  
Anthropogenic Air Pollutants

Aerosol samples were collected in Phoenix, Arizona on eleven dates between July 1989 and April 1990. Elemental compositions were determined for approximately 1000 particles per sample using an electron microprobe with an energy-dispersive x-ray spectrometer. Fine-fraction samples (particle cut size of 1 to 2 μm) were analyzed for each date; coarse-fraction samples were also analyzed for four of the dates.The data were reduced using multivariate statistical methods. Cluster analysis was first used to define 35 particle types. 81% of all fine-fraction particles and 84% of the coarse-fraction particles were assigned to these types, which include mineral, metal-rich, sulfur-rich, and salt categories. "Zero-count" particles, consisting entirely of elements lighter than Na, constitute an additional category and dominate the fine fraction, reflecting the importance of anthropogenic air pollutants such as those emitted by motor vehicles. Si- and Ca-rich mineral particles dominate the coarse fraction and are also numerous in the fine fraction.

Pollen morphology of some Tamarix (Tamaricaceae) species of Iran

2020 ◽  
Vol 62 (1-2) ◽  
pp. 151-161
Author(s):  
T. Shagholi ◽  
M. Keshavarzi ◽  
M. Sheidai
Keyword(s):  
North Africa ◽  
Pollen Grains ◽  
Ecological Niches ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Qualitative And Quantitative ◽  
Petal Length ◽  
Electron Microscopes ◽  
High Level ◽  
Scanning Electron Microscopes

Tamarix L. (Tamaricaceae) is a halophytic shrub in different parts of Asia and North Africa. Taxonomy and species limitation of Tamarix is very complex. This genus has three sections as Tamarix, Oligadenia, and Polyadenia, which are mainly separated by petal length, the number of stamens, the shape of androecial disk and attachment of filament on the androecial disk. As there was no palynological data on pollen features of Tamarix species of Iran, in the present study 12 qualitative and quantitative pollen features were evaluated to find diagnostic ones. Pollen grains of 8 Tamarix species were collected from nature. Pollen grains were studied without any treatment. Measurements were based on at least 50 pollen grains per specimen. Light and scanning electron microscopes were used. Multivariate statistical methods were applied to clarify the species relationships based on pollen data. All species studied showed monad and tricolpate (except some individuals of T. androssowii). Some Tamarix species show a high level of variability, in response to ecological niches and phenotypic plasticity, which make Tamarix species separation much more difficult. Based on the results of the present study, pollen grains features are not in agreement with previous morphological and molecular genetics about the sectional distinction.

An Emperical Study on Credit Rating Agencies (with Ref. to Default Rates of Crisil)

2015 ◽  
Vol 9 (1and2) ◽  
Author(s):  
Ms. Reenu Bansal ◽  
Dr. N M Sharma
Keyword(s):  
Performance Management ◽  
Credit Rating ◽  
Credit Rating Agencies ◽  
Rating Agencies ◽  
Default Rates ◽  
Key Issues ◽  
The Subject ◽  
Investment Grade ◽  
Financial Obligation

Credit rating is the symbolic indicator of the current opinion of rating agencies regarding the relative capability of issuer of debt instruments, to service the debt obligations as per contract. The corporations with specialized functions namely, assessment of the likelihood, of the timely payments by an issuer on a financial obligation is known as credit rating agencies. Lately, the credit rating agencies have been the subject of significant criticism for failing to warn the investors of the defaults well in advance. Investors in long-term debt instruments are usually risk averse, buy-and-hold types; and hence, for them, the variability of investment-grade default rates is particularly important since they employ simple investment-grade rating cut-offs in the design of their investment eligibility plan. According to CRISIL (Credit Rating Information Services of India) and another credit rating agencies, default mean that the company has either already failed in the payment of interest and/or principal as per terms or is expected to fail. This paper tests the reliability of ratings assigned by CRISIL on the basis of the actual default rate experience in different sectors over a period of ten years, i.e., 2000-2011.Since the credit rating agencies do not publish ratings that are not accepted by the issuers, this study is limited to only those issues that have been accepted and used by the issuers. The default statistics were examined sector-wise, period-wise, and company/institution-wise. Analyses of the background and business, operating performance, management and systems, financial performance, prospects, key issues, and the reasons cited for defaults were undertaken with respect to all the companies. Further, an attempt was made to identify whether companies in default had issued other debt instruments that were rated by other credit rating agencies.

