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Credit Rating

Credit rating is a codified rating assigned to an issue by authorized credit rating agencies . These agencies have been promoted by well-established financial Institutions and reputed banks/finance companies. Credit rating is a relative ranking arrived at by a systematic analysis of the strengths and weaknesses of a company and debt instrument issued by the company, based on financial statements , project analysis, creditworthiness factors and future prospectus of the project and the company appraised at a point of time.

Objectives of Credit Rating

Credit rating aims to:

• Provide superior information to the investors at a low cost;
• Provide a sound basis for proper risk-return structure ;
• Subject borrowers to a healthy discipline, and
• Assist in the framing of public policy guidelines on institutional investment.

Thus, credit rating in financial services represent an exercise in faith building for the development of a healthy financial system .

Approaches to Credit Rating

Significance of Credit Rating

Credit rating is always project/instrument specific. Credit rating for different financial instruments issued by the same company at the same time can be different. In the same way credit rating for similar instruments issued by the same company at different times can also be different. Credit rating is useful for investors, banks and other financial institutions and investments advisers as it helps them taking business decisions. Credit rating by an authorized competent authority gives a bird’s eye view of financial strength of an organisation and its instruments. It is of considerable help to an investor in deciding whether his investment is likely to be safe.

As financial markets have grown increasingly complex and global and borrowers base has become increasingly diversified, investors and regulators have increased their reliance on the opinions of credit rating agencies. Credit ratings attempt to provide a consistent and reasonable rank ordering of relative credit risks, with specific reference to the instrument being rated.

Credit rating can be applied in the following areas/instruments:

• Equity shares
• Rating for banking sector
• Individual credit rating
• Rating for insurance sector
• New instruments, floating rate notes , index based bonds, long-term deep discount bonds , etc.
• Rating of intermediaries in financial services
• Securitization
• Rating of companies raising funds overseas.

It is expected that credit rating will assume multi-dimensional role covering all sectors of the economy which would include rating of products, services, suppliers, customers, management schools, merchant bankers , banks, health services, schools, political parties and politicians and so on.

Methodology of Credit Rating

The process of credit rating begins with the prospective issuer approaching the rating agency for evaluation. The experts in analyzing banks should be given a free hand and they will collect data and informant and will investigate the business strength and weaknesses in detail. The entire process of rating stands on the for of confidentiality and hence even the most confidential business strategies , marketing plans , future outlook etc., are revealed to the steam of analysis.

The rating is based on the investigation analysis, study and interpretation of various factors. The world of investment is exposed to the continuous onslaught of political, economic, social and other forces which does not permit any one to understand sufficiently certainty. Hence a logical approach to systematic evaluation is compulsory and within the framework of certain common features the agencies employ different methodologies. The key factors generally considered are listed below:

1. Business Analysis or Company Analysis

This includes an analysis of industry risk, market position of the company, operating efficiency of the company and legal position of the company.

• Industry risk: Nature and basis of competition , key success factors; demand supply position; structure of industry; government policies, etc.
• Market position of the company within the Industry: Market share; competitive advantages , selling and distribution arrangements; product and customer diversity etc.
• Operating efficiency of the company: Locational advantages ; labor relationships ; cost structure and manufacturing as compared to those of competition.
• Legal Position: Terms of prospectus; trustees and then responsibilities; system for timely payment and for protection against forgery/fraud, etc.

2. Economic Analysis

In order to evaluate an instrument an analyst must spend a considerable time in investigating the various economic activities and also analyze the characteristics peculiar to the industry, whose issue the analyst is concerned with. It will be an error to ignore these factors as the individual companies are always exposed to changing environment and the economic activates affect corporate profits, attitudes and expectation of investors and the price of the instrument. hence the relevance of the economic variables such as growth rate, national income and expenditure cannot be ignored. The analysis, while doing the economic forecasting use surveys, various economic indicators and indices.

3. Financial Analysis

This includes an analysis of accounting, quality, earnings, protection adequacy of cash flows and financial flexibility.

• Accounting Quality: Overstatement/under statement of profits ; auditors qualification; methods of income recognition’s inventory valuation and depreciation policies, off balance sheet liabilities etc.
• Earnings Protection: Sources of future earnings growth; profitability ratios ; earnings in relation to fixed income changes.
• Adequacy of cash flows: In relation to dept and fixed and working capital needs ; variability of future cash flows ; capital spending flexibility working capital management etc.
• Financial Flexibility: Alternative financing plans in ties of stress; ability to raise funds asset redeployment.

4. Management Evaluation

• Track record of the management planning and control system, depth of managerial talent, succession plans .
• Evaluation of capacity to overcome adverse situations
• Goals , philosophy and strategies .

5. Geographical Analysis

• Location advantages and disadvantages
• Backward area benefit to the company/division/unit

6. Fundamental Analysis

Fundamental analysis is essential for the assessment of finance companies. This includes an analysis of liquidity management, profitability and financial position and interest and tax sensitivity of the company.

• Liquidity Management: Capital structure ; term matching of assets and liabilities policy and liquid assets in relation to financing commitments and maturing deposits.
• Asset Quality: Quality of the company’s credit-risk management; system for monitoring credit; sector risk; exposure to individual borrower; management of problem credits etc.
• Profitability and financial position: Historic profits, spread on fund deployment revenue on non-fund based services accretion to reserves etc.
• Interest and Tax sensitivity: Exposure to interest rate changes, hedge against interest rate and tax low changes, etc.

