This case may be the first of its kind, but we all know it won&#8217;t be the last.  The SEC&#8217;s greenwashing taskforce is on a mission and this will be a wake up call to fund managers.  This particular case is around the claim that all stocks went through an ESG quality review as part of its investment process, which they apparently didn&#8217;t.
Overstating the ESG qualities of a fund to attract capital (ie greenwashing) is a very real problem which risks damaging not only the credibility of ESG, but its potential to actually get capital flowing in ways that will benefit society and help make the planet liveable for our children.
However, I must admit I don&#8217;t envy the regulators trying to police ESG space.  It often feels like it&#8217;s going through the tricky teenage years and right now they&#8217;re having a pretty wild party.  What is an &#8216;ESG quality review&#8217; in the first place?  Every fund will take a different approach to this process, but it&#8217;s guaranteed there will be subjectivity and maybe even smoke and mirrors.  What can bring integrity back to ESG funds, (aside from telling the truth about whether or not these quality reviews are being done at all) is oversight and transparency on the process; consistency and due diligence in the method.
Regulation is coming down the pipeline soon to crack down on the freeloaders at the party.
At Insig AI, we speak to a lot of asset managers anticipating regulation and striving for best practice.  They talk about the time pressures to turn around ESG analysis, and costs and expertise overheads of doing the research they want to on stocks as limiting factors.  This is why we&#8217;ve designed AI tools to take the pain out of doing due diligence on your portfolio&#8217;s public disclosures on ESG with transparency and consistency &#8211; to bring integrity to the ESG review process so that our clients aren&#8217;t looking over their shoulder for the greenwashing police.

sec-issues-first-greenwashing-fine-to-an-investment-adviser

SEC issues first 'greenwashing' fine to an investment adviser

As Quoted in the Financial Times https://www.esg-specialist.com/sustainable-companies-are-more-expensive-says-study/ 
<h2>New research from Schroders suggests investors should dig down into sectors and individual stocks to find well-priced sustainable opportunities.</h2>
Companies ranked strongly for sustainability credentials trade at a higher multiple than those that perform less well – but many opportunities remain for investors, according to new research.
An <a href="https://www.schroders.com/en/media-relations/newsroom/insights/are-more-sustainable-companies-more-expensive/" target="_blank" rel="noopener">analysis</a> of valuation metrics for companies on the MSCI ACWI index, conducted by Schroders, found that for almost every major industry sector the top 25 per cent of stocks by environmental, social and governance score were valued significantly higher than the bottom 25 per cent. ESG scores were assessed by FTSE-listed asset manager Schroders’ proprietary SustainEx model.
However, Schroders head of strategic research Duncan Lamont said this did not mean there were no attractive investment opportunities for sustainability-focused investors.
“These industries are portrayed as the bad guys when it comes to environmental impact,” Lamont said. “They are under the spotlight more than most. So it goes to figure that the market will have differentiated more between them, marking up those who are better placed to navigate the coming decades, and [marking] down those who risk being in the firing line.”
Measuring across quartiles did not show the whole picture, Lamont added. While the data indicated that “the market gets many of the big-picture calls approximately right”, there were still many areas in which ESG risks were not adequately priced in.
<h3>Opportunities in the detail</h3>
Lamont said this still gave companies an incentive to improve their ESG credentials, while investors could still be rewarded for engaging positively with companies to encourage these improvements. This was especially evident when drilling down into each of the three pillars of ESG.
Diana Rose, head of ESG research at sustainability data specialist Insig AI, said focusing in on specific elements of ESG was “key to isolating attributes that may still be undervalued by the market”. While climate change had been dominating most ESG investment discussions over the past few years, she said, there were other “key environmental issues being overlooked at the moment”.
“The savvy investor will be anticipating emerging regulation such as the Taskforce on Nature-related Financial Disclosures,” Rose said. “While the herd is following the carbon price, some digging will reveal there are some environmental areas, such as water and waste, that present tangible and imminent risks and opportunities – particularly for certain industries.”
However, for social issues, investors needed to do more digging to unearth risks as disclosures and data were patchier, making it harder to identify risks and opportunities, she said.
“There are more frameworks getting traction to define what ‘good’ looks like in this space, but still a greater demand on the investor to unearth evidence that companies are leading or lagging against their peer group,” she added. “This comes down to primary research and due diligence – reading between the lines of glossy reports to spot the gaps that might present material risks alongside impressive stats on social investment.”
<h3>Better data, better valuations?</h3>
Schroders’ research echoes many academic papers of recent years, which have reported links between companies’ ESG credentials and improved investment performance. A <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3235190" target="_blank" rel="noopener">paper</a> published in November 2019 found that institutional investors overall did not believe that equity valuations fully reflected climate-related risks.
The authors – Philipp Krueger of the University of Geneva, Zacharias Sautner of the Frankfurt School of Finance &amp; Management, and Laura Starks of the University of Texas – said investors believed sectors such as oil and gas, car manufacturing, and utilities tended to be overvalued considering the climate-related risks they faced.
They said the results indicated that the investment industry was “still at [the] early stages of incorporating these risks into their investment processes”, with larger asset owners tending to be “better prepared for the transition to a low-carbon economy”.
Chris Bennett, managing director of sustainability consultancy Evora Global, said good data was the cornerstone for identifying sustainable companies.
“Increasingly, institutions are putting pressure on investment managers to supply ESG data and have a stack of questions about sustainability,” he said. However, he said there hadn’t been a significant impact on valuations – particularly in areas such as real estate, where he said “climate risk is not being priced in”.
Disclosure laws
Several jurisdictions are implementing reporting and disclosure rules to improve the amount of information available to investors to assess climate and sustainability-related risks.
In the UK, the government implemented rules to require reporting in line with the recommendations of the Task Force on Climate-related Financial Disclosures at the start of this year. The rules apply to all reports for financial years beginning on or after 6 April 2022, and Bennett said some companies might be “viewed in a different light” after this information is published.
“I am pleased the recommendations of TCFD have now been enshrined into law,” he said. “It’s the sort of approach we need where companies must provide markets with proper information with regards to their climate risk.”
The EU requires listed and financial companies to report non-financial information, and is working on creating its own sustainability disclosure standards, while the US Securities and Exchange Commission is currently considering climate-related reporting requirements for listed companies.

