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Journal articles on the topic 'Dynamic rating'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Reb, Jochen, and Gary J. Greguras. "Understanding performance ratings: Dynamic performance, attributions, and rating purpose." Journal of Applied Psychology 95, no. 1 (2010): 213–20. http://dx.doi.org/10.1037/a0017237.

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2

Khromova, Ella. "Dynamic Mapping of Probability of Default and Credit Ratings of Russian Banks." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 14, no. 4 (2020): 31–46. http://dx.doi.org/10.17323/j.jcfr.2073-0438.14.4.2020.31-46.

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Investors are interested in a quantitative measure of banks’ credit risk. This paper maps the credit ratings of Russian banks to default probabilities for different time horizons by constructing an empirical dynamic calibration scale. As such, we construct a dynamic scale of credit risk calibration to the probability of default (PD).Our study is based on a random sample of 395 Russian banks (86 of which defaulted) for the period of 2007-2017. The scale proposed by this paper has three features which distinguish it from existing scales: dynamic nature (quarterly probability of default estimates

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3

Oehler, Andreas, Andreas Höfer, Matthias Horn, and Stefan Wendt. "Do mutual fund ratings provide valuable information for retail investors?" Studies in Economics and Finance 35, no. 1 (2018): 137–52. http://dx.doi.org/10.1108/sef-05-2017-0120.

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Purpose Retail investors use information provided by mutual fund rating agencies to make investment decisions. This paper aims to examine whether the ratings provide useful information to retail investors by analyzing the rating migration and closure risk of mutual funds that received Morningstar’s mutual fund ratings from 2005 to 2012. Design/methodology/approach The research design differentiates between buy-and-hold investment strategies and dynamic investment strategies. To assess the information content of mutual fund ratings for buy-and-hold investment strategies, the rating migration ba

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4

Michiorri, Andrea, Huu-Minh Nguyen, Stefano Alessandrini, et al. "Forecasting for dynamic line rating." Renewable and Sustainable Energy Reviews 52 (December 2015): 1713–30. http://dx.doi.org/10.1016/j.rser.2015.07.134.

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5

Knorr-Held, Leonhard. "Dynamic Rating of Sports Teams." Journal of the Royal Statistical Society: Series D (The Statistician) 49, no. 2 (2000): 261–76. http://dx.doi.org/10.1111/1467-9884.00236.

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6

Constantinou, Anthony Costa, and Norman Elliott Fenton. "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries." Journal of Quantitative Analysis in Sports 9, no. 1 (2013): 37–50. http://dx.doi.org/10.1515/jqas-2012-0036.

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AbstractA rating system provides relative measures of superiority between adversaries. We propose a novel and simple approach, which we call pi-rating, for dynamically rating Association Football teams solely on the basis of the relative discrepancies in scores through relevant match instances. The pi-rating system is applicable to any other sport where the score is considered as a good indicator for prediction purposes, as well as determining the relative performances between adversaries. In an attempt to examine how well the ratings capture a team’s performance, we have a) assessed them agai

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7

Jokivuolle, Esa, and Samu Peura. "Rating targeting and dynamic economic capital." Journal of Risk 12, no. 4 (2010): 3–13. http://dx.doi.org/10.21314/jor.2010.215.

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8

Talpur, Saifal, Carl Johan Wallnerström, Christer Flood, and Patrik Hilber. "Dynamic line rating for wind power." Journal of Renewable and Sustainable Energy 8, no. 1 (2016): 013114. http://dx.doi.org/10.1063/1.4942780.

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9

Islam, A. K. M. Anwarul, Amer S. Jaroo, and Frank Li. "Bridge Load Rating Using Dynamic Response." Journal of Performance of Constructed Facilities 29, no. 4 (2015): 04014120. http://dx.doi.org/10.1061/(asce)cf.1943-5509.0000620.

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10

Held, Leonhard, and Rainer Vollnhals. "Dynamic rating of European football teams." IMA Journal of Management Mathematics 16, no. 2 (2005): 121–30. http://dx.doi.org/10.1093/imaman/dpi004.

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11

Shaker, H., H. Zareipour, and M. Fotuhi-Firuzabad. "Reliability Modeling of Dynamic Thermal Rating." IEEE Transactions on Power Delivery 28, no. 3 (2013): 1600–1609. http://dx.doi.org/10.1109/tpwrd.2013.2252204.