Country’s Credit Rating

Country’s credit rating denotes its ability to source debt from the international market at a reasonable cost. Low rated nations will have discounts, offer high yield and are treated as risky investment. Risk relates to default. Country’s credit rating involves evaluation of external financial accounts and macro economic factors and is directed towards future trends. Credit rating of any country involves evaluation of:

• Economic growth and development : Gross national product and gross domestic product , population growth , Infrastructure development, good financial management, saving growth rate, industrial production, agricultural production, growth of services sector etc.
• Balance of trade and balance of payments : Export products, export prices, diversification of products and export market, global competition, import substitution, etc.
• Debt service ratio : This indicates the country’s external vulnerability. This is a ratio of external debt to total external earnings including export earning and earning from tourism, etc.
• Debt composition : Soft loans, commercial borrowings , interest rate structure, proportion of external debt .
• Liquidity : Level of reserves, foreign exchange reserves, import coverage ratio, currency backed by assets such as gold.
• Political and internal stability : Socio-religious conflicts, majority government strong opposition, unequal economic distribution, relations with neighboring countries, political factors are not predictable and is prone to unexpected events.
• Inflation and price stability.

Political challenges, economic transformation and policy consensus, fiscal imbalances and imposing public sector debt burdens are all factors which enhance or inhibit the credit rating of a country while political and economic forces are clearly a key determination of sovereign credit risk in emerging market countries, the financial pressures due to fiscal indiscipline pose threat to liquidity problems and default. Fiscal control is the key indicator of improving or deteriorating credit quality.

Drawbacks of Credit Rating

Following are some of the drawbacks of credit rating:

• The ratings process attempts to provide a guidance to investors/creditors in determining the risks associated with the instrument/credit obligation. It does not attempt to provide a recommendation and does not take into account factors like market prices, personal risk/reward preferences that might influence investment decisions .
• The ratings process is based on certain primitives. The agency, for instance, does not perform an audit. Instead, it has to rely solely on information provided by the user. Consequently, to the extent that the information provided is inaccurate and incomplete, the rating process is compromised.
• To the extent that a certain instrument of a specific company attracts a lower rating, the company has an incentive to shop around for the best possible rating, compromising the authenticity of the rating process itself.

Credit Rating Agencies in India

The concept of credit rating has been widely discussed and debated in India in recent times. Since the setting up of the first credit rating agency. Credit Rating and Information Services of India Ltd. (CRISIL) in India in 1987, there has been a rapid growth of credit rating agencies in India. The major players in the Indian market, apart from CRISIL include Investment Information and Credit Rating agency of India Ltd. (ICRA) , promoted by IDBI in 1991 and Credit Analysis and Research Ltd. (CARE), promoted by IFCI in 1994. Duff and Phelps has tied up with two Indian NBFCs to set up Duff and Phelps Credit Rating India (P) Limited in 1996.

Major International Credit Rating Agencies

As capital flows have become increasingly global and turbulence in one economy has had contagion effects across the globe, credit ratings have spread outside the domain of the home country to overseas markets. Credit ratings are in use in the financial markets of most developed economies and several emerging market economies as well. The principal characteristics of the major internationally known rating agencies are as follows:

Over time, the agencies have expanded the depth and frequency of their coverage. The leading U.S. credit rating agencies rate not ply the long-term bonds issued by corporate in the U.S., but also wide variety of other debt instruments including, for example, municipal bonds, asset-backed securities, private placements, commercial paper programmes and bank certificates of deposit (CDs). In addition, the leading rating agencies also play a major role in evaluating sovereign ratings.

Most of the rating agencies have long had their own symbols. Some of them use alphabets; others use numbers; many use a combination of both for ranking the risk of default. The default risk varies from extremely safe to highly speculative. Gradually, major agencies has emerged to provide finer rating gradations to help investors distinguish more carefully among issuers. Standard & Poor Corporation in 1974 and Moody’s in 1982 started attaching plus and minus symbols to their ratings. Other modifications of the grading scheme-including the addition of a ‘credit watch’ category to denote that a rating is under review-have also become standard.

Related posts:

• Role of Credit Rating Agencies in Securitization
• Merchant Banking Services: Credit Syndication
• Credit Management – Managing Trade Credit and Accounts Receivable in Business
• Point Rating Method of Job Evaluation
• Types of Packing Credit (Pre-Shipment Credit)
• Buyers Credit and Suppliers Credit
• Introduction to Commercial Credit Analysis
• Credit Derivatives – Meaning and Definition
• Credit Linked Notes (CLN)
• Difference between Cash Credit and Overdraft

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In response to ongoing changes in the banking industry, Moody’s Ratings continues to enhance its bank rating methodology in order to maintain the relevance and accuracy of credit analysis and ratings.

Corporate Governance in Bank Ratings Infographic

As part of Moody’s bank rating methodology, governance is assessed under a Corporate Behavior framework. Moody’s currently applies Corporate Behavior adjustments to 75 banks globally, most of which are negative.