sustainable-companies-are-more-expensive-says-study

Sustainable companies are more expensive, says study

As quoted in Computer Weekly &#8211; https://www.computerweekly.com/blog/Green-Tech/Using-AI-to-discern-if-enterprises-are-walking-the-talk-on-ESG 
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In the transition to net zero, the investment community needs ESG information that allows them to meaningfully appraise a firm’s progress against targets and, in turn, be able to hold them to account. However, there are complaints of ‘aggregate confusion’ from investors around the myriad of ESG ratings, using a range of different approaches to adding up data points into ESG scores.
Artificial intelligence (AI) is emerging as a powerful tool in the evolution of meaningful ESG assessment, revealing more granular snapshots of data, such as whether companies are fulfilling their net-zero promises.
<h2 class="section-title">Assessing ESG ratings</h2>
ESG scoring has enormous potential to unlock a significant amount of information on the management and resilience of companies when pursuing long-term value creation. It can also represent an important market mechanism to help investors better align their portfolios with environmental and social criteria that match with sustainable development.
However, the investment community is faced with the dilemma of ESG ratings varying strongly depending on the provider chosen. This happens for reasons that are often obscured in the methodology and aggregation of data, such as different frameworks, key indicators and metrics, data quality, qualitative judgement, and weighting.
These differences make it nearly impossible for investors to have confidence in the way they are managing the material ESG risks within their investment mandates, and can lead to missed opportunities as well as nasty surprises.
<h2 class="section-title">AI supports the thoughtful analyst</h2>
At the global business and political summit of COP26, a key focus area was around strengthening the impact of ESG investment, calling for greater efforts toward transparency, consistency, and comparability, alongside the announcement of a convergence of reporting standards.
Attention was placed on corporate disclosures as the real temperature check, both for businesses and the investors that finance them.
However, gathering evidence from corporate disclosures is a combination of quantitative science and qualitative art, which places a high demand on the time and efforts of the analyst.
While the bar is being raised in ESG assessment, AI has also been busy, fast developing into a versatile partner for the investment analyst.  Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention; and it’s becoming more available.  The advent of cheaper off-the-shelf algorithms, combined with advances in computational power, mean investment managers and analysts have new superpowers.
This has not gone unnoticed by the powers driving UK PLC’s transition to net zero – The Task Force on Climate-related Financial Disclosures (TCFD)  is a disclosure body who itself uses AI to verify whether large number of organisations include its recommendations in their reports.
Proprietary machine-learning models can process and visualise huge amounts of messy qualitative data, taking a lot of the grunt-work out and enabling more transparent, robust and timely analysis. When directed thoughtfully, the insights that AI offers can enable investors to make more informed investment decisions and reduce the dependence on a high-level ESG score.
<h2 class="section-title">Directions of travel</h2>
One of the more insightful ways to consider ESG data is to analyse change over time through a company’s disclosure history – this can reveal gaps that may flag up greenwashing, as well as meaningful improvements compared to a peer group.  A search for trends like this is just one example of analysis that’s painful to tackle manually but lends itself beautifully to technology.
To illustrate this, let’s take a look at the collective volume of the FTSE 100’s net zero-related disclosures across all reports published since 2015.  Using a keyword approach, we can class these into generic terms, labelled ‘Net Zero Talk’, and phrases that carry more weight and accountability, ‘Net Zero Walk’.
The two trajectories are striking, and when this analysis is taken down to company level, this data equips investors with a new perspective on the gap between rhetoric and accountability, so they have better agency to ask the right questions.
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<img class="wp-align wp-image-173 aligncenter" src="https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-1-300x191.jpg" sizes="(max-width: 542px) 100vw, 542px" srcset="https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-1-300x191.jpg 300w, https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-1-1024x653.jpg 1024w, https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-1-768x490.jpg 768w, https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-1.jpg 1343w" alt="" width="542" height="345" />
Fig 1. Count of Net Zero related phrases mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts. Net Zero Talk: ‘net zero’, ‘low carbon’, ‘climate change’; Net Zero Walk: ‘science based target’, ‘climate scenario’, ‘decarbonisation’, ‘carbon transition’, ’climate transition’. Source: InsigAI
Given the high materiality and profile of workforce issues such as human rights, diversity and pay among certain sectors, using AI to view disclosures with a social lens is also revealing.   Changes in the social narrative of the consumer staples sector of household names between 2018 and 2020 show the scale of the gap between apparently leaders and laggards on social disclosure, particularly with some companies accelerating their reporting significantly in 2020.
<img class="wp-align alignnone wp-image-174 aligncenter" src="https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-2-300x185.jpg" sizes="(max-width: 541px) 100vw, 541px" srcset="https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-2-300x185.jpg 300w, https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-2-1024x630.jpg 1024w, https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-2-768x473.jpg 768w, https://itknowledgeexchange.techtarget.com/greentech/files/2022/05/Graph-2.jpg 1327w" alt="" width="541" height="333" />
Fig 2. Count of social impact related phrases mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts.  Phrases:  ‘human rights’, ‘forced labour’, ‘diversity’, ‘pay gap’. Source: InsigAI
<h2 class="section-title">Nowhere to hide</h2>
Geopolitical summits like COP26 have made it clear that the spotlight is firmly on company disclosures regarding net zero, with the idea that those driving the transition are rewarded by the market. This requires investor scrutiny. Science-based metrics are essential, but so is the qualitative assessment of the context and direction of travel contained in disclosures.
Stock-pickers are pouring more and more into companies making most progress regarding ESG, so the potential for greenwashing will increase. Last year, the UK’s Competition and Markets Authority found that <a href="https://www.gov.uk/government/news/global-sweep-finds-40-of-firms-green-claims-could-be-misleading">40%</a> of environmental information on corporate websites was deemed to be misleading.
By using machine learning algorithms and natural language processing, businesses and investors now have tools at their fingertips to surface and expose evidence of empty promises and hold companies to account. In the evolution of the ESG space, the benefits of access to powerful technology to decision-makers is playing its part. Now it’s time to read between the lines to find who’s really going to move the needle.

using-ai-to-discern-if-enterprises-are-walking-the-talk-on-esg

Using AI to discern if enterprises are walking the talk on ESG

<div class="attribute-short">
As quoted in <a href="https://www.pensions-expert.com/Law-Regulation/What-does-the-future-of-ESG-disclosure-look-like?ct=true">Pensions Expert &#8211; Link</a>
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For trustees, the rise of environmental, social and governance factors within the pensions sector has resulted in a shift in agendas, with compliance and regulatory matters growing in prominence.
</div>
<div class="attribute-long">
Fifty-three per cent of trustee boards now consider ESG an important agenda item, according to <a href="https://www.bmogam.com/uploads/2021/07/32a8fdc88ae69de2acd5b732bb30c730/pmi-bmo-report-2021.pdf" target="_blank" rel="noopener">research</a> produced by the Pensions Management Institute and BMO Global Asset Management in 2021.
This is up markedly from 2020, when ESG was important for just 29 per cent of trustee boards.
With current regulatory shifts and changes to disclosure standards driving up the importance of ESG matters, trustees are now thinking in earnest about what ESG disclosures will look like in the future.
&nbsp;
&#8216;Company disclosures will need to evolve to demonstrate a full assessment of the key dimensions material to a company&#8217;
<div class="quotation right" style="text-align: right;">Thomas Höhne-Sparborth, Lombard Odier</div>
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<h3>Turning up the volume</h3>
The trend of climate-related reporting across the FTSE 100 over the past few years illustrates a potential trajectory for the scale of ESG disclosures being made.
Between 2015 and 2020, disclosures quadrupled, according to Diana Rose, head of ESG research at Insig AI.
“Climate disclosure will keep increasing in the UK over the next five years, driven by the Task Force on Climate-related Financial Disclosures becoming mandatory from April 6 2022 for large companies,” she says.
“I’d expect to see a big increase compared with 2020 in TCFD-related disclosures in the 2022-23 reporting cycle, but by no means a peak.”
Within the same time frame, Chris Fidler, senior director of global industry standards at the CFA Institute, is expecting the International Sustainability Standards Board to issue climate-related disclosure standards. These  guidelines will create adherence within “major capital markets”, he says.
On the back of this, Fidler expects the “quality and availability of issuers’ ESG disclosures related to other ESG issues to improve as well”, although it is unlikely to result in the same extent of standardisation.
Thomas Höhne-Sparborth, head of sustainability research at Lombard Odier, says a focus on the “larger spectrum of material sustainability factors with a broader scope across a company’s full supply chain”, can be expected alongside the recognition of a company’s impact on its wider environment, as well as the possible impact of that wider environment on the company.
“We believe company disclosures will need to evolve to demonstrate a full assessment of the key dimensions material to a company,” he says.
“On environmental issues, this extends beyond an analysis of carbon footprints and other widely reported data, to a broader appreciation of dimensions such as biodiversity impact, waste footprints, water scarcity and availability, and chemical pollution, among others.”
Höhne-Sparborth adds that disclosures must “recognise two sides of the same coin” by demonstrating the company’s understanding of the impact it has on the wider environment and the company’s exposure to transitional and physical risks is a two-way street.
“As such, disclosures should focus both on the company’s wider impact, as well as its financial exposure to key risks,” he says.
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<h3>Social risks</h3>
On the social side of ESG, the trend for increasing volumes of disclosures is also apparent, particularly around workforce, human rights and community issues, Rose says.
“I’d expect to see more disclosure focusing on a company’s supply chain, or ‘value chain’.  There are growing calls from all sides for greater transparency on issues such as modern slavery, but very little meaningful disclosure on it yet,” she continues.
“It will take a big shift and there will be push-back from corporates as it’s hard to do, but they may have no choice thanks to the combined pressures of media and consumer awareness, vocal watchdogs and, of course, regulation.”
But five years is a long time for a space “still immature and so connected to both the financial dynamics and ethics of a volatile world”, Rose adds.
“The current war in Europe is a reminder to expect the unexpected to rock what ESG means to investors, trustees, corporates and other stakeholders alike.
“As investors pin down specific ESG attributes that translate to better returns or pose the greatest risk, these will no doubt be a driving force in shaping the disclosure landscape, alongside the social and environmental agendas hoping finance will bring about the change they want,” she says.
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<h3>Preparing for change</h3>
Efforts to improve ESG disclosures currently face several barriers, including the lack of a universal taxonomy and data standards, increasing workload impacting trustees, and a lack of ESG expertise, according to experts.
Rose does not believe that these challenges will be solved entirely within five years, but observes that the “market is responding” with a growing pipeline of young ESG professionals converging with improved reporting frameworks.
The scale of upheaval facing both the pensions sector and the wider world means exposure to the transition must be “carefully managed”, Hohne-Sparborth says.
He believes the market is currently “ill-adapted” to the rise of ESG and is not yet “pricing in the significant downside and upside risks our economy is exposed to.”
Yet there is a need to prepare for these changes now, and Hohne-Sparborth says trustees must begin by identifying partners that have the “capabilities, expertise and mindset to help them navigate the transition in a manner aligned to their investment priorities”.
</div>

what-does-esg-disclosure-look-like

What does the future of ESG disclosure look like?

