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Bahar Gidwani


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Insights Into FB Heron’s ESG Investment Process: A Case Study

[fa icon="calendar'] Sep 5, 2018 10:05:41 AM / by Bahar Gidwani

Our friends at FB Heron recently published a summary of how they arrived at their current ESG (Environment, Social and Governance) investment process.  It should be interesting for anyone who is trying to bring ESG factors into their own framework.Heron

Six years ago when Heron declared its intention to invest 100% of its assets for mission, they needed to find new ways to track and visualize the portfolio as it changed over time.

As is true for many foundations, FB Heron invests both directly and via outside managers.  The article starts with a four box screening system that sought to remove “bad” companies and portfolios and focus investment on “good” ones.  We then see that there is a broad distribution of good and bad performance—even after this type of screening. 

The largest segment of their endowment is invested in publicly traded companies, so it was extremely important to find a data partner that had social performance data on that universe. Heron works with a few data providers to do so, including CSRHuboekom, and others.

Heron uses CSRHub’s percentile rankings to help keep comparisons consistent and account for inherent differences across industries. The 0%-100% score can be applied to all of their corporate holdings. 

Heron uses the CSRHub scores of the commonly associated benchmarks (like the S&P 500). The weighted average helps them get a sense of how much of the fund was allocated to higher scoring companies, relative to the benchmark.

Heron has attempted to convert their scoring into a -5 to +5 scale. For now, the percentile scores are scaled so the average enterprise (50th percentile) receives a score of 0. They believe this scale is overly simplistic — however view it as a step in the right direction. Heron has achieved “relative” goodness—a distribution that is markedly superior to that of the market.  But, it continues to struggle with “outliers.”

Please see the FB Heron piece for their thought leadership in forming their evolving portfolio.


Bahar_Gidwani-9Bahar Gidwani has built and run large technology-based businesses for many years. Bahar holds a CFA (Chartered Financial Analyst) and was one of the first people to receive the FSA (Fundamentals of Sustainability Accounting) designation from SASB. Bahar worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. He has founded several technology-based companies and is a co-founder of CSRHub, the world’s broadest source of corporate social responsibility information. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 18,400+ companies from 135 industries in 133 countries. By aggregating and normalizing the information from 550 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers, API partners and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

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A Fresh Resource for ESG-Oriented Financial Advisors

[fa icon="calendar'] Aug 30, 2018 10:10:38 AM / by Bahar Gidwani

One of the gurus of ESG investing has recently launched a new service for financial advisors (FAs) who want to integrate Environment, Social, and Governance (ESG) factors into their investment process.  He has created a site called “Sustainable Investing” and filled it with content about ESG investing.  Those who subscribe (there is a three month free trial option) get a quarterly newsletter and access to some otherwise hidden research reports.

The site’s author is Henry Shilling, who until recently led Moody’s sustainability 

Sustainable Investing

research efforts. I believe there are several reasons it has been difficult for FAs to bring ESG into their work. (Henry was also one of CSRHub’s beta testers and an early subscriber.)  During his time at Moody’s, Mr. Shilling performed several seminal studies that connected ESG factors with corporate long-term financial performance and risk.  I recall taking so many notes during one of his talks at an S-Networks “Summer in the City CSR Investing Summit” that the fellow next to me told me to stop.  My laptop keyboard clicks were making too much noise and he also wanted to hear Henry speak.

  • The available ESG data sets are too expensive for many FAs to afford.
  • ESG data sets are complex and hard for advisors to navigate and understand. They focus on detail over substance and have “holes” in their data that make comparisons difficult.
  • FA clients have personal biases and views that demand client-specific adjustments. The correct portfolio of one client may not fit the needs of an FA’s other clients.
  • Clients have expected (and FAs have promised) that ESG-oriented portfolios will outperform those that do not take corporate social responsibility considerations into account. ESG funds have more or less performed in line with the market—but most of those currently offered have not been around that long.  We have not seen yet an ESG fund show consistent multi-year outperformance.

Henry has stated publicly that he believes investors and their advisors who care about ESG issues should seek to earn only market rate returns.  I agree with him.  He and I both believe that it is possible to construct a market-performing portfolio of investments that reflects a client’s personal values, if one uses a broad enough initial investment universe.  Henry’s new site is an attempt to provide practical advice and tools for implementing these ideas.