Proposed changes to the banks methodology

Moody’s Investors Service is seeking feedback by May 10, 2021 from market participants on proposed changes to the Advanced Loss Given Failure component of its Banks Methodology. This proposal includes revisions that were proposed in a March 2020 Request for Comment as well as some additional revisions related to Advanced LGF. The proposed changes reflect evolving regulatory approaches to resolution, changes in banks’ liabilities structures and Moody’s own reassessment of the risk of loss faced by holders of more junior instruments as well as to simplify and harmonize the analytical treatment of AT1 securities and the rating of highly integrated entities. For more details on the proposed changes and to provide feedback:

Bank Methodology Overview

Moody’s Ratings approach starts by analyzing a banks intrinsic or standalone strength, without external support, and assigning a Baseline Credit Assessment. In the Support & Structural Analysis component, consideration is given to the potential for external or Affiliate Support , the expected loss in the event of a default or bank failure, the Loss Given Failure , and the likelihood of Government Support . Click on the components below to learn more.

Bank Rating Methodology Explained

An introduction to the Bank Rating Methodology.

Counterparty Risk Rating (CRR)

Find out how Counterparty Risk Rating completes banks rating architecture.

What Bank Ratings Mean

Baseline credit assessment (bca).

Our Baseline Credit Assessment (BCA) has three main components: a macro profile, financial factors and qualitative factors.

Support & Structural Analysis

Our Support & Structural Analysis assesses the probability of external support from an affiliate or government entity.

Credit Rating Methodology

• 1 Definition
• 2 Methodology Components
• 3 Issues and Challenges

Credit Rating Methodology is an analytic framework (set of considerations, analyses, tools, models and algorithms) that underpin the generation (assignement) of a Credit Rating .

Methodology Components

Credit rating methodologies vary significantly by the market segment (type of issuer) that is being rated. This reflects:

• the available Credit Information
• the nature of borrowers / instruments
• the investor due diligence practices / demands
• legal /regulatory frameworks in different jurisdictions

Indicative components:

• Quantitative Analysis of Corporate Balance Sheet ( Financial Ratios , Liquidity ) usually from audited reports
• Qualitative Analysis of Corporate Balance Sheet
• Analysis of Operating Performance and Assessment of Operating Plan
• Analysis of Business Profile ( Business Model Risk , product mix, quality of management, Internal Governance , SWOT Analysis )
• Analysis of Enterprise Risk Management
• Analysis of Sectoral Profile (business model trends, technological developments) and Comparison with Peers
• Comparison with Industry Benchmarks
• Analysis of Political Risk and FX Lending Risk
• Assessment of Credit Support
• Assessment of legal environment
• Scenario Analysis , Simulation or other tools

The above elements are usually brought together an overall operational [[Credit Rating Process] including any models or tools, intermediate results or assessments, iterative fact finding etc. culminating into a final placement in one or more credit rating scales within a concrete Credit Rating System and an eventual Credit Rating Report .

Issues and Challenges

• Methodologies for different market segments and asset classes can be very inconsistent with each other
• Credit Rating Model , the concrete algorithm / quantitative procedure
• Credit Rating Philosophy , the conceptual and economic / financial context
• Credit Ratings
• Credit Ratings Ontology

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OWASP Risk Rating Methodology

Over the years there has be lots of debate about the OWASP Risk Rating Methodology and the weighting of Threat Actor Skill levels. There are other more mature, popular, or well established Risk Rating Methodologies that can be followed:

• NIST 800-30 - Guide for Conducting Risk Assessments
• Government of Canada - Harmonized TRA Methodology
• Risk Assessment Summary
• Rapid Risk Assessment (RRA)

Alternatively you may with the review information about Threat Modeling, as that may be a better fit for your app or organization:

• https://owasp.org/www-community/Threat_Modeling
• https://owasp.org/www-community/Application_Threat_Modeling
• OWASP pytm Pythonic framework for threat modeling
• OWASP Threat Dragon threat modeling tool

Lastly you might want to refer to the references below.

Note : Edits/Pull Requests to the content below that deal with changes to Threat Actor Skill will not be accepted.

Introduction

Discovering vulnerabilities is important, but being able to estimate the associated risk to the business is just as important. Early in the life cycle, one may identify security concerns in the architecture or design by using threat modeling . Later, one may find security issues using code review or penetration testing . Or problems may not be discovered until the application is in production and is actually compromised.

By following the approach here, it is possible to estimate the severity of all of these risks to the business and make an informed decision about what to do about those risks. Having a system in place for rating risks will save time and eliminate arguing about priorities. This system will help to ensure that the business doesn’t get distracted by minor risks while ignoring more serious risks that are less well understood.

Ideally, there would be a universal risk rating system that would accurately estimate all risks for all organizations. But a vulnerability that is critical to one organization may not be very important to another. So a basic framework is presented here that should be ‘‘customized’’ for the particular organization.

The authors have tried hard to make this model simple to use, while keeping enough detail for accurate risk estimates to be made. Please reference the section below on customization for more information about tailoring the model for use in a specific organization.

There are many different approaches to risk analysis. See the reference section below for some of the most common ones. The OWASP approach presented here is based on these standard methodologies and is customized for application security.

Let’s start with the standard risk model:

• Risk = Likelihood * Impact

In the sections below, the factors that make up “likelihood” and “impact” for application security are broken down. The tester is shown how to combine them to determine the overall severity for the risk.