The &#8216;G&#8217; in ESG is widely known as governance, but what exactly is governance? And how can it be used to ensure better long-term performance in a company?
You can listen to our ESG Research Director <a title="https://www.linkedin.com/in/ACoAAAEN8ukB24Qgb2FV7o0onAJyWf9meMTRnpw" href="https://www.linkedin.com/in/ACoAAAEN8ukB24Qgb2FV7o0onAJyWf9meMTRnpw">Diana Rose</a> discuss governance on the latest <a title="https://www.linkedin.com/company/financial-times/" href="https://www.linkedin.com/company/financial-times/">Financial Times</a> Adviser Podcast <a href="https://podcasts.apple.com/gb/podcast/exploring-governance-the-g-in-esg/id1437151302?i=1000556289364">here.</a>

what-exactly-is-governance

What exactly is governance?

<h2>TCFD: Task Force on Climate-related Financial Disclosure</h2>
One could argue that this four-letter acronym is the start of the green revolution for the corporate reporting world. With TCFD reporting now mandatory from 6 April 2022 for over 1,300 large corporations with &gt;500 employees and &gt;£500m in annual turnover, the UK is the first of the G20 to make it law, with others set to follow.
One could also argue that the regulatory burden of disclosure and reporting is going to cause a huge amount of pain. Not just for the companies required to report, but every investor required to analyse the mountain of disclosure documents coming their way, in order to substantiate their investment decisions.
The TCFD was established in 2015 and finalised in 2017 to drive the reporting of climate-related financial information in publicly available Annual Reports. Widely applicable across all sectors and jurisdictions, disclosures are around 4 core pillars that demand oversight of climate risk at every level of business: governance, strategy, risk management, and metrics and targets.
There is no question that the merits of TCFD are going to change the way companies operate and report, and will certainly change the way investors evaluate the companies that they buy into. The near-term pain and overhead of the reporting requirement will also hopefully accelerate corporate awareness on their impact on climate and, over time, we all start to see and feel the benefits of a recovering environment.
With the 6th of April looming, the big question is: how many of the major FTSE companies are prepared for the disclosure task ahead of them or risk falling foul of regulation? What’s telling is to see how long a company has been talking about TCFD, and where they publish their disclosures. The point here is that TCFD requires significant lead-in time to implement and requires that climate risk is addressed in the fundamentals of the Annual Report.
<h2>What Does the Data Say?</h2>
Using the Insig.AI database of corporate ESG and financial documents, the goal was to identify trends around TCFD disclosure over time and across report types.
The approach was to use a keyword and phrase search across the entire database on: “TCFD”, “climate related”* and “climate risk”.
COMPANY, TIMEFRAME AND DOCUMENT UNIVERSE 
Companies: FTSE 100 (plus a few other UK names) 
Timeframe: 2015-2021 
No. of documents queried: 3,345 
Document Types: Annual Report, Integrated Report, Sustainability/ESG Report, Climate Change Report, Climate Change Policy, Financial Document, ESG Data
*Searching “climate related” also accurately surfaces “climate-related” which is the TCFD naming convention.
<h2>Summary Observations</h2>
<h3>Who was first to start referencing TCFD?</h3>
Aviva, Barclays, BHP and LSEG were the first FTSE companies to make reference to TCFD back in 2016, before the TCFD recommendations were even finalised.  This indicates foresight and these companies have continued to be leaders in TCFD disclosure over the past 5 years.
<img class="wp-image-2540 size-full aligncenter" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png" alt="" width="904" height="346" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=200%2C77&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=300%2C115&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=400%2C153&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=600%2C230&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=768%2C294&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=800%2C306&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?fit=904%2C346&amp;ssl=1 904w" sizes="(max-width: 904px) 100vw, 904px" />
<h3>References Over Time</h3>
FTSE companies have been actively referencing “climate related” since 2017 and the trend has continued to grow year on year. Reference to “climate risk” only started to gain traction in 2020. TCFD references have doubled year on year since 2017.
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<img class="wp-image-2541 size-full aligncenter" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png" alt="" width="932" height="592" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=200%2C127&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=300%2C191&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=320%2C202&amp;ssl=1 320w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=400%2C254&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=600%2C381&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=768%2C488&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=800%2C508&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?fit=932%2C592&amp;ssl=1 932w" sizes="(max-width: 932px) 100vw, 932px" />
<h3>Leaders and Laggards</h3>
Analysing combined TCFD relevant disclosures over this same period, there are clear and consistent leaders. At the other end of the spectrum, there is a stark difference in level of disclosure.  It raises some questions about the level of commitment and readiness for TCFD reporting come April. Whilst the ‘materiality’ of climate change risk and impact does vary between sectors, which means some industries would start with more established reporting, a principle of the TCFD is that climate change is a risk to all businesses.
 <img class="size-full wp-image-2542 aligncenter" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png" alt="" width="928" height="574" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=200%2C124&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=300%2C186&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=400%2C247&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=600%2C371&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=768%2C475&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=800%2C495&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?fit=928%2C574&amp;ssl=1 928w" sizes="(max-width: 928px) 100vw, 928px" />
<h3>Disclosure by Document Type</h3>
Taking 2020 as a snapshot of where TCFD disclosures are found across reports, Annual and Climate Change Reports make up just over a third each.  22% are found in Sustainability and ESG Reports.  Companies not yet reporting to the TCFD in their Annual Reports will be required to start in the 2022-23 reporting cycle.
<img class="aligncenter wp-image-2543" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_4-800x720.png" alt="" width="600" height="540" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=200%2C180&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=300%2C270&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=400%2C360&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=600%2C540&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=768%2C691&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=800%2C720&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?fit=1011%2C910&amp;ssl=1 1011w" sizes="(max-width: 600px) 100vw, 600px" />
&nbsp;
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tcfd-which-ftse-companies-are-ready

TCFD: Which FTSE companies are ready for the 6th of April…and which are not?

what-is-so-hard-about-pdf-text-extraction

What is so hard about PDF text extraction

If you ask any data scientist what they spend most of their time doing, they won’t tell you that it is solving the world&#8217;s hardest problems, or fine tuning an incredible new life changing algorithm, or even predicting the next bitcoin bounce. No- what they would say is, collecting and cleaning data. Period. Once they have done this, they still have to transform it into a format that is machine readable. The reality is that these seemingly mundane tasks are 80% of their day job. In the asset management industry, there is a constant drive to find a trading edge or to find a new signal in a dataset that informs an investment decision. You only need to look at firms like Renaissance, TwoSigma and Citadel to find evidence of advanced data science driven strategies. They have invested millions of dollars into curating their own datasets and building teams to find value in their data. Anyone new to the game, however, has to start from scratch. This is why getting access to transformed, structured, labelled/tagged and machine readable data is so valuable.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Initial success</h3>
In late 2020, we were developing a new machine learning model for an asset management client who was looking to identify the strategic direction of a company based on earnings call transcripts and annual reports. We started with the S&amp;P 500 and very quickly were able to get machine readable transcripts from Capital IQ along with companies&#8217; annual reports from the SEC Edgar database. The data was well formatted, tagged, easy to download and there was a lot of history &#8211; a data scientist’s dream! We used a combination of NLP to analyse what was being said in the text, and predictive machine learning algorithms to analyse the company financials. This approach resulted in an ecstatic client and an expanded mandate.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">The challenge</h3>
Filled with confidence (perhaps even a hint of arrogance) following our recent success, we set out to repeat the exercise on European companies. The earnings call transcripts were again, readily available and these were ingested and processed quickly. Next, we set out to add the European annual reports and filings, expecting to find the same clean dataset, beautifully structured as was held in the Edgar database. Instead, we found ourselves in a data-science nightmare. European companies do not have to report in a uniform, consumable standard, nor is there a central database to access files. They are free to do whatever they want and can publish the content anywhere, whether this be the investor page on their website, to LinkedIn. Put simply, there is no consistency of format, or filing. Given we had promised our client a quick turnaround (based on our first experience), we had a problem.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">The solution</h3>
In a scramble to find a reliable source of data that would help us avoid having to find and download thousands of PDF documents that we would have to manually clean and tag, we stumbled across an article by a small company called FDB Filings. They had apparently come across the same problem and built a business around it!
&#8211; Comprehensive coverage of European annual, interim &amp; press release reports
&#8211; PDF documents converted into a machine readable JSON format
&#8211; Link back to every original source document
&#8211; Point-in-time entity mapping
&#8211; Clear and well-defined tags on revenue guidance, executive speaker, business risks &amp; more
&#8211; 10 years of filing history
&#8211; Data accessible via an AWS S3 bucket
We set up a trial to test the data, and to our collective delight, the quality of their technology, data structuring and team was outstanding! We were able to ingest and score 10+ years of annual reports for 1,000 European companies, almost immediately and deliver back the results our client was expecting. The team at FDB had set out to solve a very hard data structuring problem and succeeded so much so that In November 2021 Insig AI acquired FDB.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">What we learned</h3>
Starting from scratch doesn’t necessarily require billions. This process demonstrates that a combination of thoughtful data structuring, good timing, and robust infrastructure, is fertile ground to create an AI-powered analytical platform, that delivers unbiased, uniform, value-add insights to an entire sector, across different markets, in an accessible, and easily digestible format. Financial Institutions are entering an entirely new paradigm, one where stakeholders and shareholders alike are squarely focused on sustainability and accountability. This is bringing a significant layer of complexity and cost to a financial institution. Technology and data companies are emerging with cost effective and transparent data scientific propositions that enhance the due diligence and risk management capabilities of an institution, while informing and improving investment decisions. For the heads of risk and sustainability at financial institutions, the choice of data provider is yours, but the time for acting, is now!