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12

Weedy, B. M. "Dynamic current rating of overhead lines." Electric Power Systems Research 16, no. 1 (1989): 11–15. http://dx.doi.org/10.1016/0378-7796(89)90032-1.

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13

Margaris, Dionisis, and Costas Vassilakis. "Improving collaborative filtering's rating prediction accuracy by considering users' dynamic rating variability." International Journal of Big Data Intelligence 7, no. 2 (2020): 59. http://dx.doi.org/10.1504/ijbdi.2020.10029523.

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14

Margaris, Dionisis, and Costas Vassilakis. "Improving collaborative filtering's rating prediction accuracy by considering users' dynamic rating variability." International Journal of Big Data Intelligence 7, no. 2 (2020): 59. http://dx.doi.org/10.1504/ijbdi.2020.107373.

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15

BIELECKI, TOMASZ R., IGOR CIALENCO, and ISMAIL IYIGUNLER. "COLLATERALIZED CVA VALUATION WITH RATING TRIGGERS AND CREDIT MIGRATIONS." International Journal of Theoretical and Applied Finance 16, no. 02 (2013): 1350009. http://dx.doi.org/10.1142/s021902491350009x.

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In this paper we discuss the issue of computation of the bilateral credit valuation adjustment (CVA) under rating triggers, and in presence of ratings-linked margin agreements. Specifically, we consider collateralized OTC contracts, that are subject to rating triggers, between two parties — an investor and a counterparty. Moreover, we model the margin process as a functional of the credit ratings of the counterparty and the investor. We employ a Markovian approach for modeling of the rating transitions of the two parties to the contract. In this framework, we derive the representation for bila

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16

Tang, L., R. Ghafurian, and D. W. Purnhagen. "Dynamic rating of forced cooled transmission cables." IEEE Computer Applications in Power 5, no. 3 (1992): 50–53. http://dx.doi.org/10.1109/67.143275.

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17

Babś, Adam, and Tomasz Samotyjak. "Dynamic Rating of 110 kV Overhead Lines." Acta Energetica 28, no. 3 (2016): 4–9. http://dx.doi.org/10.12736/issn.2300-3022.2016301.

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18

Foss, S. D., and R. A. Maraio. "Dynamic line rating in the operating environment." IEEE Transactions on Power Delivery 5, no. 2 (1990): 1095–105. http://dx.doi.org/10.1109/61.53127.

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19

Shaker, Hamid, Mahmud Fotuhi-Firuzabad, and Farrokh Aminifar. "Fuzzy Dynamic Thermal Rating of Transmission Lines." IEEE Transactions on Power Delivery 27, no. 4 (2012): 1885–92. http://dx.doi.org/10.1109/tpwrd.2012.2193672.

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20

Hajeforosh, SeyedeFatemeh, and Math H. J. Bollen. "Uncertainty analysis of stochastic dynamic line rating." Electric Power Systems Research 194 (May 2021): 107043. http://dx.doi.org/10.1016/j.epsr.2021.107043.

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21

Calcagnì, A., and L. Lombardi. "Dynamic Fuzzy Rating Tracker (DYFRAT): a novel methodology for modeling real-time dynamic cognitive processes in rating scales." Applied Soft Computing 24 (November 2014): 948–61. http://dx.doi.org/10.1016/j.asoc.2014.08.049.

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22

Shimizu, Shigeo, Hirokazu Shimoda, and Chandra S. Sharma. "Theoretical Reevaluation of Basic Dynamic Load Rating and Rating Life for Linear Bearings." Tribology Transactions 51, no. 4 (2008): 460–68. http://dx.doi.org/10.1080/10402000802016926.

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23

Trofimovich, Pavel, Charles L. Nagle, Mary Grantham O’Brien, Sara Kennedy, Kym Taylor Reid, and Lauren Strachan. "Second language comprehensibility as a dynamic construct." 25 years of Intelligibility, Comprehensibility and Accentedness 6, no. 3 (2020): 430–57. http://dx.doi.org/10.1075/jslp.20003.tro.