Step 1: Identifying a Risk

The first step is to identify a security risk that needs to be rated. The tester needs to gather information about the threat agent involved, the attack that will be used, the vulnerability involved, and the impact of a successful exploit on the business. There may be multiple possible groups of attackers, or even multiple possible business impacts. In general, it’s best to err on the side of caution by using the worst-case option, as that will result in the highest overall risk.

Step 2: Factors for Estimating Likelihood

Once the tester has identified a potential risk and wants to figure out how serious it is, the first step is to estimate the “likelihood”. At the highest level, this is a rough measure of how likely this particular vulnerability is to be uncovered and exploited by an attacker. It is not necessary to be over-precise in this estimate. Generally, identifying whether the likelihood is low, medium, or high is sufficient.

There are a number of factors that can help determine the likelihood. The first set of factors are related to the threat agent involved. The goal is to estimate the likelihood of a successful attack from a group of possible attackers. Note that there may be multiple threat agents that can exploit a particular vulnerability, so it’s usually best to use the worst-case scenario. For example, an insider may be a much more likely attacker than an anonymous outsider, but it depends on a number of factors.

Note that each factor has a set of options, and each option has a likelihood rating from 0 to 9 associated with it. These numbers will be used later to estimate the overall likelihood.

Threat Agent Factors

The first set of factors are related to the threat agent involved. The goal here is to estimate the likelihood of a successful attack by this group of threat agents. Use the worst-case threat agent.

Skill Level - How technically skilled is this group of threat agents? No technical skills (1), some technical skills (3), advanced computer user (5), network and programming skills (6), security penetration skills (9)

Motive - How motivated is this group of threat agents to find and exploit this vulnerability? Low or no reward (1), possible reward (4), high reward (9)

Opportunity - What resources and opportunities are required for this group of threat agents to find and exploit this vulnerability? Full access or expensive resources required (0), special access or resources required (4), some access or resources required (7), no access or resources required (9)

Size - How large is this group of threat agents? Developers (2), system administrators (2), intranet users (4), partners (5), authenticated users (6), anonymous Internet users (9)

Vulnerability Factors

The next set of factors are related to the vulnerability involved. The goal here is to estimate the likelihood of the particular vulnerability involved being discovered and exploited. Assume the threat agent selected above.

Ease of Discovery - How easy is it for this group of threat agents to discover this vulnerability? Practically impossible (1), difficult (3), easy (7), automated tools available (9)

Ease of Exploit - How easy is it for this group of threat agents to actually exploit this vulnerability? Theoretical (1), difficult (3), easy (5), automated tools available (9)

Awareness - How well known is this vulnerability to this group of threat agents? Unknown (1), hidden (4), obvious (6), public knowledge (9)

Intrusion Detection - How likely is an exploit to be detected? Active detection in application (1), logged and reviewed (3), logged without review (8), not logged (9)

Step 3: Factors for Estimating Impact

When considering the impact of a successful attack, it’s important to realize that there are two kinds of impacts. The first is the “technical impact” on the application, the data it uses, and the functions it provides. The other is the “business impact” on the business and company operating the application.

Ultimately, the business impact is more important. However, you may not have access to all the information required to figure out the business consequences of a successful exploit. In this case, providing as much detail about the technical risk will enable the appropriate business representative to make a decision about the business risk.

Again, each factor has a set of options, and each option has an impact rating from 0 to 9 associated with it. We’ll use these numbers later to estimate the overall impact.

Technical Impact Factors

Technical impact can be broken down into factors aligned with the traditional security areas of concern: confidentiality, integrity, availability, and accountability. The goal is to estimate the magnitude of the impact on the system if the vulnerability were to be exploited.

Loss of Confidentiality - How much data could be disclosed and how sensitive is it? Minimal non-sensitive data disclosed (2), minimal critical data disclosed (6), extensive non-sensitive data disclosed (6), extensive critical data disclosed (7), all data disclosed (9)

Loss of Integrity - How much data could be corrupted and how damaged is it? Minimal slightly corrupt data (1), minimal seriously corrupt data (3), extensive slightly corrupt data (5), extensive seriously corrupt data (7), all data totally corrupt (9)

Loss of Availability - How much service could be lost and how vital is it? Minimal secondary services interrupted (1), minimal primary services interrupted (5), extensive secondary services interrupted (5), extensive primary services interrupted (7), all services completely lost (9)

Loss of Accountability - Are the threat agents’ actions traceable to an individual? Fully traceable (1), possibly traceable (7), completely anonymous (9)

Business Impact Factors

The business impact stems from the technical impact, but requires a deep understanding of what is important to the company running the application. In general, you should be aiming to support your risks with business impact, particularly if your audience is executive level. The business risk is what justifies investment in fixing security problems.

Many companies have an asset classification guide and/or a business impact reference to help formalize what is important to their business. These standards can help you focus on what’s truly important for security. If these aren’t available, then it is necessary to talk with people who understand the business to get their take on what’s important.

The factors below are common areas for many businesses, but this area is even more unique to a company than the factors related to threat agent, vulnerability, and technical impact.

