Credit Rating Agencies
Quantitative and Qualitative Credit
In the recent past, credit rating has become a significant aspect in the financial industry where credit ratings such as Moody’s plays invaluable roles in providing the investors with information about where to invest securities and debts and where not to invest. When financial institutions are evaluating the risk levels that are associated with the issuance of the bonds, they will look at the credit firms from various credit rating agencies. Many investors tend to look for a tradeoff between risk and return on their investments, which means that they would typically go for a higher coupon rate for the bonds that have a poor credit rating. Consequently, credit rating has effectively enabled the financial industry, providing debt and security instruments to measure the risks associated with the issuance of these instruments.
Why Credit Rating should not be based only on past financial statements
Moody’s argues that their methodology uses both quantitative and qualitative approaches to arrive at the rating outcomes. The idea of credit rating started as early as the 20th century when credit rating agencies were created to provide credit rating reports for organizations. In the following years, many credit rating agencies have been formed, with the leading ones being Moody’s, Fitch and Standards Poor’s. Moody’s model enables secure computation and implementation of debt policy and guidelines. Through credit information organizations and especially financial service industry issuing debt can know the best place to invest their debt or securities. Unlike before, credit rating has made decisions centered on debt and bond issuance
easier.
The credit rating process has mainly depended on past and present financial statements to rate credit and to provide information to securities industry service providers. However, this process has been criticized as it has been associated with the financial crisis that led to fall of many organizations including mortgage-lending Lehman Brothers. Though many players such as lenders, regulators and issuers and other macro effects may have contributed to the credit crunch in 2009, credit rating agencies such as Moody’s have been argued to be the source of mispricing and malfunctioning credit markets as they depend on few issues when providing rating information to financial service industry issuing debts. Credit ratings agencies have, over time misstated complex structured debt instruments and many other subprime mortgages and for operating a biased model of business in the oligopolistic market.
Rating that is done on present and past historical data can be labeled as static process. The prediction of the health of the company through rating is just momentarily and anything may happen after the ratings have been assigned to the company. Depending on the future results on the rating process defeats the very aim of indicating risk of the debt through rating. Many changes tend to take place in political, economic and in government policy framework which directly affects working of the company. Therefore, rating should not be solely dependent on past and present financial statements but on other factors such as policy frameworks and likely political and economic factors.
Time factor affects rating and may sometime lead to misleading conclusions. A company in a particular industry may, for instance, be in an adverse temporal condition and end up being given low rating. This may adversely affect the interest of the company. Once the company has been rated and is not in a position to maintain the working results and performance, credit rating agencies would review the grade and down grade the rating which would result to impairment of the image of the company. When rating agencies are grading credit worthiness of the various firms, they pay attention into issues such as level of debt, character of the firm and willingness to pay by the firm as well as the financial ability of the firm to meet its obligations. Despite the fact that these factors are based on information provided in the income statement and balance sheet other forms of information such as the attitude towards paying debts demands critical scrutiny.
How Machine Learning may be used for credit scoring via quantitative financial statement Analysis
Corporate insolvency may have devastating effects on the economy. With increased number of companies making expansion into the international market and capitalize on foreign resources, any bankruptcy of a multinational corporate may lead to disruption in the global financial ecosystem. Many corporations do not instantaneously fail and objective measures including rigorous qualitative and quantitative data may help in the identification of the financial risk of the company. Gathering and storing financial data has become less difficult with the breakthrough in technology. The main challenge remains in the mining of the relevant information about the health of the financial service debt provider. However, in the recent past machine learning has become a popular field in big data analytics given the success in learning complicated models.
The effects of machine learning to organizations are broad. Other than being used in sales targets and market segmentation, it may be used to optimize inventory based on demanding forecasting among other services. Any unbiased objective prediction of the probability of the company going bankrupt may be useful management. Various methods have been proposed for measuring likelihood of bankruptcy. Statistical methods such as logistic regression and intelligent systems such as the vector machines are some of the methods to resort to in an attempt to get credit rating.
Machine Learning as a subset of data science uses statistical model to draw insights and make predictions. The magic with machine learning is that it is able to learn from experience without being explicitly programmed. To use the model, one has to feed the data to the selected model and the model can adjust its parameters automatically. Data scientists have trained machine learning models with the existing datasets and then adopt well-trained models top real-life situations. The more data being fed into the machine, the more accurate the results would be. Incidentally, many organizations in the financial industry have large datasets on factors such as customers, bills, credit transactions, and transfers. This becomes a perfect fit for learning machines in generating credit rating reports.
The advantage of machine learning has major learning when it comes to credit rating. One of the advantages is that it reduces the operational costs due to the fact that automation takes fewer resources. There is also increased efficiency due to enhanced experience of machine learning g as well as its better productivity. There is also better compliance and reinforced security when learning machines are used. Despite these advantages, machine learning presents some shortfalls. One of the shortcomings is that many businesses tend to have unrealistic expectations towards the machine learning and the value for the organization. More so, research and development on machine learning is costly.
Advantages and Disadvantages of turning financial Statements into credit rating categories
A company that has its financial variables highly rated has the opportunity to reduce the cost of borrowing from the public by quoting lesser interest rates on fixed bonds or debentures as those investors with low preference for risk would invest in such safe securities through yielding lower returns. This process also gives a wider view of the company’s real position with regard to financial viability. A company whose instruments have been rated highly may approach the investors for the purpose of mobilizing resources. Investors in various societal strata may be attracted by highly rated instruments as investors understands the level of certainty with regard to payment of principal and interest on security instruments with better rating.
This rating also provides motivation for growth of the company as promoters feel confident in their own efforts and are encouraged to undertake some expansion of their own operations. With better view being given by credit rating, a company is able to mobilize funds from banks, public and other institutions. Essentially, credit rating provides a clear picture of how the financial viability of the company or the issuer. It is like a financial anatomy that enables the organization measure the riskiness of the financial debt or instruments.
One of the disadvantages of turning financial statement variables into credit rating categories is that this process may end up being subjected to biased misrepresentation. In absence of quality rating, credit rating is argued to be a curse in the capital markets. Carrying out an analysis of the company must have no links with any interested parties or the company. This would ensure that reports produced on credit rating of the financial statement variables are judicious and impartial. Additionally, rating that is being done by two credit rating agencies for same instruments of the issuer company in many circumstances would not produce identical results. Such differences may occur because the value judgment differs on quantitative aspects during the analysis.
Conclusion
Credit rating plays a a major role in forewarning financial debt service industries on any investment decisions they make of their debt instruments. Today, in most cases debt instruments are being issued on the basis of credit rating. Despite the fact that credit rating is somewhat subjective, it is imperative to note that it has helped in decision making with regard to bonds and security issuance. It ha s also encouraged proper and right financial disclosure for many organizations. It is vital that companies issuing debt instruments take advantages of machine learning in credit leaning and overcomes some of the shortfalls presented by credit rating agencies.
References
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Baum, C.F., Schäfer, D., and Stephan, A., 2016. Credit rating agency downgrades and the Eurozone sovereign debt crises. Journal of Financial Stability, 24, pp.117-131.
Blinder, A.S., 2013. After the music stopped: The financial crisis, the response, and the work ahead (No. 79). Penguin Group USA.
Baldwin, R.E., and Giavazzi, F., 2015. The Eurozone crisis a consensus view of the causes and a few possible solutions. London: CEPR Press.
Chodorow-Reich, G., 2013. The employment effects of credit market disruptions: Firm-level evidence from the 2008–9 financial crisis. The Quarterly Journal of Economics, 129(1), pp.1-59.