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Abstract This study examined longitudinal changes in second language (L2) interlocutors’ mutual comprehensibility ratings (perceived ease of understanding speech), targeting comprehensibility as a dynamic, time-varying, interaction-centered construct. In a repeated-measures, within-participants design, 20 pairs of L2 English university students from different language backgrounds engaged in three collaborative and interactive tasks over 17 minutes, rating their partner’s comprehensibility at 2–3 minute intervals using 100-millimeter scales (seven ratings per interlocutor). Mutual comprehensibi

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24

Bizzotto, Jacopo, and Adrien Vigier. "Fees, Reputation, and Information Production in the Credit Rating Industry." American Economic Journal: Microeconomics 13, no. 2 (2021): 1–34. http://dx.doi.org/10.1257/mic.20180170.

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We compare a credit rating agency’s incentives to acquire costly information when it is only paid for giving favorable ratings to the corresponding incentives when the agency is paid up-front, i.e., irrespective of the ratings assigned. We show that, in the presence of moral hazard, contingent fees provide stronger dynamic incentives to acquire information than up-front fees and may induce higher social welfare. When the fee structure is chosen by the agency, contingent fees arise as an equilibrium outcome, in line with the way the market for credit rating actually works. (JEL D21, D82, D83, G

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25

Zambrano Pinto, Alvaro A., and Javier Rosero Garcia. "Assessment of the Impact of Dynamic Line Rating on Reliability Indices of Level I Systems." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 4 (2019): 286–97. http://dx.doi.org/10.2174/2352096511666180531103420.

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Background: This paper evaluates the impact of implementing dynamic line rating on the reliability indices of an electric power system. Methods: In order to obtain this goal, the implementation process of a dynamic rating model that considers the required variables for its functioning is built. A validation of the model is performed based on the measurements of an ASCR conductor in laboratory. Results: Subsequently, a reliability analysis that includes the use of the dynamic rating is structured and the impact on the reliability indices of dynamic rating within the electrical system is verifie

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26

Karimi, Soheila, Petr Musilek, and Andrew M. Knight. "Dynamic thermal rating of transmission lines: A review." Renewable and Sustainable Energy Reviews 91 (August 2018): 600–612. http://dx.doi.org/10.1016/j.rser.2018.04.001.

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27

Nemati, Hassan, A. Laso, M. Manana, Anita Sant'Anna, and Sławomir Nowaczyk. "Stream Data Cleaning for Dynamic Line Rating Application." Energies 11, no. 8 (2018): 2007. http://dx.doi.org/10.3390/en11082007.

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The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination o

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28

Saied, Mohamed M. "Assessing the dynamic rating of overhead transmission lines." European Transactions on Electrical Power 17, no. 5 (2007): 526–36. http://dx.doi.org/10.1002/etep.151.

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29

Reb, Jochen, and Gary J. Greguras. "Dynamic Performance and the Performance–Performance Rating Relation." Industrial and Organizational Psychology 1, no. 2 (2008): 194–96. http://dx.doi.org/10.1111/j.1754-9434.2008.00038.x.

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30

Anders, G. J., A. Napieralski, M. Zubert, and M. Orlikowski. "Advanced modeling techniques for dynamic feeder rating systems." IEEE Transactions on Industry Applications 39, no. 3 (2003): 619–26. http://dx.doi.org/10.1109/tia.2003.810648.

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31

Hou, Yu, Wei Wang, Zheng Wei, et al. "Research and application of dynamic line rating technology." Energy Reports 6 (December 2020): 716–30. http://dx.doi.org/10.1016/j.egyr.2020.11.140.

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32

Dorsch, Christian, Xiao Wang, and Ferit Küçükay. "Objective Rating of the Launch Behavior of Conventional, Hybrid and Electric Vehicles." Automotive Innovation 4, no. 1 (2021): 70–80. http://dx.doi.org/10.1007/s42154-020-00131-z.

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AbstractThe calibration of conventional, hybrid and electric drivetrains is an important process during the development phase of any vehicle. Therefore, to optimize the comfort and dynamic behavior (known as driveability), many test drives are performed by experienced drivers during different driving maneuvers, e.g., launch, re-launch or gear shift. However, the process can be kept more consistent and independent of human-based deviations by using objective ratings. This study first introduces an objective rating system developed for the launch behavior of conventional vehicles with automatic

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33

Bohen, Faye, and Ceri Woodrow. "Dynamic support database clinical support tool: inter-rater reliability." Advances in Mental Health and Intellectual Disabilities 14, no. 2 (2020): 25–32. http://dx.doi.org/10.1108/amhid-09-2019-0027.