Financial damage - How much financial damage will result from an exploit? Less than the cost to fix the vulnerability (1), minor effect on annual profit (3), significant effect on annual profit (7), bankruptcy (9)

Reputation damage - Would an exploit result in reputation damage that would harm the business? Minimal damage (1), Loss of major accounts (4), loss of goodwill (5), brand damage (9)

Non-compliance - How much exposure does non-compliance introduce? Minor violation (2), clear violation (5), high profile violation (7)

Privacy violation - How much personally identifiable information could be disclosed? One individual (3), hundreds of people (5), thousands of people (7), millions of people (9)

Step 4: Determining the Severity of the Risk

In this step, the likelihood estimate and the impact estimate are put together to calculate an overall severity for this risk. This is done by figuring out whether the likelihood is low, medium, or high and then do the same for impact. The 0 to 9 scale is split into three parts:

Informal Method

In many environments, there is nothing wrong with reviewing the factors and simply capturing the answers. The tester should think through the factors and identify the key “driving” factors that are controlling the result. The tester may discover that their initial impression was wrong by considering aspects of the risk that weren’t obvious.

Repeatable Method

If it is necessary to defend the ratings or make them repeatable, then it is necessary to go through a more formal process of rating the factors and calculating the result. Remember that there is quite a lot of uncertainty in these estimates and that these factors are intended to help the tester arrive at a sensible result. This process can be supported by automated tools to make the calculation easier.

The first step is to select one of the options associated with each factor and enter the associated number in the table. Then simply take the average of the scores to calculate the overall likelihood. For example:

Next, the tester needs to figure out the overall impact. The process is similar here. In many cases the answer will be obvious, but the tester can make an estimate based on the factors, or they can average the scores for each of the factors. Again, less than 3 is low, 3 to less than 6 is medium, and 6 to 9 is high. For example:

Determining Severity

However the tester arrives at the likelihood and impact estimates, they can now combine them to get a final severity rating for this risk. Note that if they have good business impact information, they should use that instead of the technical impact information. But if they have no information about the business, then technical impact is the next best thing.

In the example above, the likelihood is medium and the technical impact is high, so from a purely technical perspective it appears that the overall severity is high. However, note that the business impact is actually low, so the overall severity is best described as low as well. This is why understanding the business context of the vulnerabilities you are evaluating is so critical to making good risk decisions. Failure to understand this context can lead to the lack of trust between the business and security teams that is present in many organizations.

Step 5: Deciding What to Fix

After the risks to the application have been classified, there will be a prioritized list of what to fix. As a general rule, the most severe risks should be fixed first. It simply doesn’t help the overall risk profile to fix less important risks, even if they’re easy or cheap to fix.

Remember that not all risks are worth fixing, and some loss is not only expected, but justifiable based upon the cost of fixing the issue. For example, if it would cost $100,000 to implement controls to stem $2,000 of fraud per year, it would take 50 years return on investment to stamp out the loss. But remember there may be reputation damage from the fraud that could cost the organization much more.

Step 6: Customizing the Risk Rating Model

Having a risk ranking framework that is customizable for a business is critical for adoption. A tailored model is much more likely to produce results that match people’s perceptions about what is a serious risk. A lot of time can be wasted arguing about the risk ratings if they are not supported by a model like this. There are several ways to tailor this model for the organization.

Adding factors

The tester can choose different factors that better represent what’s important for the specific organization. For example, a military application might add impact factors related to loss of human life or classified information. The tester might also add likelihood factors, such as the window of opportunity for an attacker or encryption algorithm strength.

Customizing options

There are some sample options associated with each factor, but the model will be much more effective if the tester customizes these options to the business. For example, use the names of the different teams and the company names for different classifications of information. The tester can also change the scores associated with the options. The best way to identify the right scores is to compare the ratings produced by the model with ratings produced by a team of experts. You can tune the model by carefully adjusting the scores to match.

Weighting factors

The model above assumes that all the factors are equally important. You can weight the factors to emphasize the factors that are more significant for the specific business. This makes the model a bit more complex, as the tester needs to use a weighted average. But otherwise everything works the same. Again it is possible to tune the model by matching it against risk ratings the business agrees are accurate.

• Managing Information Security Risk: Organization, Mission, and Information System View
• Industry standard vulnerability severity and risk rankings (CVSS)
• Threat Modeling Web Applications
• Threat Modeling
• A Platform for Risk Analysis of Security Critical Systems
• Model-driven Development and Analysis of Secure Information Systems
• Value Driven Security Threat Modeling Based on Attack Path Analysis
• Risk Rating Template Example in MS Excel

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• MSCI ESG Ratings
• Why MSCI ESG Ratings Matter
• How They Work
• Sustainable Investing
• Socially Responsible Investing

MSCI ESG Ratings Definition, Methodology, Example

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

What Are MSCI ESG Ratings?

MSCI ESG ratings are a comprehensive measure of a company’s long-term commitment to socially responsible investments (SRI) and environmental, social, and governance (ESG) investment standards. In particular, the MSCI ESG ratings focus on a company’s exposure to financially relevant ESG risks.

ESG and SRI investing prioritize a company’s positive contributions to its community, the environment, and social impact. Scoring companies along ESG dimensions allows socially conscious investors to screen potential investments to fit with their investment goals and values.