Bahar_Gidwani-9Bahar Gidwani has built and run large technology-based businesses for many years. Bahar holds a CFA (Chartered Financial Analyst) and was one of the first people to receive the FSA (Fundamentals of Sustainability Accounting) designation from SASB. Bahar worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. He has founded several technology-based companies and is a co-founder of CSRHub, the world’s broadest source of corporate social responsibility information. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 18,052+ companies from 135 industries in 133 countries. By aggregating and normalizing the information from 556 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers, API partners and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

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Why Use Big Data to Measure CSR?

[fa icon="calendar'] Jun 28, 2017 7:00:00 AM / by Bahar Gidwani

The following is part 3 of a 3-part series on “Big Data.”

In the past several posts, we have defined Big Data, shown the problems we hope it will address, and described how CSRHub has implemented a Big Data approach to creating corporate social responsibility (CSR) and sustainability ratings.  It is time now to discuss the benefits and drawbacks of the “Big Data” approach.


The assumption is that this approach offers many benefits which are not available under traditional analyst-based ratings methods:

 
  • A broad measure of perceived performance. Input from most of the “stakeholders” who evaluate a company’s sustainability performance is captured.  Investor input from the ESG/SRI sources, community input from NGOs and government groups, and input from suppliers, employees, and customers via supply chain tools, employee surveys, and product ratings are included.  While no one can claim to measure true company performance—no external system can do this, it is possible to give an accurate overall multi-stakeholder-based estimate of how a company is perceived.
  • Increased transparency and accountability. The system described automatically reveals to users which sources have reported on each company rated.  Via subscriber-accessible tables and custom reports, users can inspect the details of the data gathered.  This allows companies and their stakeholders to identify the data elements that affect how they are perceived (transparency) and to respond to or correct data that may not be accurate (accountability).
  • Reduced impact from errors and bias. If a source contains a lot of factual errors or an undisclosed bias, this system automatically reduces the weight given to the source.  In this way, the effect on our results of poor quality sources is minimized and corrected for systemic biases.  Because sources generate their information independently, there is good statistical accuracy for our aggregated scores.
  • Regular update and trend tracking. Some sources update their information daily, some quarterly, some only once per year.  However, because there are so many sources, our ratings are updated each month.  This allows the system to show trend charts that connect actions and outcomes with perception.
  • Broad coverage of industries, geographies, and company types. An aggregation system is dependent on its sources for coverage.  We do not yet have full data on small companies or on those in remote geographies or unusual industries.  But, the system allows us to use whatever is available.  We may not be able to rate all aspects of each new company we add to our system, but any ratings we can generate should be consistent across our system.

 

We Don’t Yet Measure Thousands of Smaller Corporate, Not-for-profit, and Government Entities

Percent who could be measured.jpg

  • This approach supports a fourth Big Data “V”—“Veracity.”  There is free access to basic ratings information to everyone.  As a result, any stakeholder can check scores and audit results.
  • Users can adjust ratings to fit their own personal views. There is sufficient data from a wide enough range of sources that we can present alternate sides of many contentious issues.  Users can record a profile that adjusts our ratings to match their own view.  Users can emphasize the priority of environment, employee, community or governance issues, be in favor of nuclear power or against it, or focus on the risks from mercury in fish.  They can then share their personal overall ratings of company sustainability with the other users.

 

The CSRHub approach to ratings has a few drawbacks that are common to Big Data systems:

  • Perception is not reality. The data that companies self-report is focused mostly on its policies and intent.  A company that is good at communicating and “spinning” its story could raise its ratings on the system to a level they do not deserve.  Of course, as more data is secured—especially from bottoms up “crowd” sources—this type of behavior will likely eventually be detected.  A Governance Metric tool called Audit Integrity used to do this type of sleuthing on corporate financial reports.
  • Best practices are not immediately obvious. It is fairly easy to discover that certain activities seem connected with better ratings.  For instance, companies who use the Global Reporting Initiative (GRI) guidelines or who participate in the UN Global Compact have statistically better ratings than those who do not.  However, it is hard to tell if a program at one company has more effect on its perceived CSR performance than a different program at another company.  The system described can only provide a base of data—the study and explanation of ratings differences must be done by CSR professionals.
  • We cannot correct individual company errors that are found. There are conflicts in the views of disparate sources, on a regular basis.  These discrepancies can’t be “resolved” even when we suspect that some are caused by a source’s data collection or analysis error.  The best that can be done is to report a suspected error to the source and allow it to research and correct the error, in its own way.