european-machine-readable-company-filings-a-data-scientist's-dream

European machine readable company filings: a data scientist's dream

As quoted in <a href="https://www.pensions-expert.com/ESG/Is-now-the-time-for-schemes-to-consider-natural-capital?ct=true" target="_blank" rel="noopener">Pensions Expert &#8211; Link</a>
<h2 class="p1">2022 is shaping up to be the year when natural capital joins the mainstream agenda in ESG.</h2>
Following the stickiness of the Taskforce for Climate-related Financial Disclosures (TCFD), the first draft of the equivalent Nature-related framework (TNFD) is due to be released this quarter.  It will contain recommendations on how to manage, report and act on nature-related risks in practice, and calls for organisations to integrate these into their governance, strategy, targets and performance metrics.
Other highly influential ESG frameworks are adding to this momentum; the Climate Disclosures Standards Board (CDSB) has released biodiversity guidance, the Global Reporting Initiative (GRI) will release an updated Biodiversity Standard, and the CDP has announced it will expand its questionnaire from climate, forests and water to include oceans, land use, biodiversity, food production and waste.  The Science Based Targets Network is bringing out guidance on targets for nature, and the EU is due to draft a biodiversity disclosure standard as part of the development of the European Sustainability Reporting Standards.
This acceleration is in response not only to the growing international agenda around natural capital, as witnessed at COP26, but to the market calling for better risk management and disclosure frameworks to support finance and business to assess and manage this area of risk and opportunity.
<h2 class="p1">What does this all mean for investors and trustees with ESG goals, balancing risk and returns?</h2>
There will be growing expectations for nature-related considerations to be integrated into ESG investment, in the same way there has been for climate over the past few years.
However, to make evidence-based decisions, investment-useful information needs to be available.  Currently, there is very little being disclosed on natural capital compared to climate; companies are barely off the ground and it will take time that environmentalists argue we don’t have to spare.
<img class="alignnone wp-image-2569 size-full" src="https://insigai.io/wp-content/uploads/2022/04/natural-capital-1.png" alt="" width="1636" height="914" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=200%2C112&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=300%2C168&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=400%2C223&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=600%2C335&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=768%2C429&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=800%2C447&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=1024%2C572&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=1200%2C670&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?resize=1536%2C858&amp;ssl=1 1536w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-1.png?fit=1636%2C914&amp;ssl=1 1636w" sizes="(max-width: 1636px) 100vw, 1636px" />
<blockquote>
Considering the trajectory of climate-related disclosures over the past 7 years, we could reasonably expect nature-related disclosures to follow suit.
</blockquote>
Businesses will start to feel the pressure to disclose information in line with the leading frameworks – the issue will become more material to more industries.  Awareness around the topic will also start to grow in the media and among the pension-holding public, alongside tightening regulation and the trend for greater engagement and influence between investors and what their money is doing.
<h2 class="p1">Nature-positive outcomes and opportunities</h2>
The timing of the natural capital agenda and the momentum behind ESG investment means that it’s not going away, which is good news for the planet and its inhabitants, but presents fresh challenges for robust ESG assessment on the dependencies and impacts on nature to ensure money is flowing in ways that will contribute to ‘nature positive outcomes’.
There are various initiatives to quantify natural capital, and these should gain some traction in being integrated into financial decisions, such as the Natural Capital Protocol and Value Balancing Alliance.  But it can’t be ignored that something like soil mineral quality is hard to quantify in the same as C02, and its value is interconnected with other ESG factors such as climate change and society.  Many biodiversity risks are also hidden in the international supply chain, where regulation is harder to track and oversight tends to be weaker.
For trustees and investors, there’ll be the mandate both to demand greater evidence and transparency from their portfolios (and critically, their supply chains), and a chance to take a strategic and long-term view on opportunities around natural capital such as nature-based solutions and climate change adaptation.  The emergence and convergence of the reporting frameworks will support this, and will help investors to know what to look for among disclosures as they grow, and to ask the most pressing questions of their portfolio companies.
<h2>Appendix: Additional Chart Options</h2>
Comparing frequency of reference to Climate and Nature related disclosure (FTSE 100 2015 &#8211; 2021)
<img class="alignnone wp-image-2570 size-full" src="https://insigai.io/wp-content/uploads/2022/04/natural-capital-2.png" alt="" width="1708" height="1084" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=200%2C127&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=300%2C190&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=320%2C202&amp;ssl=1 320w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=400%2C254&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=600%2C381&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=768%2C487&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=800%2C508&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=1024%2C650&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=1200%2C762&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?resize=1536%2C975&amp;ssl=1 1536w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-2.png?fit=1708%2C1084&amp;ssl=1 1708w" sizes="(max-width: 1708px) 100vw, 1708px" />
Leaders and laggards in frequency of reference to Climate and Nature related disclosure (FTSE 100 2015 &#8211; 2021)
<img class="alignnone wp-image-2571 size-full" src="https://insigai.io/wp-content/uploads/2022/04/natural-capital-3.png" alt="" width="1248" height="1352" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=200%2C217&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=277%2C300&amp;ssl=1 277w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=400%2C433&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=600%2C650&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=768%2C832&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=800%2C867&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=945%2C1024&amp;ssl=1 945w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?resize=1200%2C1300&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/natural-capital-3.png?fit=1248%2C1352&amp;ssl=1 1248w" sizes="(max-width: 1248px) 100vw, 1248px" />

is-now-the-time-for-schemes-to-consider-natural-capital

Is now the time for schemes to consider natural capital?