Key Takeaways

• MSCI ESG ratings measure a company’s resilience to long-term, financially relevant ESG (environment, social, governance) risks.
• ESG investing has grown to become an important and influential investment strategy, largely motivated by values of social responsibility and corporate accountability.
• MSCI’s ESG ratings score along all three dimensions of ESG and rank potential investments on a letter-scale from AAA (leaders) to CCC (laggards).

Understanding MSCI ESG Ratings

ESG investing has become increasingly popular over the past decade. The US SIF: The Forum for Sustainable and Responsible Investment reports that in 2020, more than $17 trillion of professionally managed assets were held in sustainable assets, around one-third of all assets under management. With its growing popularity, data providers have also created various scoring criteria upon which to rank and grade potential ESG investments, allowing socially responsible investors to make more informed decisions when choosing which companies, ETFs, or mutual funds to include in their portfolios.

Alongside MSCI, several other financial firms have developed their own proprietary ESG scoring models, including Russell Investments and Standard & Poors (S&P), among others.

MSCI’s ratings decompose ESG into its three thematic components: the environment, social responsibility, and corporate governance.

Under the environmental dimension, key issues include:

• contribution to climate change
• a company’s utilization of "natural capital" (such as biodiversity and raw materials sourcing)
• pollution and waste management
• use of green technologies and renewable energy

Under social:

• health, safety, and human capital development
• product and consumer safety
• community relations
• social opportunities

And, under governance:

• corporate governance fairness and accountability
• transparency and ethics

How Do MSCI ESG Ratings Work?

Analyzing metrics within each of these key issue items, MSCI scores the companies that it rates on each key issue from zero to ten, with zero indicating virtually no exposure and ten representing very high exposure to a particular ESG risk or opportunity. MSCI also evaluates companies on exposure to controversial business activities (e.g., weapons, tobacco, gambling, etc.). The data informing these scores are obtained from corporate filings, financial statements, and press releases in addition to almost half of all data coming from hundreds of third-party media, academic, NGO, regulatory, and government sources.

Scores based on individual metrics are aggregated, weighted, and scaled to the relevant industry sector to arrive at an intuitive letter-based grade, akin to lettered credit scores issued by credit rating companies.

Source: MSCI

According to MSCI, a "leader" (rated AAA & AA) indicates a company leading its industry in managing the most significant ESG risks and opportunities. "Average" (rated A, BBB, or BB) companies are described by a mixed or unexceptional track record of managing ESG risks and opportunities relative to industry peers; while a "laggard" (rated B or CCC) trails its industry based on its high exposure and failure to manage significant ESG risks.

Real-World Example of MSCI ESG Ratings: Tesla, Inc.

To illustrate how MSCI ESG ratings can be used by investors, let’s take a look at the electric vehicle producer, Tesla, Inc. ( TSLA ). The company earns an overall grade of "A," putting it on the higher end of "average" among the 41 companies in the car industry rated by MSCI. Digging into its rating, Tesla excels in corporate governance and environmental risks, maintaining a relatively small carbon footprint while both utilizing and investing in green technologies. The company scores an average grade for product quality and safety, with the company making headlines in the past for exploding batteries, undesirable crash test ratings, and accidents involving the cars’ self-driving "autopilot" feature – although CEO Elon Musk has publicly announced a commitment to improving both driver and bystander safety.

What truly drags down Tesla’s MSCI ESG rating is its below-average score for product quality and safety. The battery banks in its cars have been known to spontaneously combust and the National Transportation Safety Board (NTSB) has accused Tesla for neglecting driver safety, calling certain Autopilot features "completely inadequate" and citing Autopilot as the probable cause of several deadly crashes involving Tesla cars.

Tesla has also been criticized for its labor management practices. For instance, the company has been found to be in violation of labor laws by blocking unionization, and that it has violated the National Labor Relations Act multiple times. More recently, the company’s leadership has come under fire for keeping plants open and unsafe during the COVID-19 pandemic, leading several of its workers to come down with the illness.

Despite earning only an "average" score, it is worth noting that only one company covered in the auto industry (including both automobiles and auto parts) currently earns "leader" status on MSCI’s ESG ratings – the French auto parts maker, Valeo SE.

What Is ESG in Investing?

Environmental, social, and governance (ESG) criteria are used to screen investments based on corporate policies and to encourage companies to act responsibly. ESG also helps investors who care about these issues to screen for those companies that rank highly in social and environmental responsibility.

What Is MSCI's Implied Temperature Rise?

MSCI has recently developed an ESG screening criterion known as Implied Temperature Rise (ITR), which is an intuitive, forward-looking metric, expressed in degrees Celsius, designed to show the temperature alignment of companies, portfolios, and funds with global temperature goals. Implied Temperature Rise can help investors assess the environmental alignment of companies, portfolios, funds, and benchmarks with net-zero carbon emissions targets by the middle of this century.

How Many Companies Does MSCI's ESG Ratings Cover?

As of 2022, MSCI has ESG ratings for more than 8,500 companies worldwide.

US SIF: The Forum for Sustainable and Responsible Investment. " The US SIF Foundation’s Biennial “Trends Report” Finds That Sustainable Investing Assets Reach $17.1 Trillion ."

S&P Global Ratings. “ ESG Evaluation ,” Page 2.

Russell Investments. " Materiality Matters ,” Page 1.

MSCI. " MSCI ESG Ratings Methodology, Executive Summary ," Page 4.