 

We believe the benefits of using a Big Data approach to measure corporate social responsibility and sustainability performance far outweigh the drawbacks.  A Big Data system can be extended to include thousands of smaller companies and organizations.  We are expanding our universe of coverage while we keep narrowing down the “error bars” in our ratings. We have discovered several areas that appear driven by sustainability factors.  For instance, we have connected our data to measures of brand strength, risk, communications quality, and cost of credit.

percent correlation with CSRHub ratings graph.jpg


Bahar_Gidwani.jpgBahar Gidwani is CEO and Co-founder of CSRHub.  He has built and run large technology-based businesses for many years. Bahar holds a CFA, worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. Bahar has consulted to a number of major companies and currently serves on the board of several software and Web companies. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 17,400 companies from 135 industries in 134 countries. By aggregating and normalizing the information from 530 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

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A Big Data Approach to Gathering CSR Data

[fa icon="calendar'] Jun 22, 2017 9:50:14 AM / by Bahar Gidwani

The following is part 2 of a 3-part series on “Big Data.”

We have previously defined “Big Data” and shown how we feel a Big Data system built by CSRHub could help address some problems that exist in collecting corporate social responsibility (CSR) and sustainability data on companies.  We have also further described the problems with the currently dominant method of gathering this data—an analyst-based method.

CSRHub uses input from investor-driven sources (known as “ESG” for Environment, Social, and Governance or “SRI” for Socially Responsible Investment), non-governmental organizations, government organizations, and “crowd sources” to construct a 360 degree view of a company’s sustainability performance.

The illustration below shows the steps in our process.

How CSRHub Generates a Score--2017-06-22.jpg

The steps are:

  1. Convert measurement from each data source into a 0 (low) to 100 (high) scales. This requires understanding how each source evaluates company performance.
  2. We next connect each rating element with one or more of our twelve subcategory ratings. (Some elements may also map partially or exclusively to special issues such as animal testing, fracking, or nuclear power.)
  3. We compare each source’s ratings with those for all other sources. Each company we study gives us more opportunities to compare one source’s ratings with another.  The total number of comparisons possible is very large and growing, exponentially.  We use the results of our comparison to adjust the distribution of scores for each rating source so that they fall into a “beta” distribution that has a central peak in the 50s.
  4. Some sources match up well with all of our other data. Some sources don’t line up.  We add weight to those who match well but continue to “count” those who don’t.

We then repeat steps A to D as many times until we have found a “best fit” for the available data.  Each time we add a new source, we go through an initial mapping, normalization, and weighting process.

 

An Example

It may help explain our data analysis process by using a specific example.  (Note that we are using an example from 2014 for a company that has since been split into two parts.  This avoids any risk of an implied criticism of an existing company.)  Hewlett Packard was a heavily tracked company prior to its split.  We had 154 sources of data for this company that together provided 17,571 individual data elements.  Only 62 of these data sources provided data for our July 1, 2014 rating—the rest of the data sources provided data for previous periods (our data set goes back to 2008).  The 62 current data sources provided 575 different types of rating elements and a total of 610 different ratings values that did not affect/apply to special issues.

After their conversion to our 0 to 100 scale, we mapped the rating elements into our twelve subcategories.  We had 1,403 ratings factors.  We selected our subcategories to allow an even spread of data across them. You can see that we had a reasonably even spread for Hewlett Packard:

CSRHub category.jpg

Before we could present a rating, we needed to check first that we had enough sources and enough “weight” from the sources we had, to generate a good score.  In general, we require at least two sources that have good strength or three or four weaker ones, before we offer a rating.  As you can see, we had plenty of sources to rate a big company such as HP.

Subcategory Chart.jpg

Even after normalization, the curve of ratings for any one subcategory may have a lot of irregularities.  However, we had enough data to provide a good estimate of the midpoint of the available data, for those ratings we collected and adjusted.  Below you can see that some sources had a high opinion of HP’s board while others had a less favorable view.  The result was a blended score that averaged to less than the more uniform Leadership Ethics rating.

 Subcategory Ratings Graph.jpg

The overall effect of our process is to smooth out the ratings input and make them more consistent.  As you can in the illustration below, the final ratings distribution was organized well around a central peak.  The average overall rating of 64 was below the peak, which was around 80.  The original average rating was 61.

CSRHub analysis graph.jpg

By making a few assumptions about how the errors in data are distributed, one can assess the accuracy of ratings.  In a previous post, we showed that CSRHub’s overall rating accurately represents the values that underlie it to within 1.8 points at a 95% confidence interval.  Our error bars have continued to decline and now are less than 1 point at this confidence interval.

In our next post, we will discuss the benefits and drawback of using this complex and data intensive approach to measuring company CSR performance.

Did you miss part 1? See part 1 here.