women-in-finance-experts-reflect-on-their-experiences

Women in finance - experts reflect on their experiences

technology-for-explainable-ai-look-in-the-white-box

Technology: For explainable AI, look in the white box

We spend a large part of our time working with text data and as a result we often find ourselves implementing some kind of NLP tool either as part of our product or on behalf of customers. One of those tools is Named Entity Recognition (NER) – a process for identifying entities, e.g. people and places, in text. There are a wide range of possible use cases for NER but ours are usually focused on investing where we have used it to augment search tools, automatically connect individuals and companies, and screen huge volumes of text for known bad actors. While we work with NER regularly, and with text data every day, we don’t consider ourselves to be experts in NER specifically – our interest is practical in nature and as such our experience is in implementing other people’s models rather than in developing new models from scratch.
This short piece contains some of the lessons we’ve learned: it’s primarily aimed at the potential NER user without much experience of the technology but we hope there will be something here too for the algorithm developers and vendors who may find it useful to see the experiences of a more narrowly commercially focused organisation.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">What is NER?</h2>
Before we go any further, for those who aren’t familiar with NER it is roughly speaking the process of going from this:
Elon Musk invites Vladimir Putin to connect on the Clubhouse app.
to this:
(Elon Musk)[PERSON] invites (Vladimir Putin)[PERSON] to connect on the (Clubhouse)[ORGANISATION] app.
As you can see there are basically two components to NER: (1) identifying the strings (i.e. words) which are entities, and (2) identifying the type of entity. The main benefit of this technology is that because it is automated you can process volumes of text that would be completely unfeasible for a human with the result that several tools and applications which were previously impossible become possible.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">One NER algorithm to rule them all?</h2>
One of the most immediately striking things when you attempt to implement NER in real use cases is that the available solutions are very generic and may not fit that well for your specific use case. One way in which this appears is in the labels used to categorise entities.
The overwhelming majority of solution developers and vendors use 4 categories of entity: people, places, organisations, and miscellaneous. This is mostly for the historical reason that these were the categories used in the CoNLL-2003 shared task which has acted as a touchstone for the industry ever since. Nor are the categories bad or unreasonable – given a task to group entities into four intuitive groups it is likely many people would arrive at something similar.
The problem is that reasonable though those categories may be they don’t necessarily make sense in every use case. In our use cases, for example, we typically don’t care about organisations as such but about companies. The difference being that ‘organisation’ is a broader category which includes things like the U.N. and the FBI.
The practical implication of this is that many off-the-shelf NER models may simply not be that useful for your application and may require some additional work or post-processing steps to deliver what you want. This situation seems to be improving as some more targeted vendors/developers are beginning to offer a wider range of commercially useful categories but given the number of possible entity categories we might want to target we are probably some way off having readily available models for all of them and it may not even be possible. We are also seeing more platforms which offer some ‘train your own’ capabilities and this may be a promising path.
What this touches on is that, practically speaking, it is a little misleading to think of NER as a generic problem with a generic solution. Perhaps one day there will be one algorithm to rule them all but today the best solution for identifying drugs in research papers is unlikely to be the same one as for identifying companies in regulatory filings. It may be more helpful for both consumers and producers to think of NER as a family of solutions to a range of different problems which share some formal characteristics. From a commercial perspective this is no bad thing because it is usually far easier to solve problems in a domain or application specific way than in a general manner as we will see below.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">What does good look like?</h2>
One of the surprising facts about implementing NER solutions in the wild is not just that it is tricky to work out which solution is best, but that it is also much harder than you might think to establish some objective and general measure of quality against which you can measure them.
If you wanted to know which NER solution was best for you, a reasonable starting place might be the publicly available benchmarking data. Unfortunately, our experience is that these benchmarks have not been especially useful. Any benchmark is only useful if it matches your intended use case in the relevant ways and we have found that the absolute and relative performance of NER algorithms on benchmarks was quite different to the performance on our specific data (mainly company filings and regulatory releases). Further, just as with a CPU or database benchmark, implementation details matter and can contribute significantly to the outcome. Even when the implementation details in a benchmark have been honestly chosen they may not be applicable to your circumstances and again result in the benchmark being unrepresentative.
These issues are really true of all benchmarking processes to a greater or lesser extent, but there are some features of NER that make it difficult even in theory to establish a general and objective measure of quality. Consider an algorithm which returns us the following:
Philip (Seymour Hoffman)[PERSON] was a prolific actor.
It correctly identifies “Seymour Hoffman” as a person, but misses the full name. Should this receive a full point, half point or no point? And what about cases in which the correct string is identified but the wrong label is applied? There just isn’t a right answer here which doesn’t depend on the context and use case – even among the relatively homogenous set of use cases we have encountered it has sometimes made sense to use very strict scoring criteria and at other times to weight it differently.
What this means is that if commercial circumstances allow it is far preferable to have (1) an independent benchmark which reflects the data you will be running the models on and (2) a scoring model which reflects the value of different kinds of partial scores in your use case. This might seem intimidating or impractical but we have found it more achievable than you might expect – we have built test datasets of &gt;90,000 tokens which have been hand labelled multiple times (with additional human reviews) in under a week with a small team. In general for situations where you are considering investing substantial resources into a solution or where you are sensitive to performance differences we have found the ROI to be compelling.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">Algos in action: YMMV</h2>
You could be forgiven for assuming that all NER solutions would perform about the same – after all they are all using roughly similar methodologies, designed by people with similar skills and experience, and tend to be in the same ballpark on claimed performance. This has not been our experience at all and we have seen very wide differences in performance on our independent benchmarks. For example below is a table showing the F1 scores (this is a value between 0 and 1) of different providers (OS = Open Source, COM = Commercial) in one of our benchmarks and you can see that in a sense the best performing model was almost twice as good as the worst.
<table style="border-collapse: collapse; width: 100%;">
<tbody>
<tr>
<td style="width: 10%;">Provider</td>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS4</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS6</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM4</th>
</tr>
<tr>
<td style="width: 10%;">F1-Score</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.36</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.42</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.44</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.46</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.47</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.54</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.56</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.63</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.68</td>
</tr>
</tbody>
</table>
There are of course other dimensions on which one algorithm can be superior to another including speed (latency and/or throughput), ease-of-use, cost etc. NER algorithms are highly parallelizable so for self hosted solutions the limiting factor is usually going to be the size of the compute grid you have access to and the cost of running it. Here again the differences can be enormous and a range of 100x is not uncommon which, depending on the size of your dataset, may make some options unfeasible.
Another important and often overlooked consideration is that with a vendor API you do not typically have visibility into which model they are using or what changes are being deployed. There are ways to monitor the stability of a vendor algorithm in production but in practice these are cumbersome and not widely used – functionally these are black boxes. Again the exact use case matters, for use cases that are highly sensitive to changes or refinements in the underlying model you may need to self-host, seek guarantees from your vendor, and/or put a monitoring system in place.
A side note on performance is that we have seen some success using post processing steps on the outputs of the models. Because we tend to care about specific data types (in our case usually company filings) we can often achieve meaningful accuracy improvements with simple heuristics which exploit domain specific formal or content based features of our datasets. Below is the same table as above showing F1 scores after some very simple post processing steps were applied. We can’t promise that these results will be replicable in every case but for commercial applications this is certainly worth considering because the datasets being used will often have features that lend themselves to this kind of exploitation – for example, formal/structural characteristics or a tight domain focus.
<table style="border-collapse: collapse; width: 100%;">
<tbody>
<tr class="MuiTableRow-root MuiTableRow-head css-1hsm0p4-MuiTableRow-root">
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 16.7257%;" scope="col">Provider</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10.708%;" scope="col">COM1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.61062%;" scope="col">OS1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.78761%;" scope="col">OS2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.87611%;" scope="col">OS3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.87611%;" scope="col">OS4</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 11.1504%;" scope="col">COM2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 11.2389%;" scope="col">COM3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.9646%;" scope="col">OS6</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 11.2389%;" scope="col">COM4</th>
</tr>
<tr class="MuiTableRow-root css-1hsm0p4-MuiTableRow-root">
<th class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 16.7257%;">F1-Score</th>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10.708%;">0.36</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.61062%;">0.47</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.78761%;">0.49</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">0.51</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">0.52</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.1504%;">0.61</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">0.61</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.9646%;">0.67</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">0.75</td>
</tr>
<tr class="MuiTableRow-root css-1hsm0p4-MuiTableRow-root">
<th class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 16.7257%;">Uplift (%)</th>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10.708%;">0.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.61062%;">11.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.78761%;">11.4</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">11.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">11.3</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.1504%;">12.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">9.7</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.9646%;">5.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">10.0</td>
</tr>
</tbody>
</table>
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">Takeaways</h2>
As people that work with text data everyday we are excited about the potential role NER technologies can play in a wide range of applications as they improve and evolve. To recap our observations for potential users and developers are:
Many solutions currently available are very generic meaning a little more effort may be required to deliver what you want. A definition of ‘good’ is less obvious than it might seem and benchmarking against your definition is both advisable and surprisingly achievable. In our experience performance varies greatly across different providers and it may be possible to improve results with some post processing steps.

some-observations-from-implementing-named-entity-recognition-algorithms-in-the-real-world

Some observations from implementing Named Entity Recognition algorithms in the real world