MSCI. " MSCI Sustainable Select Index Methodology ," Page 7.

MSCI. " MSCI Sustainable Select Index Methodology ," Pages 11-12.

MSCI. " MSCI ESG Ratings Methodology, Executive Summary ," Page 11.

MSCI. " ESG Ratings ."

MSCI. " ESG Ratings & Climate Search Tool: Tesla ."

Washington Post. " Tesla Model S erupts in flames, prompting NHTSA to step in ."

Reuters. " US NTSB head criticizes Tesla over vehicle self-driving feature ."

U.S. Securities and Exchange Commission. “ Notice of Exempt Solicitation Pursuant to Rule 14a-103 .”

Tesla. “ Impact Report 2020 ,” Page 56.

Tesla. “ Tesla Operational Update .”

MSCI. " ESG Ratings & Climate Search Tool: Valeo SE ."

MSCI. " What is Implied Temperature Rise (ITR)? "

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• What Is a Likert Scale? | Guide & Examples

What Is a Likert Scale? | Guide & Examples

Published on July 3, 2020 by Pritha Bhandari and Kassiani Nikolopoulou. Revised on June 22, 2023.

A Likert scale is a rating scale used to measure opinions, attitudes, or behaviors.

It consists of a statement or a question, followed by a series of five or seven answer statements. Respondents choose the option that best corresponds with how they feel about the statement or question.

Because respondents are presented with a range of possible answers, Likert scales are great for capturing the level of agreement or their feelings regarding the topic in a more nuanced way. However, Likert scales are prone to response bias , where respondents either agree or disagree with all the statements due to fatigue or social desirability or have a tendency toward extreme responding or other demand characteristics .

Likert scales are common in survey research , as well as in fields like marketing, psychology, or other social sciences.

Download Likert scale response options

Table of contents

What are likert scale questions, when to use likert scale questions, how to write strong likert scale questions, how to write likert scale responses, how to analyze data from a likert scale, advantages and disadvantages of likert scales, other interesting articles, frequently asked questions about likert scales.

Likert scales commonly comprise either five or seven options. The options on each end are called response anchors . The midpoint is often a neutral item, with positive options on one side and negative options on the other. Each item is given a score from 1 to 5 or 1 to 7.

The format of a typical five-level Likert question, for example, could be:

• Strongly disagree
• Neither agree nor disagree
• Strongly agree

In addition to measuring the level of agreement or disagreement, Likert scales can also measure other spectrums, such as frequency, satisfaction, or importance.

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Researchers use Likert scale questions when they are seeking a greater degree of nuance than possible from a simple “yes or no” question.

For example, let’s say you are conducting a survey about customer views on a pair of running shoes. You ask survey respondents “Are you satisfied with the shoes you purchased?”

A dichotomous question like the above gives you very limited information. There is no way you can tell how satisfied or dissatisfied customers really are. You get more specific and interesting information by asking a Likert scale question instead:

“How satisfied are you with the shoes you purchased?”

• 1 – Very dissatisfied
• 2 – Dissatisfied
• 4 – Satisfied
• 5 – Very satisfied

Likert scales are most useful when you are measuring unobservable individual characteristics , or characteristics that have no concrete, objective measurement. These can be elements like attitudes, feelings, or opinions that cause variations in behavior.

Each Likert scale–style question should assess a single attitude or trait. In order to get accurate results, it is important to word your questions precisely. As a rule of thumb, make sure each question only measures one aspect of your topic.

For example, if you want to assess attitudes towards environmentally friendly behaviors, you can design a Likert scale with a variety of questions that measure different aspects of this topic.

Here are a few pointers:

Include both questions and statements

Use both positive and negative framing, avoid double negatives, ask about only one thing at a time, be crystal clear.

A good rule of thumb is to use a mix of both to keep your participants engaged during the survey. When deciding how to phrase questions and statements, it’s important that they are easily understood and do not bias your respondents in one way or another.

If all of your questions only ask about things in socially desirable ways, your participants may be biased towards agreeing with all of them to show themselves in a positive light.

• Positive framing
• Negative framing

Respondents who agree with the first statement should also disagree with the second. By including both of these statements in a long survey, you can also check whether the participants’ responses are reliable and consistent.

Double negatives can lead to confusion and misinterpretations, as respondents may be unsure of what they are agreeing or disagreeing with.

• Bad example
• Good example

Avoid double-barreled questions (asking about two different topics within the same question). When faced with such questions, your respondents may selectively answer about one topic and ignore the other. Questions like this may also confuse respondents, leading them to choose a neutral but inaccurate answer in an attempt to answer both questions simultaneously.

The accuracy of your data also relies heavily on word choice:

• Pose your questions clearly, leaving no room for misunderstanding.
• Make language and stylistic choices that resonate with your target demographic.
• Stay away from jargon that could discourage or confuse your respondents.

When using Likert scales, how you phrase your response options is just as crucial as how you phrase your questions.

Here are a few tips to keep in mind.

Decide on a number of response options

Choose the type of response option, choose between unipolar and bipolar options, make sure that you use mutually exclusive options.

More options give you deeper insights but can make it harder for participants to decide on one answer. Fewer options mean you capture less detail, but the scale is more user-friendly.

