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When the COVID-19 threat loomed worldwide, stock markets crashed and rebounded quicker than ever. In 2020, between mid-February and mid-March, the Dow lost a staggering 37% of its value (it crossed 30,000 for the first time just eight months later). The Standard and Poor's 500 sunk 34% during the pandemic-triggered crash in March, the quickest decline of its kind in history. It rallied to reach an all-time high in August of the same year. After crashing over 1,000 points 14 times in 2020, the Sensex soared past the 50,000 and 60,000 levels in 2021. How did financial markets recover at record speeds, despite the global uncertainty, job losses, businesses shutting down, and the worst economic recession in decades? The answer is optimism. The stock market is rarely a reflection of the real economy but rather the investors’ perception of the economy in the future.
We do a deep dive into the main reasons for the divergence between the financial markets and the real economy and the role credit rating agencies (CRAs) can play in bridging this gap and supporting economic recovery.
How ‘real’ is the real economy?
The real economy is a cycle, kept in motion by consumer spending, as shown in ￼￼Figure 1. For example, suppose a garment factory gets a large order from fashion outlets due to an upcoming festive season. In that case, it will need to purchase material and hire workers to execute the order on time. The factory owner may approach a financial institute for funding, with a plan to repay the loan with the earned profits. The workers who receive the wages may take a loan to buy a home or a vehicle, planning to repay it through their salary. This cycle can continue without any disruption in a world with no uncertainties.
However, during an economic downturn, people worry about losing their jobs and cut back on spending. Businesses start losing money and may have to lay off workers. For example, the COVID-19 pandemic slammed the breaks on demand generation in almost every industry. Many businesses stopped production, leading to unprecedented job losses. According to the International Labour Organization, the global labor income (before considering income support measures) in 2020 declined by 8.3%, which amounts to USD 3.7 trillion, or 4.4% of the global gross domestic product (GDP). In 2022, the economic crisis is said to result in global unemployment of more than 200 million. How does the economy overcome this slump? Governments make it cheaper for businesses and consumers to borrow money to pay off existing debts, shop, and invest in the stock market, and in turn, support businesses and job creation.
To boost consumer spending and fast-track economic recovery, governments may implement several stimulus packages. For instance, during the COVID-19 pandemic, in the Unites States, the Federal Reserve slashed interest rates and injected USD 1.5 trillion into money markets. By November 2020, the Central Government and Reserve Bank of India (RBI) had provided a total fiscal stimulus of INR 29.87 lakh crore (about USD 400 billion) since the beginning of the COVID-19 pandemic—almost 15% of India’s GDP. There are some questions confronting us today. Does liquidity really power long-term economic recovery or create a new type of crisis? Does it address the actual economic reality? The RBI has also expressed concern about a possible stock market bubble due to the inflated prices of risky assets created by the sudden influx of monetary and fiscal stimulus during 2020-21. Financial intermediaries and lending corporations may find it hard to differentiate between good and bad borrowers. How will they ensure support for the suitable entities in the most critical growth sectors? Are these entities borrowing to keep their lights on or for future growth and profitability?
Stimulus packages are likely to have a more profound impact on economic recovery if lending institutions can answer these basic questions, and credit rating agencies hold the key to getting the right answers.
Credit rating agencies: Playing an expanded role in economic recovery
The 2008 global financial crisis was a wake-up call for the worldwide economy. As a result of a series of events, there was a sharp increase in mortgage-backed securities, eventually leading to a stock market crash. Could this have been prevented? Better regulation of high-risk investments and an in-depth assessment of borrowing entities could have helped. CRAs have access to large amounts of financial data and repayment history, as shown in Figure 2.
Figure 2: Credit rating agencies—the bridge between the financial
markets and the real economy
By throwing light on the ability of an individual or entity to generate healthy returns and repay on time, banks can determine the risk premium. A higher risk premium means higher interest rates and vice versa.
Here are some of the ways CRAs can reduce the deviation between financial markets and the economy and reimagine their role to prevent an economic downturn:
Suggest measures to reduce the impact of a sudden change
The COVID-19 pandemic brought the construction industry to a grinding halt, placing companies under immense cash flow stress. Industry workers lost jobs or faced wage cuts, leading to a possibility of payment defaults on borrowings. In such a scenario, the CRA can provide a holistic view of financial health on both sides – individuals and entities.
This is true for most countries of the world.
An extension of the above logic would mean that the financial sectors are the backbone of the economic cycle, and the CRA is the fintech-based monitoring system that is attached to economic players such as consumers and producers. In this case, CRAs can provide insight and drive the development of a technology-driven model that will help all three.
For example, suppose the business intelligence system of the CRA detects unusual loan defaults from those employed in manufacturing. In that case, it should trigger an event-based reporting on various parameters that facilitate a better understanding of defaults to manufacturer loans as well. The CRA can thus help all the three entities through the AI model by highlighting the stress on the latter and proposing a model to relieve the “stress”.
Offer predictive support to prevent economies from free-falling
CRAs need to take a more proactive approach by keeping a close watch on symptoms that point to an impending economic upheaval. For example, in the case of mortgage defaulters, CRAs can guide financial institutions on the following best options, such as renegotiating the repayment terms or using government stimulus to offer immediate relief.
Adding value beyond the ratings: CRAs and digital transformation
CRAs are positioned uniquely and offer a 360-degree view of the financial system and economy. They can use this advantage to provide insights for modelling solutions to aid economic revival.
CRAs have started using artificial intelligence (AI) systems, powered with natural language processing (NLP) and machine learning (ML) capabilities, to:
Analyze large volumes of dynamic, unstructured data with complex semantic extractions
Create more efficient rating models and uncover deeper insights into the risk exposure, financial health and repayment ability of individuals and entities in real time.
For example, CRISIL, India’s first credit rating agency, has diversified significantly over the years and today offers risk-based advisory and modeling services to its clients.
CRAs can play a pivotal role in the new edge banking of DeFi (decentralized finance) whereby lenders and borrowers come on to the same platform with minimal cost overheads and reduced borrowing costs. For example, the low-income salaried class can be financed for household needs such as refrigerator, TV, education and so on by small finance companies by at least 200 basis points lower than the market rate.
CRAs can enable government agencies to conduct forensic audits for financial fraud by creating patterns and trails on financial transactions. For example, a consortium of banks gives a ship building company a huge loan for a specific business purpose, and it siphons off the amount to more than 90 companies that are not related to the business – this can be identified well before the fraud is publicly discovered.