scholarly journals Wpływ COVID-19 na nastawienie i credit ratingi krajów

2020 ◽  
Vol 8 (4) ◽  
pp. 535-564
Author(s):  
Patrycja Chodnicka-Jaworska
Keyword(s):  
Financial Crisis ◽  
Foreign Currency ◽  
Credit Rating ◽  
Credit Ratings ◽  
Short Term ◽  
Rating Agencies ◽  
Thomson Reuters ◽  
The Impact ◽  
The International Monetary Fund

Covid-19 Impact on Countires’ Outlooks and Credit Ratings The aim of the study is to examine the impact of the financial crisis caused by COVID-19 on chang­es in outlooks and credit ratings of major rating agencies. The research hypothesis was as follows: the financial crisis caused by COVID-19 negatively affected the change in outlooks and credit ratings of countries. The study used long-term and short-term credit ratings and outlooks collected from the Thomson Reuters / Refinitiv database regarding liabilities expressed in foreign currency and macroeconomic data from the International Monetary Fund databases, for 2010–2021. The analysis was carried out using ordered logit panel models. The presented results showed a weak significant im­pact of the COVID-19 pandemic on credit rating. The agency that changed its notes in connection with this situation is Standard & Poor’s (S&P). However, the attitude responded to the situation un­der investigation. During the crisis, country ratings have become less sensitive to growing debt, which may be dictated by widespread loosening of fiscal policy. The rate of GDP growth has a par­ticular impact during the COVID-19 period in the event of a change of outlook. Rising inflation is particularly dangerous in the age of pandemics. It may be related to monetary policy easing.

South Africa's Mboweni will aid Ramaphosa reform push

2018 ◽  
Keyword(s):  
South African ◽  
Credit Rating ◽  
Short Term ◽  
Content Type ◽  
Rating Agencies ◽  
Reserve Bank ◽  
Freedom Fighters ◽  
Left And Right ◽  
The Government

Significance A former South African Reserve Bank (SARB) governor and minister of labour, Mboweni faces a crucial first few weeks in his new post as the government attempts to placate rating agencies and engineer an economic turnaround. Mboweni’s initial moves may be determined by Moody’s credit rating review expected today. Impacts In the short term, Mboweni’s appointment will be a boost for Ramaphosa’s bid for fiscal consolidation and growth. In the medium-to-long term, Mboweni will likely prove a more polarising figure inside the ANC than Nene. Allegations linking the Economic Freedom Fighters with a major banking scandal could give Mboweni and the ANC an early political 'win'. Mboweni's previous social media utterances could be further exploited by opponents, both left and right, in the months ahead.

scholarly journals LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 170
Author(s):  
Rafael Lahoz-Beltra ◽  
Claudia Corona López
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fourier Analysis ◽  
Sentiment Analysis ◽  
Emotional State ◽  
Fundamental Issue ◽  
Multivariate Statistical Methods ◽  
Simulation Experiments ◽  
Multivariate Statistical ◽  
Analysis Techniques