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Purpose The dynamic support database (DSD) clinical support tool structures the risk of admission rating for individuals with intellectual disabilities. This study aims to investigate inter-rater reliability between multi-disciplinary health care professionals within the North West of England. Design/methodology/approach A small-scale quantitative study investigated reliability between raters on the DSD clinical support tool. A data set of 60 rating tools for 30 individuals was used. Descriptive statistics and Kappa coefficient explored agreement. Findings The DSD clinical support tool was fou

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34

PIRES, Danilo Machado, and Júlio Sílvio de Sousa BUENO FILHO. "CAN ELO RATINGS BE IMPROVED? A CASE STUDY WITH ELITE CHESS PLAYERS." REVISTA BRASILEIRA DE BIOMETRIA 38, no. 4 (2020): 483. http://dx.doi.org/10.28951/rbb.v38i4.462.

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   Originally designed as a way to reflect past performance, chess ratings are now widely used to reflect players strength with many important aspects in tournament scheduling, advertising and premium shares. The ELO system has been officially adopted by World Chess Federation (FIDE). We used Bayesian analysis of actual data from elite chess players to fit parametric statistical models that could subsidize proposals for rating system improvement. Although most of the considered options are not new, since based on well known preference models, the use of a weighed likelihood f

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35

Ma, Nan, Patrick Wang, Qin He, Wenjia Li, Ying Zheng, and Zhang Huan. "Prediction of Television Audience Rating Based on Fuzzy Cognitive Maps with Forward Stepwise Regression." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 07 (2017): 1750020. http://dx.doi.org/10.1142/s0218001417500203.

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The television audience rating is an important indicator of the quality of television programs and important reference for decision-television operator. As many factors that affect the ratings and the trends are complex, the article proposes a television rating mining predictive model based on fuzzy cognitive maps (FCMs) with forward stepwise regression. The FCMs use the causal relationship among various concept nodes to simulate the fuzzy reasoning, and enhance the dynamic behavior of the simulation system with its feedback mechanism, which is suitable for system to predict the trend of telev

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36

Naveed, Farrukh, Muhammad Kashif Khurshid, and Shahnawaz Saqib. "Corporate governance mechanism and the ratings of Islamic and conventional mutual funds: Evidence from Pakistan." International Journal of Islamic and Middle Eastern Finance and Management 13, no. 5 (2020): 769–86. http://dx.doi.org/10.1108/imefm-08-2018-0257.

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Purpose This study aims to analyze the impact of different governance characteristics on the ratings of both Islamic and conventional mutual funds. Design/methodology/approach This study used panel data ordered probit regression model. Furthermore, to capture the mutual funds rating persistence effect and address the issue of endogeneity dynamic panel model is used and the results are estimated using the generalized method of the moment (GMM) technique. Findings The results indicated that amongst the corporate governance characteristics, board size, the board independence, directors and instit

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37

Møller, Henrik. "Annoyance of Audible Infrasound." Journal of Low Frequency Noise, Vibration and Active Control 6, no. 1 (1987): 1–17. http://dx.doi.org/10.1177/026309238700600101.

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Contours of equal annoyance were determined for pure tones in the frequency range 4 – 31.5 Hz. The curves show a narrowing of the dynamic range of the ear at low frequencies. The same pattern is seen for equal loudness curves, and the results support the theory that the annoyance of infrasound is closely related to the loudness sensation. Annoyance ratings of 1/3 octave noise did not deviate from ratings of pure tones with the same sound pressure level. Combinations of audio and infrasonic noise were in general given a rating close to or slightly above the rating of the most annoying of the in

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38

Fischer, Thomas. "Market structure and rating strategies in credit rating markets – A dynamic model with matching of heterogeneous bond issuers and rating agencies." Journal of Banking & Finance 58 (September 2015): 39–56. http://dx.doi.org/10.1016/j.jbankfin.2015.03.010.

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39

Wang, Yan, Jie Su, Sulei Zhang, Siyao Guo, Peng Zhang, and Mingqing Du. "A Dynamic Risk Assessment Method for Deep-Buried Tunnels Based on a Bayesian Network." Geofluids 2020 (August 5, 2020): 1–14. http://dx.doi.org/10.1155/2020/8848860.

