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- W4297235787 abstract "Purpose This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context. Design/methodology/approach The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019. Findings The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms. Practical implications The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India. Social implications Better bond ratings may mitigate corporate defaults. Originality/value Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market." @default.
- W4297235787 created "2022-09-28" @default.
- W4297235787 creator A5007822056 @default.
- W4297235787 creator A5017559376 @default.
- W4297235787 creator A5087170695 @default.
- W4297235787 date "2022-09-27" @default.
- W4297235787 modified "2023-10-14" @default.
- W4297235787 title "Bond rating determinants and modeling: evidence from India" @default.
- W4297235787 cites W1128184534 @default.
- W4297235787 cites W117254788 @default.
- W4297235787 cites W1481519903 @default.
- W4297235787 cites W1499573490 @default.
- W4297235787 cites W1512135724 @default.
- W4297235787 cites W1575456056 @default.
- W4297235787 cites W1673066967 @default.
- W4297235787 cites W1684755199 @default.
- W4297235787 cites W1964813156 @default.
- W4297235787 cites W1966577366 @default.
- W4297235787 cites W1968023063 @default.
- W4297235787 cites W1970591008 @default.
- W4297235787 cites W1972443234 @default.
- W4297235787 cites W1974244766 @default.
- W4297235787 cites W1990113270 @default.
- W4297235787 cites W1998394599 @default.
- W4297235787 cites W1998452394 @default.
- W4297235787 cites W2003864267 @default.
- W4297235787 cites W2005596732 @default.
- W4297235787 cites W2009411066 @default.
- W4297235787 cites W2012659999 @default.
- W4297235787 cites W2016031348 @default.
- W4297235787 cites W2018850114 @default.
- W4297235787 cites W2027731080 @default.
- W4297235787 cites W2028465564 @default.
- W4297235787 cites W2029869759 @default.
- W4297235787 cites W2034067039 @default.
- W4297235787 cites W2034465910 @default.
- W4297235787 cites W2036547589 @default.
- W4297235787 cites W2047869949 @default.
- W4297235787 cites W2050532363 @default.
- W4297235787 cites W2050831067 @default.
- W4297235787 cites W2051455168 @default.
- W4297235787 cites W2059296300 @default.
- W4297235787 cites W2067092736 @default.
- W4297235787 cites W2072875480 @default.
- W4297235787 cites W2073109200 @default.
- W4297235787 cites W2079492342 @default.
- W4297235787 cites W2079553537 @default.
- W4297235787 cites W2081180521 @default.
- W4297235787 cites W2083352970 @default.
- W4297235787 cites W2083396159 @default.
- W4297235787 cites W2085831731 @default.
- W4297235787 cites W2087318681 @default.
- W4297235787 cites W2093829413 @default.
- W4297235787 cites W2095092051 @default.
- W4297235787 cites W2107954682 @default.
- W4297235787 cites W2115568940 @default.
- W4297235787 cites W2124532504 @default.
- W4297235787 cites W2129054434 @default.
- W4297235787 cites W2137119394 @default.
- W4297235787 cites W2137334813 @default.
- W4297235787 cites W2152189064 @default.
- W4297235787 cites W2163094209 @default.
- W4297235787 cites W2165063012 @default.
- W4297235787 cites W2171762692 @default.
- W4297235787 cites W2313081741 @default.
- W4297235787 cites W2316178584 @default.
- W4297235787 cites W2324188699 @default.
- W4297235787 cites W2501505505 @default.
- W4297235787 cites W2545395668 @default.
- W4297235787 cites W2577798205 @default.
- W4297235787 cites W2579061498 @default.
- W4297235787 cites W2616855634 @default.
- W4297235787 cites W2781607813 @default.
- W4297235787 cites W2788025656 @default.
- W4297235787 cites W2798262432 @default.
- W4297235787 cites W2887477864 @default.
- W4297235787 cites W2888680315 @default.
- W4297235787 cites W2911964244 @default.
- W4297235787 cites W2916020747 @default.
- W4297235787 cites W2945422928 @default.
- W4297235787 cites W2953276351 @default.
- W4297235787 cites W2954213112 @default.
- W4297235787 cites W2954214241 @default.
- W4297235787 cites W2988906881 @default.
- W4297235787 cites W2995588700 @default.
- W4297235787 cites W3010059221 @default.
- W4297235787 cites W3021889044 @default.
- W4297235787 cites W3045991306 @default.
- W4297235787 cites W3046045515 @default.
- W4297235787 cites W3046234331 @default.
- W4297235787 cites W3081743379 @default.
- W4297235787 cites W3111943090 @default.
- W4297235787 cites W3121234595 @default.
- W4297235787 cites W3122348317 @default.
- W4297235787 cites W3123033635 @default.
- W4297235787 cites W3123300772 @default.
- W4297235787 cites W3124580702 @default.
- W4297235787 cites W3125195410 @default.