Usually, researchers include five or seven response options. It’s a good idea to include an odd number so that there is a midpoint. However, if you want to force your respondents to choose, an even number of responses removes the neutral option.

You can measure a wide range of perceptions, motivations, and intentions using Likert scales. Response options should strive to cover the full range of opinions you anticipate a participant can have.

Some of the most common types of items include:

• Agreement: Strongly Agree, Agree, Neither Agree nor Disagree, Disagree, Strongly Disagree
• Quality: Very Poor, Poor, Fair, Good, Excellent
• Likelihood: Extremely Unlikely, Somewhat Unlikely, Likely, Somewhat Likely, Extremely Likely
• Experience: Very Negative, Somewhat Negative, Neutral, Somewhat Positive, Very Positive

Some researchers also include a “don’t know” option. This allows them to distinguish between respondents who do not feel sufficiently informed to give an opinion and those who are “neutral” on the topic. However, including a “don’t know” option may trigger unmotivated respondents to select that for every question.

On a unipolar scale, you measure only one attribute (e.g., satisfaction). On a bipolar scale, you can measure two attributes (e.g., satisfaction or dissatisfaction) along a continuum.

Your choice depends on your research questions and aims. If you want finer-grained details about one attribute, select unipolar items. If you want to allow a broader range of responses, select bipolar items.

Unipolar scales are most accurate when five-point scales are used. Conversely, bipolar scales are most accurate when a seven-point scale is used (with three scale points on each side of a truly neutral midpoint.)

Avoid overlaps in the response items. If two items have similar meanings, it risks making your respondent’s choice random.

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Before analyzing your data, it’s important to consider what type of data you are dealing with. Likert-derived data can be treated either as ordinal-level or interval-level data . However, most researchers treat Likert-derived data as ordinal: assuming there is not an equal distance between responses.

Furthermore, you need to decide which descriptive statistics and/or inferential statistics may be used to describe and analyze the data obtained from your Likert scale.

You can use descriptive statistics to summarize the data you collected in simple numerical or visual form.

• Ordinal data: To get an overall impression of your sample, you find the mode, or most common score, for each question. You also create a bar chart for each question to visualize the frequency of each item choice.
• Interval data: You add up the scores from each question to get the total score for each participant. You find the mean , or average, score and the standard deviation , or spread, of the scores for your sample.

You can use inferential statistics to test hypotheses , such as correlations between different responses or patterns in the whole dataset.

• Ordinal data: You hypothesize that knowledge of climate change is related to belief that environmental damage is a serious problem. You use a chi-square test of independence to see if these two attributes are correlated.
• Interval data: You investigate whether age is related to attitudes towards environmentally friendly behavior. Using a Pearson correlation test, you assess whether the overall score for your Likert scale is related to age.

Lastly, be sure to clearly state in your analysis whether you treat the data at interval level or at ordinal level.

Analyzing data at the ordinal level

Researchers usually treat Likert-derived data as ordinal . Here, response categories are presented in a ranking order, but the distances between the categories cannot be presumed to be equal.

For example, consider a scale where 1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, and 5 = strongly disagree.

In this scale, 4 is more negative than 3, 2, or 1. However, it cannot be inferred that a response of 4 is twice as negative as a response of 2.

Treating Likert-derived data as ordinal, you can use descriptive statistics to summarize the data you collected in simple numerical or visual form. The median or mode generally is used as the measure of central tendency . In addition, you can create a bar chart for each question to visualize the frequency of each item choice.

Appropriate inferential statistics for ordinal data are, for example, Spearman’s correlation or a chi-square test for independence .

Analyzing data at the interval level

However, you can also choose to treat Likert-derived data at the interval level . Here, response categories are presented in a ranking order, and the distance between categories is presumed to be equal.

Appropriate inferential statistics used here are an analysis of variance (ANOVA) or Pearson’s correlation . Such analysis is legitimate, provided that you state the assumption that the data are at interval level.

In terms of descriptive statistics, you add up the scores from each question to get the total score for each participant. You find the mean , or average, score and the standard deviation , or spread, of the scores for your sample.

Likert scales are a practical and accessible method of collecting data.

• Quantitative: Likert scales easily operationalize complex topics by breaking down abstract phenomena into recordable observations. This enables statistical testing of your hypotheses.
• Fine-grained: Because Likert-type questions aren’t binary ( yes/no , true/false , etc.) you can get detailed insights into perceptions, opinions, and behaviors.
• User-friendly: Unlike open-ended questions, Likert scales are closed-ended and don’t ask respondents to generate ideas or justify their opinions. This makes them quick for respondents to fill out and ensures they can easily yield data from large samples.

Problems with Likert scales often come from inappropriate design choices.

• Response bias: Due to social desirability bias , people often avoid selecting the extreme items or disagreeing with statements to seem more “normal” or show themselves in a favorable light.
• Fatigue/inattention: In Likert scales with many questions, respondents can get bored and lose interest. They may absent-mindedly select responses regardless of their true feelings. This results in invalid responses.
• Subjective interpretation: Some items can be vague and interpreted very differently by respondents. Words like “somewhat” or “fair” don’t have precise or narrow definitions.
• Restricted choice: Since Likert-type questions are closed-ended, respondents sometimes have to choose the most relevant answer even if it may not accurately reflect reality.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

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