 


Bahar_Gidwani.jpgBahar Gidwani is CTO and Co-founder of CSRHub.  He has built and run large technology-based businesses for many years. Bahar holds a CFA, worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. Bahar has consulted to a number of major companies and currently serves on the board of several software and Web companies. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 17,400 companies from 135 industries in 134 countries. By aggregating and normalizing the information from 530 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

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Using “Big Data” to Rate Corporate Social Responsibility: One Company’s Approach

[fa icon="calendar'] Jun 14, 2017 9:00:02 AM / by Bahar Gidwani

The following is part 1 of a 3-part series on “Big Data.”

“Big Data” is a useful tool for rating corporate social responsibility (CSR) and sustainability performance.  We believe that the Big Data system that CSRHub has developed is one answer to dealing with the rise in new ratings systems (it seems there is a new one announced each month) and with the disparities in scores that occur among these different systems.

In 2001, Doug Laney (currently an analyst for Gartner), foresaw that users of data were facing problems handling the Volume of data they were gathering, the Variety of data in their systems, and the Velocity with which data elements changed.  These “three Vs” are now part of most definitions of the “Big Data” area.

Ratings in the CSR space appear to be a candidate for a big data solution to its three “V” problems.

  • Volume: There are many sources of ratings.  CSRHub currently tracks more than 530 sources of CSR information and plans to add at least another 30 sources over the next six months.  Our system already contains more than 120,000,000 pieces of data from these sources that touch more than 150,000 companies.  We hope eventually to expand our coverage to include several million companies.
  • Variety: Each of these 530+ sources uses different criteria to measure corporate sustainability and social performance.  A number of comprehensive sustainability measurement approaches have been created.  Unfortunately, each new entrant into the area seems compelled to create yet another system.
  • Velocity: With hundreds of thousands of companies to measure and so many measurement systems, the perceived sustainability performance of companies constantly changes.  Many of the available ratings systems track these changes only on a quarterly or annual basis.

Most systems for measuring the CSR and sustainability performance of corporations rely on human-based analysis.  A researcher selects a set of companies to study, determines the criteria he or she wishes to use to evaluate their performance, and then collects the data needed to support the study.  When the researcher can’t find a required data item in a company’s sustainability report or press releases, he or she may try to contact the company to get the data. 

Some research firms try to streamline this process by sending out a questionnaire that covers all the things they want to know.  Then, they follow up to encourage companies to answer their questions and follow up again after they receive the answers, to check the facts and be sure their questions were answered consistently.  An NAEM survey showed that its members were seeing an average of more than ten of these results in 2011. This number has continued to grow and some large companies say they receive as many as 300 survey requests per year.

 Graph on External Data.jpg

NAEM Green Metrics That Matter Report—2012 for 35 members.

Both the direct and survey-driven approaches to data gathering are reasonable and can lead to sound ratings and valuable insights.  However, both are limited in several important ways:

  • The studied companies are the primary source of the data used to evaluate them.  While analysts can question and probe, they have no way to determine how accurately a company has responded.
  • Different areas of a company may respond differently to analyst questions.  It’s hard to determine objectively from the outside, which area of a company has the right perspective and which answer is correct.
  • When companies get too many surveys and requests for data, they stop responding to them.  This “survey fatigue” leads to gaps in the data collected.  Note that only a few thousand large companies have full-time staff available to answer researcher questions.
  • Often analysts cannot financially justify studying smaller companies.  There is little interest in smaller companies from the investor clients who pay for most CSR data collection.  As a result, most analyst-driven research covers a subset of the world’s 5,000 largest companies.  There are only a few data sets bigger than this, and they cover only limited subject areas.  There is very little coverage for private companies, public organizations, or companies based in emerging markets.

 

Large Companies Get Heavy ESG Attention

Large Companies-Heavy ESG Attention.jpg

  • A human-driven process will always involve a certain amount of interpretation of the data.  This in turn can lead to biases that are hard to detect and remove.
  • Each human-driven result is based on its own schema and therefore they are hard to compare.  Companies do not understand why their rating varies from one system to the next and this reduces their confidence in all ratings systems.

It may be useful to take a look at some details of one company’s approach to a “Big Data” based analysis of CSR ratings. Our next post explains how CSRHub applies its methodology to address “Big Data” problems while also noting that every system has some limitations.


Bahar_Gidwani.jpgBahar Gidwani is CEO and Co-founder of CSRHub.  He has built and run large technology-based businesses for many years. Bahar holds a CFA, worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. Bahar has consulted to a number of major companies and currently serves on the board of several software and Web companies. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 17,400 companies from 135 industries in 134 countries. By aggregating and normalizing the information from 530 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

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