Many investors have in their heads a model of the commercial world that feels natural to them. In this model the basic units are the listed companies: there is Exxon, Shell, and BP in oil, P&amp;G, Nestle, and Colgate in consumer staples and so on. Working with these entities everyday it can be easy to forget that often they are financial abstractions sitting on top of the world made up by their operating subsidiaries. This is not only true for obvious conglomerates and holding companies like Berkshire Hathaway, but also for many large companies which have multiple product lines and offerings. While investors may spend most of their time in the financial world, customers, suppliers, and employees primarily interact with the operating world.
This abstract sounding idea explains one of the most common reasons why it is hard for investors to find really impactful data. Most data comes into existence at the behest of people who care about the operating world – it could be regulators who want to ensure transparency (resulting in e.g. a database of government tender offers), advertisers looking to reach specific targets (e.g. location data), or employees looking to evaluate their next career move (e.g. Glassdoor-style datasets). As such, datasets are usually cut up to fit the operating world, covering particular products, regions, or company types which do not necessarily map neatly onto the listed companies. The result is that you are often left with datasets which cover only a small part of what you care about as an investor.
We can illustrate this with an extreme example: imagine you had access to all of the consumer transactions in Spain, such that you could know with complete certainty and in real time what every consumer facing business in Spain was making; how useful would that data actually be? We can begin with the most obvious application and ask where does it make intuitive sense for this data to be predictive of revenues starting with the IBEX 35, a reasonable-ish proxy for the largest companies in Spain. We can first remove companies where there is no obvious relationship between our data and their revenues: we can throw out the commodities companies, most financials, and the likes of steel company Acerinox, infrastructure operator Ferrovial, pharma company Grifols, and wind energy company Siemens Gamesa. In fact after this exercise we are left with Inditex (the parent company of Zara), IAG (the parent company of British Airways and Iberia), Melia Hotels, and Telefonica.. From these the two most likely candidates are Melia and Inditex – in the former 38% of its rooms are in Spain, and in the latter 16.8% of sales are generated in Spain. Without question being able to forecast those component parts with 100% accuracy would be an edge well worth having, but we can see that even in this perfect world hypothetical case we would be some way from simply knowing the revenues in advance.
Not every dataset/investment pairing has this problem – particularly when the data covers important regions like the US and China – but now that the lowest hanging data fruit has been harvested more often than not some version of this problem explains the difficulty in finding impactful data in a ready-to-consume fashion. Nor is this a bad thing, investing is a game of relative advantage and there is plenty of advantage to be seized here by those willing to try. There are broadly three ways in which an investor can deal with the situation described above: (1) combine datasets in order to get insights which are impactful, (2) look for smaller companies with less diversified characteristics where it is easier to get a good data/investment fit, (3) attempt to neutralise the areas with no edge.
The first approach embraces the complexity and seeks to benefit from it but demands some sophistication on the part of the investor as they must have the capability and the resources to acquire multiple datasets and combine them in ways that produce useful insights. Sometimes this is as simple as combining similar datasets in different regions – for a CPG company whose biggest markets are the USA and mainland China we might need to purchase two point of sales datasets – other times we may be combining entirely different datasets. But even in the simple cases combining them can be non trivial from a data engineering point of view and therefore expensive: common problems would include incompatible entity identifiers (e.g. Coke vs. The Coca Cola Company vs. KO vs. US1912161007), non-matching time periods (e.g. calendar vs. fiscal), different structures (e.g. different product categories, age intervals). Less obvious is the increased analytical burden that comes with more data sources – to be in a position to generate useful insights the researcher needs to take the time not only to understand each dataset but also how they should be integrated with and weighed against each other. The result of all this is that complexity and resource-demands tend to increase not linearly but closer to exponentially with each new dataset added.
The second approach tries to avoid the resource intensivity of the first without having to concede a data advantage. It typically means looking for companies with narrow regional or product exposure and the best place to look for these is further down the cap scale where companies on average are less diversified. As a rule it is likely to be easier to find straightforward data/investment fits for companies with more concentrated businesses.In the example above looking at Spanish transactional data we could look at smaller cap Spanish companies, for example local retailers like DIA from the IBEX small cap which has roughly two thirds of its revenues in Spain and where prima facie we would expect our transaction data to be more impactful. The obvious problem with this approach is that many investors either cannot or will not have their investment universes restricted in this way.
The third approach is the one usually employed by systematic funds. Imagine we have excellent insight into Nestle’s bottled water business and we know it is going to beat expectations, but know nothing about the other businesses. If we buy Nestle, we have to accept the risk from the unknown of the other business performance and, if only done once, this risk can be tough to stomach. However, this approach becomes a lot more appealing when we can trade the same edge multiple times – over many bets, the risk from the unknowns cancels out, (as half the time you’re on the right side and half the time you’re on the wrong side) and all that remains is the edge from the bottled water business. This is a sound approach in theory but to work it requires a large number of iterations which is a luxury the discretionary investor may not have. In practice, for a discretionary investor, this approach needs to be evaluated on a case by case basis where we assess the magnitude and volatility of the unknowns as well as the trading frequency of the investor.
In our experience most investors eventually pursue the first option and introduce elements of the second and third when it makes sense to do so for reasons of resource efficiency. Once we understand this we can start to ask what the implications are for what funds will look like in the future and what investors will need to do if they want to keep up. Our view is that the need to combine datasets for insights is a key driver in the growth in the importance of research infrastructure. Infrastructure in this sense describes the capability to find, evaluate, ingest, engineer, combine, and research new datasets – it involves not just the technology elements but also the human capital in the form of the knowledge and understanding of the people who operate it. The value of good infrastructure is that it allows researchers to generate insights more effectively and with less time and cost, factors which of course become much more important as the volume and complexity of the data handled by the fund grows.
Finally, the broader implication of this is worth addressing. One point is that infrastructure tends to be costly and slow to build and this is likely over time to increase barriers to entry for new funds who would need to invest upfront to match infrastructure which incumbent funds have built over years. Further, we are already seeing, and we expect we will continue to see, how the growing importance of infrastructure results in more stable fund performance and a clearer winners-stay-winners dynamic, as better data infrastructure helps performance which enables further investment in better infrastructure which enables better performance and so on.

why-is-impactful-data-hard-to-find-and-what-it-means-for-the-future

Why is impactful data hard to find and what it means for the future?

