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Sovereign Credit Ratings Using Neural Networks Case Study

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Definition of sovereign ratings and its importance

A sovereign rating refers to a measure used to assess the level of risk involved in investing in a company or financial institution. In most cases, it is used by investors who want to assess the amount of uncertainty involved before investing in a company. Moreover, a sovereign rating is vital for the assessment of the credit risk of a nation and the companies within its borders. The ratings enable The Bank for International Settlements to determine capital sufficiency.

Role of rating agencies

Rating agencies assign sovereign ratings to foreign- currency by striking a balance between the factors of the constant and those brought about by the theory. It is essential to establish sovereign capability and willingness to handle an external debt.

Role of neural networks in sovereign ratings and ideology of whether to entirely rely on neural network prediction

Artificial neural network ideology originated from the human neuron physiology. A series of neurons pass information to the nucleus using synapses and the information is interpreted as an output. The input of various variables in a credit rating is re-laid out and output obtained. This is termed as a prediction.

Neural networks are used to classify sovereign ratings according to the percentages of correctness in performance. Rating agencies use neural networks as the best models for sovereign rating. The neural networks are very accurate inaccuracy of sovereign ratings and when tested with two models their accuracy ranges to 90 percent.

One can rely entirely on the neural networks since the sample size for obtaining the most suitable had five thousand epochs. The level of correctness and accuracy of predictions will be high. Since the sovereign ratings are essential for the prediction of credit risks in firms and countries, it is vital to have accurate predictions to avoid huge losses of the capital invested by investors. In conclusion, the neural networks will be significant in Sovereign credit ratings.

Conclusion of the outcomes in neural network-based rating prediction experiments

Prediction experiments were long but carefully done to come to the best model. The classification-based neural network is 40.4 percent on average and the regression-based neural network is 34.65percent on average. In one instance, the appropriately categorized ratings were attained at sixty-seven percent and seventy percent cases, while achieving 63.9 percent and sixty percent cases for the cataloging and regression models instantaneously.

Looking at both of the experiments carried out at the first notch, the averages are above 50% correctness for both classification and regression models. If the results for the first notch are as shown, then the percentages for the second and subsequent notches will rise. One notices that the regression model attains a lower percentage of incorrectly classifying ratings than the classified model. These experiments indicate that sovereign credit rating can depend on these models for accurate predictions.

Factors to consider in rating India’s sovereign credit and the applicability of the neural network when credit rating India

The factors considered in credit rating include financial rates, the economic, political, regulatory environment, and industry trends. As a credit analyst for Standard and Poor’s, I would use the following factors to rate India: its poor economic fundamentals, bankruptcy, and lack of political action.

India stands at a very critical position such that any economic downfall will drive it below the investing level. This will prompt the country to sell its securities to boost investor security to invest within its borders. This indicates that the economy of India is unreliable and contributes significantly to the prediction of high credit risk.

Food inflation in India is the major contributor to poor economic status. If the country cannot feed its citizens using its recourses, then its financial status is low. Therefore, I would rate India’s Sovereign credit as a big risk using its economic status and incapability of its government as my major factors. This is because they don’t have enough resources to sustain the country itself, so investors may be faced with a big risk in investing in India. Its government has not come up with a solution to raise its economic status. If the countries own leadership is poor, it will be very hard for the investors to invest in it since they will be foreigners.

Using Artificial neural networks to rate India’s sovereign credit, I would put in different variables of factors, configure them using an algorithm, just like synapses, to pass information to the nucleus. Then come up with the final output which is the prediction. By using the neural networks being able to acquire a prediction with accuracy. This because the variables are well studied and configured before being re-laid. Just like in a human the information given as output in the brain has high accuracy. Therefore, the sovereign rating of credit in India can give a reliable picture of the returns expected by the investors in the domestics companies.

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IvyPanda. (2021, February 19). Sovereign Credit Ratings Using Neural Networks. Retrieved from https://ivypanda.com/essays/sovereign-credit-ratings-using-neural-networks/

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"Sovereign Credit Ratings Using Neural Networks." IvyPanda, 19 Feb. 2021, ivypanda.com/essays/sovereign-credit-ratings-using-neural-networks/.

1. IvyPanda. "Sovereign Credit Ratings Using Neural Networks." February 19, 2021. https://ivypanda.com/essays/sovereign-credit-ratings-using-neural-networks/.


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IvyPanda. "Sovereign Credit Ratings Using Neural Networks." February 19, 2021. https://ivypanda.com/essays/sovereign-credit-ratings-using-neural-networks/.

References

IvyPanda. 2021. "Sovereign Credit Ratings Using Neural Networks." February 19, 2021. https://ivypanda.com/essays/sovereign-credit-ratings-using-neural-networks/.

References

IvyPanda. (2021) 'Sovereign Credit Ratings Using Neural Networks'. 19 February.

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