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In view of the shortcomings in the risk assessment of deep-buried tunnels, a dynamic risk assessment method based on a Bayesian network is proposed. According to case statistics, a total of 12 specific risk rating factors are obtained and divided into three types: objective factors, subjective factors, and monitoring factors. The grading criteria of the risk rating factors are determined, and a dynamic risk rating system is established. A Bayesian network based on this system is constructed by expert knowledge and historical data. The nodes in the Bayesian network are in one-to-one corresponde

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40

Inomata, Atsushi, Ryoichi Hara, Tsutomu Oyama, and Ikuo Kurihara. "Effectiveness of Application of Dynamic Rating to Transmission System." IEEJ Transactions on Power and Energy 126, no. 1 (2006): 36–42. http://dx.doi.org/10.1541/ieejpes.126.36.

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41

Dottori, F., M. L. V. Martina, and E. Todini. "A dynamic rating curve approach to indirect discharge measurement." Hydrology and Earth System Sciences 13, no. 6 (2009): 847–63. http://dx.doi.org/10.5194/hess-13-847-2009.

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Abstract. The operational measurement of discharge in medium and large rivers is mostly based on indirect approaches by converting water stages into discharge on the basis of steady-flow rating curves. Unfortunately, under unsteady flow conditions, this approach does not guarantee accurate estimation of the discharge due, on the one hand, to the underlying steady state assumptions and, on the other hand, to the required extrapolation of the rating curve beyond the range of actual measurements used for its derivation. Historically, several formulae were proposed to correct the steady-state disc

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42

Dottori, F., M. L. V. Martina, and E. Todini. "A dynamic rating curve approach to indirect discharge measurement." Hydrology and Earth System Sciences Discussions 6, no. 1 (2009): 859–96. http://dx.doi.org/10.5194/hessd-6-859-2009.

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Abstract. The operational measurement of discharge in medium and large rivers is mostly based on indirect approaches by converting water stages into discharge on the basis of steady-flow rating curves. Unfortunately, under unsteady flow conditions, this approach does not guarantee accurate estimation of the discharge due, on the one hand, to the underlying steady state assumptions and, on the other hand, to the required extrapolation of the rating curve beyond the range of actual measurements used for its derivation. Historically, several formulas were proposed to correct the steady-state disc

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43

Hall, J. F., and A. K. Deb. "Economic evaluation of dynamic thermal rating by adaptive forecasting." IEEE Transactions on Power Delivery 3, no. 4 (1988): 2048–55. http://dx.doi.org/10.1109/61.194016.

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44

Wallnerstrom, Carl Johan, Yalin Huang, and Lennart Soder. "Impact From Dynamic Line Rating on Wind Power Integration." IEEE Transactions on Smart Grid 6, no. 1 (2015): 343–50. http://dx.doi.org/10.1109/tsg.2014.2341353.

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45

Siswobusono, P., S. E. Chen, S. Jones, D. Callahan, T. Grimes, and N. Delatte. "SERVICEABILITY-BASED DYNAMIC LOAD RATING OF A LT20 BRIDGE." Experimental Techniques 28, no. 6 (2004): 33–36. http://dx.doi.org/10.1111/j.1747-1567.2004.tb00191.x.

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46

Douglass, D. A., A. A. Edris, and G. A. Pritchard. "Field application of a dynamic thermal circuit rating method." IEEE Transactions on Power Delivery 12, no. 2 (1997): 823–31. http://dx.doi.org/10.1109/61.584390.

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47

Nick, Mostafa, Omid Alizadeh-Mousavi, Rachid Cherkaoui, and Mario Paolone. "Security Constrained Unit Commitment With Dynamic Thermal Line Rating." IEEE Transactions on Power Systems 31, no. 3 (2016): 2014–25. http://dx.doi.org/10.1109/tpwrs.2015.2445826.

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48

Douglass, Dale, William Chisholm, Glenn Davidson, et al. "Real-Time Overhead Transmission-Line Monitoring for Dynamic Rating." IEEE Transactions on Power Delivery 31, no. 3 (2016): 921–27. http://dx.doi.org/10.1109/tpwrd.2014.2383915.

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49

Dawson, Leanne, and Andrew M. Knight. "Applicability of Dynamic Thermal Line Rating for Long Lines." IEEE Transactions on Power Delivery 33, no. 2 (2018): 719–27. http://dx.doi.org/10.1109/tpwrd.2017.2691671.

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50

Teh, Jiashen, and Ian Cotton. "Risk informed design modification of dynamic thermal rating system." IET Generation, Transmission & Distribution 9, no. 16 (2015): 2697–704. http://dx.doi.org/10.1049/iet-gtd.2015.0351.

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