In the past few years we have worked with many fundamental investors to help them get value from new data and over that period we’ve seen first-hand the problems funds routinely run into and the frustration that can arise as a result of them. Over time we’ve learnt that many of the problems stem from the same small group of mistakes and that these mistakes can be fixed. In this short article our goal is to describe and to codify two of the most common problems, to explain why they tend to occur, and to suggest some actionable remedies.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">The incompatibility problem</h2>
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Problem</h3>
One of the most common problems funds hit when bringing on new data or new data methods is difficulty getting the analysts/PMs to engage. A common pattern is that a data vendor or internal data researcher is pitching something they think should be interesting but find the analyst/PM is not engaging in the way they hoped. To the data person this can feel like outright irrationality and evidence that fundamental investors are not long for this world; to the analyst/PM it can seem like a waste of resources on hype driven but irrelevant work. In practice something like this situation often happens with sentiment scores: the data team works hard to produce scores which capture the positive/negative sentiment in a quantitative way and those scores may perform very well in a predictive capacity but when introduced to the fundamental investors after a brief spell of curiosity they fall flat and end up being ignored.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Explanation</h3>
What has gone wrong in these situations is that the investor has been presented with data which is incompatible with their process. You can think of fundamental investors as machines for converting data into investment decisions, and like any kind of machine they need the right kind of input to generate the desired results – if you put diesel into your petrol powered car you won’t get very far. While no two fundamental investors will have identical processes, as a group they are similar enough for us to make some generalisations: the kind of inputs investors thrive on are things like company filings, earnings calls, broker research, industry analysis, and news. Those are datasets – and they are datasets, even if that is not how we usually think of them – which are compatible with the investment process.
What then makes one dataset compatible and another incompatible? Ultimately it comes down to understanding. For data to be compatible with an investor’s process they need to understand where to place it in their analysis, how to weigh it against other inputs, when to trust it, and when to ignore it. Sentiment scores typically fail on this test: it’s hard to know how to weigh a negative sentiment score against your higher than consensus earnings forecast, or positive M&amp;A expectations, or long term bullishness on demographic driven sales growth; and it’s hard to know when a sentiment score pointing in one direction should trump a thesis pointing in the opposite direction. Compatibility is not a fixed concept but relative to an investor: different investors may be able to work with different datasets and the same investor may, over time, find that what is compatible for them changes as their skills and experience change.
The roots of this problem lie in a common but mistaken – in the context of fundamental investors – approach to data and extracting insights from it. Many investors don’t encounter this problem until they start working with new data, so there can be a tendency to think that the problem is the data, but really the problem is what is being done to the data. You can see this by thinking about sentiment scores again: they turn something investors are very comfortable working with (earnings calls, say) into something very alien. The reason new datasets are especially subject to this is that many of them are not consumable by investors in their original or raw form, meaning there is an extra step required. This extra step introduces data science and data scientists into the mix (not necessarily in your fund, they may be on the vendor side) and this creates fertile ground for problems to appear.
A key part of the problem is that most data scientists are trained to think about data science as an independent activity instead of one which fits into a bigger pipeline. As a result they define ‘good’ in terms of certain objective measures like model performance and tend to optimize for improving these measures. This makes perfect sense in an environment where data science is the entire process – like in a quant fund – but works very badly when data science is occupying an intermediary role – like in a fundamental fund. In our experience funds do well when instead of focusing on objective measures of correctness instead they focus on compatibility, i.e. ensuring investors understand and are comfortable working with the data they receive. Data scientists in this environment need to understand that their role is not to do the best data science in a global sense, but the most useful data science.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Solution</h3>
We have two general rules you can apply to help mitigate this problem. The first is to make the output of any data analysis, model, or signal directly, logically, and obviously impact or predict some company KPI or accounting line item. It follows from this that data scientists should avoid focusing on share prices or entirely abstract things like sentiment. This rule doesn’t prevent the data from being bad but it does guarantee that it is recognisably about something investors understand and will be compatible with their process. It also importantly gives the investor and the data scientist a common language to communicate in which allows for iteration and improvement as both parties become more familiar with the working practices and preferences of the other: &#8220;this just isn’t relevant to me&#8221; becomes &#8220;I don’t care about KPI X for company Y for these reasons…&#8221;.
The second rule is to do as little data science as is necessary, or in other words to give investors the rawest data they can handle. For obvious human reasons this can be difficult for data scientists to follow and to some it may even sound wilfully perverse. But there is a good reason, namely that every extra step in processing or each additional element of complexity adds more distance in understanding between the investor and the data.
Importantly we find reducing complexity gives the best overall results even when more complexity and processing is objectively better in terms of accuracy and performance. Organisationally this means bringing investors into the data process early and making sure they understand all of the important steps in the process even when those are technical. Once again this is a dynamic state of affairs: different investors have different degrees of understanding and any one investor can learn more over time and thereby increase the range of data that is compatible with them. It also means that investors who want to get more value from data must engage with their data teams and vendors and should not shy away from digging into the nitty-gritty of collection and processing.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">The actionability problem</h2>
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Problem</h3>
Another common way in which data can fail to deliver for the investor is that the insights from the data may not be actionable or may be uneconomical if the cost to produce them outweighs their value. As an example you might be able to track social media engagement for a consumer brand which demonstrates that it has meaningfully better engagement than its main competitors – but that is unlikely to support an investment decision in the absence of other views on the company. The problem here is that while you have an insight it is a weak insight, meaning that on a standalone basis it is not enough to motivate an investment decision. Weak insights are not necessarily a problem, in fact most insights from data are weak in this narrow sense, but are only useful if you can ensure that you have pre-existing investment theses to plug them into or if not to ensure the cost to produce them is low enough to economically use them in screening and background research.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Explanation</h3>
This problem occurs because (1) it is hard for most fundamental investors to trade small insights in isolation and (2) because the process of mining insights from data is very costly. The first issue is really less about being fundamental as such, and more a byproduct of most fundamental investors being quite concentrated: investors who run concentrated portfolios have a higher bar for inclusion that an idea must meet than a more diversified manager with an otherwise identical strategy. In our experience investors running portfolios with &lt;100 names will often find that the insights they get from the data alone are insufficient to meet that bar (we should note that this is only generally true, in practice there are exceptions). This is one of the reasons why quantitative investors can often structurally pay more for certain data because it is easier for them to trade on all of the insights in a given dataset. The second issue comes down to the contingent fact that in 2021 data and the people who work with it are very expensive, which in turn means that the cost of insights from data are also very high. These are the two factors weighing on the ROI of your data strategy and keeping them in check requires a structured process and good discipline.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Solution</h3>
We find one of the best solutions to this problem is to work ‘from the idea to the data’. When you decide to bring in new data to your investment process there are broadly two ways you can do it: you can begin with the data and mine it for insights (going from the data to the idea), or you can begin with fully or partially formed investment ideas and look for data which would support or weaken them (going from the idea to the data). Most fundamental investors, most of the time, will find that they achieve significantly better ROI following the second approach. In practice this means reviewing your positions/ideas and asking what data in an ideal world would help you to confirm or disprove an investment thesis, and then going to look for that data. When stated like this most fundamental investors find this intuitive but the difficulty comes in maintaining discipline when confronted with seemingly infinite datasets and institutional pressure to do more with data.
This amounts to running a kind of ‘actionability’ filter ahead of time and ensures resources are only spent looking at datasets where successful insight mining will have an investment impact. It has two additional benefits: first that it gives you from the outset clear metrics for success which helps cut through vendor marketing and technical blather, and second that it allows you to estimate (necessarily very roughly) ahead of time the value of a given insight and to budget accordingly. Initially you may want to run a review process over all of your holdings and ideas in a strict fashion, but over time this is likely to become less burdensome as you only really need to go through this exercise on new names and ideas. You will also likely find some data sources demonstrate their evergreen usefulness which you keep on even if holdings change whereas others are tactical, picked up and discarded as needed. As part of the review process you will need to think about the stability and longevity of your interests in companies and sectors, particularly as you consider situations where data cannot be collected after the fact, and therefore need to decide whether you are willing to invest today to answer investment questions in the future.

how-to-get-more-value-from-data-actionable-solutions-to-common-problems

How to get more value from data: actionable solutions to common problems

Like most people when possible we prefer to get our data from the official source, we also like it when companies are compelled to provide the data under pain of legal or regulatory penalties. One example of that is the National Storage Mechanism, the official document storage for companies listed in the UK – now maintained by the FCA, but for a long time outsourced to Morningstar. According to Listing Rule 9.6.1. companies are supposed to forward their annual reports to the FCA’s document viewing facility (i.e. the NSM) and issue a notification via an RIS (i.e. the RNS). Many people assume that when the source is official and the data collection is governed by regulations that the underlying data will be reliable – but here we show you a couple of examples demonstrating this is not always the case.
So if we look at Tesco we see the system working like it should, on 13th May 2020 they issue an RNS notifying the market that they have published the Annual Report and have submitted it to the NSM.
<img class="alignnone wp-image-2314 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-0.png" alt="bad-data-image-0" width="822" height="400" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=200%2C97&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=300%2C146&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=400%2C195&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=600%2C292&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=768%2C374&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=800%2C389&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?fit=822%2C400&amp;ssl=1 822w" sizes="(max-width: 822px) 100vw, 822px" />
If we check the NSM we find that annual report with the correct date:
<img class="alignnone wp-image-2310 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-1.png" alt="bad-data-image-1" width="829" height="400" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=200%2C97&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=300%2C145&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=400%2C193&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=600%2C290&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=768%2C371&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=800%2C386&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?fit=829%2C400&amp;ssl=1 829w" sizes="(max-width: 829px) 100vw, 829px" />
However, the system doesn’t always work the way it should. Particularly in the Morningstar era we routinely come across examples like this:
<img class="alignnone wp-image-2311 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-2.png" alt="bad-data-image-2" width="900" height="164" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=200%2C36&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=300%2C55&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=400%2C73&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=600%2C109&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=768%2C140&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=800%2C146&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?fit=900%2C164&amp;ssl=1 900w" sizes="(max-width: 900px) 100vw, 900px" />
This date is clearly incorrect: the 31st of December is the financial year end not the publication date (which is usually months after the financial year end). The same thing happens with Sage, where we can see the correct date from this RNS:
<img class="alignnone wp-image-2312 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-3.png" alt="bad-data-image-3" width="900" height="119" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=200%2C26&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=300%2C40&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=400%2C53&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=600%2C79&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=768%2C102&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=800%2C106&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?fit=900%2C119&amp;ssl=1 900w" sizes="(max-width: 900px) 100vw, 900px" />
But again on the NSM the ‘publication date’ is in fact the financial year end:
<img class="alignnone wp-image-2313 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-4.png" alt="bad-data-image-4" width="900" height="74" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=200%2C16&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=300%2C25&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=400%2C33&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=600%2C49&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=768%2C63&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=800%2C66&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?fit=900%2C74&amp;ssl=1 900w" sizes="(max-width: 900px) 100vw, 900px" />
The NSM is not the only party to blame, sometimes the company fails to carry out its obligations under the Listing Rules properly. If we look at Sage again, for 2018 and 2017 they did not publish an RNS when they released the annual report meaning there is now no extant public record of when that document was published. Some other FTSE 100 and FTSE 250 companies that have similarly failed at least once include Wood Group, Inmarsat, Arm, Sports Direct, Amlin, Polyus Gold, Imagination Technologies, International Power, and Aberdeen Asset Management.
This should serve as a reminder that (1) official sources are by no means immune from mistakes and (2) that something is a legal or regulatory requirement does not guarantee companies will follow it. On a more positive note the FCA’s recent decision to bring the NSM back in-house appears to have made a significant difference to the quality meaning we may have less of these issues in future.