Currently, most chatbots are unable to detect the emotional state of the interlocutor and respond according to the interlocutor’s emotional state. Over the last few years, there has been growing interest in empathic chatbots. In other disciplines aside from artificial intelligence, e.g., in medicine, there is growing interest in the study and simulation of human emotions. However, there is a fundamental issue that is not commonly addressed, and it is the design of protocols for quantitatively evaluating an empathic chatbot by utilizing the analysis of the conversation between the bot and an interlocutor. This study is motivated by the aforementioned scenarios and by the lack of methods for assessing the performance of an empathic bot; thus, a chatbot with the ability to recognize the emotions of its interlocutor is needed. The main novelty of this study is the protocol with which it is possible to analyze the conversations between a chatbot and an interlocutor, regardless of whether the latter is a person or another chatbot. For this purpose, we have designed a minimally viable prototype of an empathic chatbot, named LENNA, for evaluating the usefulness of the proposed protocol. The proposed approach uses Shannon entropy to measure the changes in the emotional state experienced by the chatbot during a conversation, applying sentiment analysis techniques to the analysis of the conversation. Once the simulation experiments were performed, the conversations were analyzed by applying multivariate statistical methods and Fourier analysis. We show the usefulness of the proposed methodology for evaluating the emotional state of LENNA during conversations, which could be useful in the evaluation of other empathic chatbots.

scholarly journals Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases

2022 ◽  
Vol 2022 ◽  
pp. 1-6
Author(s):  
Saud Aljaloud ◽  
Jalawi Alshudukhi ◽  
Khalid Twarish Alhamazani ◽  
Assaye Belay
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Cognitive Maps ◽  
Literary Analysis ◽  
Efficient Management ◽  
Wrong Diagnosis ◽  
Plant Monitoring ◽  
High Level ◽  
Productivity Gains ◽  
Plant Treatment

Farming is essential to the long-term viability of any economy. It differs in each country, but it is essential for long-term economic success. Only a few of the agricultural industry’s issues include a lack of suitable irrigation systems, weeds, and plant monitoring concerns as a consequence of efficient management in distinct open and closed zones for crop and plant treatment. The objective of this work is to carry out a study on the use of artificial intelligence and computer vision methods for diagnosis of diseases in agro sectors in the context of agribusiness, demonstrating the feasibility of using these techniques as tools to support automation and obtain productivity gains in this sector. During the literary analysis, it was determined that technology could improve efficiency, hence decreasing these types of concerns. Given the consequences of a wrong diagnosis, diagnosis is work that requires a high level of precision. Fuzzy cognitive maps were shown to be the most efficient method of utilizing bibliographically reviewed preferences, which led to the consideration of neural networks as a second option because this technique is the most robust in terms of the qualifying criteria of the data stored in databases.

scholarly journals Corporate Board Attributes, Shariah Board Attributes and Credit Rating: Evidence from Islamic Banks of Pakistan

10.26414/a090 ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 19-34
Author(s):  
Muhammad Mansoor ◽  
Nazima Ellahi ◽  
Qaiser Ali Malik
Keyword(s):  
Financial Institutions ◽  
Credit Rating ◽  
Rating Scale ◽  
Credit Ratings ◽  
Islamic Banks ◽  
Corporate Board ◽  
Short Term ◽  
Rating Agencies ◽  
Shariah Governance

Shariah Governance is an essential characteristic that differentiates Islamic financial institutions from Conventional financial institutions. The study’s purpose is to explore the effect of corporate governance attributes and Shariah board attributes on the long term and short-term credit rating of Islamic banks in Pakistan. The study develops six different models based on corporate board characteristics, Shariah board attributes and credit ratings, and collected data from annual reports of Pakistani Islamic banks for the period 2013- 2019. This study used Long term credit rating scale used by Grassa (2016) and, Ashbaugh-Skaife, Collins, and LaFond (2006), and developed a Short term credit rating scale. The study applied descriptive statistics, correlations and ordered logit regression. The results confirmed that corporate governance and Shariah governance attributes are significantly associated with the long term and short-term credit ratings of Islamic banks. The study concludes that credit rating agencies in Pakistan i.e. PACRA and JC-VIS, and other international credit rating agencies including Fitch, Moody and Standard & Poor’s must consider Shariah governance attributes as key determinants while assigning long term and short term credit ratings to Islamic banks.

Sign in / Sign up

Export Citation Format

Share Document