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Bad data in official places: the UK’s National Storage Mechanism

As quoted in <a href="https://www.pensions-expert.com/ESG/Machine-learning-can-help-investors-tackle-fast-fashion-ESG-issues?ct=true" target="_blank" rel="noopener">Pensions Expert &#8211; Link</a>
<h2>Why is it difficult to uncover ESG wrongdoings in fast fashion brands?</h2>
Fast fashion’s business model is based on the mass-production of low-quality apparel at cheap prices, quickly able to respond to consumer demand and running at a higher profit margin than the rest of the industry.
Globally, apparel production has more than doubled over the last 20 years and is responsible for more carbon emissions and aviation and shipping combined, according to Greenpeace (Financial Times, 2021).
On the social side, the negative impacts of cheap labour were brought to light recently when AIM-listed boohoo Group was found to have low pay and dangerous working conditions among its Leicester suppliers.
This lack of oversight on a domestic front is a concerning indication of what goes on deeper in the supply chain, and this is why it is so difficult to uncover ESG issues in the fast fashion industry.  Its most significant environmental and social impacts will be upstream, out of sight and still broadly optional to disclose.
<h2 class="p1">What should investors consider before investing in a fast fashion brand?</h2>
Fundamentally, we need to consider whether fast fashion is compatible with the global transition to a Net Zero and socially equitable world.  In the short term, is it possible to make the margins that drive fast fashion with a legally compliant UK supply chain?  In the medium term, might tighter regulation disrupt the business model?  In the longer-term, what does the apparel industry’s own sustainability transition look like and who is leading the way?
Indications of good ESG practice come down to governance and transparency; strategic leadership, oversight and reporting of the supply chain, robust sourcing policies and adequate internal processes.  Engagement with NGOs such as Transparency Pledge, Labour Behind the Label and the Better Cotton Initiative may demonstrate a brand is willing to be held to account to industry benchmarks.
<h2>How can data and tech be used to paint a better picture of sustainability efforts?</h2>
On the basis that reliance on an ESG rating agency score is not enough to make a distinction between the good, the bad and the ugly, investors will need to look to other sources of information.  NGO benchmarks can be insightful but, fundamentally, investors need to look at what a company discloses on and how that looks in the context of its peer group and over time.
This quickly becomes a data challenge for an analyst, with corporate disclosures scattered across multiple reports over the year, awash with green marketing and growing exponentially.
The good news is that technology has been fast developing into a versatile partner for the investor as AI and machine learning have become more democratic.  With advances in cloud computing and the growth of open source coding libraries, access to sophisticated algorithms and serious computing power that is affordable, meaning investors have new weapons to hold companies to account.
Machine-learning technology can process and visualise huge amounts of messy qualitative data, taking a lot of the grunt-work out and enabling meaningful insights to be extracted.  For example, to analyse ESG data over time through a company’s disclosure history compared to a peer group is a nice example of a task that’s painful to tackle manually but lends itself naturally to technology.
<h2 class="p1">Is the picture positive or negative regarding their recent sustainability efforts?</h2>
Looking at climate change disclosures across all reports published since 2015 between a fast fashion peer group and other big retail names, some patterns immediately come to light.  There’s a significant gap between the leaders and laggards, and different trajectories in direction of travel between those who’ve been reporting consistently and those whose disclosures have recently accelerated.
<img class="alignnone wp-image-2568 size-full" src="https://insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png" alt="" width="1377" height="689" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=200%2C100&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=300%2C150&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=400%2C200&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=600%2C300&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=768%2C384&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=800%2C400&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=1024%2C512&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=1200%2C600&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?fit=1377%2C689&amp;ssl=1 1377w" sizes="(max-width: 1377px) 100vw, 1377px" />
<blockquote>
Fig 1. Count of climate-related keywords mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts; ‘Carbon Emission’, ‘Carbon Footprint’, ‘Carbon Neutral’, ‘CDP’, ‘Climate Change’, ‘Net Zero’, ‘TCFD’. Source: InsigAI
</blockquote>
Viewing disclosures through a social lens on workforce issues is also revealing, showing a steady upwards trend across most of the peer group, with boohoo lagging behind the curve in both timing and volume of disclosures.  That’s not to say it’s too late for an ESG scandal ridden company to prove their ESG credentials and be part of the sustainability transition, but an investor may want to benchmark them to best practice as part of their decision to engage.
<img class="alignnone wp-image-2568 size-full" src="https://insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png" alt="" width="1377" height="689" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=200%2C100&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=300%2C150&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=400%2C200&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=600%2C300&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=768%2C384&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=800%2C400&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=1024%2C512&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?resize=1200%2C600&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/04/climate-change-disclosures.png?fit=1377%2C689&amp;ssl=1 1377w" sizes="(max-width: 1377px) 100vw, 1377px" />
<blockquote>
Fig 2.  Count of workforce-related keywords mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts; ‘Diversity’, ‘Gender’, ‘Human Rights’, ‘Modern Slavery’, ‘Pay Gap’. Source: InsigAI
</blockquote>
High level views like this that focus on material issues are a key starting point for a deep dive into quantitative and qualitative disclosures.  And when it comes to detail and due diligence, technology can support human analysis further.  Natural language processing (NLP) as a form of machine learning is emerging as a powerful tool to help filter through the noise of reports, surface evidence of meaningful disclosures and help investors hold companies to account year on year.
In the evolution of ESG best practice, the benefit of access to powerful technology directly by decision-makers is playing a part.  Machine learning and AI are proving they can support the transparency and scrutiny required to drive a rise in ESG standards across the board, and help hold a spotlight to hard-to-assess industries such as fast fashion.

machine-learning-can-help-investors-tacklet-fast-fashing-esg-issues

Machine learning can help investors tackle fast fashion ESG issues

esg-scoring-systems-are-giving-way-to-intelligent-analysis-of-disclosures

ESG scoring systems are giving way to intelligent analysis of disclosures

When I co-founded Insig AI three years ago with my colleague Warren Pearson, we did so with a purpose to transform the asset management industry through AI and machine learning. This mission was based on multiple conversations we had with industry leaders where it became apparent to us that while many wanted to get the benefits of machine learning and AI, they simply did not know how to go about it.
Through companies like Citadel and BlackRock, we have seen just how successful machine learning and AI can be. Few in the industry, however, have their resources – to spend big and build teams who can create bespoke in-house solutions – and the challenge for them becomes how to keep up. This is where Insig AI’s suite of “out of the box” products come in. Our solutions allow investment professionals to see, interact, and experience their data in a way they never have before.
A core way in which we do this is through data visualisation. When we take a client’s data and apply our machine learning and AI tools to it, we are giving them the ability to interpret it through multiple lenses in a fraction of the time traditional tools would take. The success our clients are enjoying shows just how beneficial these solutions are. One client’s fund has so far outperformed by 30 percentage points, while another has experienced a 25% reduction in operational costs.
It’s important to note that these results are not reached by machine learning and AI alone, but rather augmenting the portfolio managers’ decision-making ability and judgement. The tools generate insights that may otherwise have gone unseen. Ultimately, however, investment decisions continue to remain with the portfolio manager. One of the major benefits AI and machine learning products gives to asset managers is speed in processing and investigating data. When once testing a hypothesis would once have taken five days to investigate can now be done in a matter of minutes.
As many asset managers know, their first instincts on an investment can often be far from the mark. Machine learning and AI products expedite this discovery process, so when they are wrong – they know it fast. This allows the user to move on to their next hypothesis rapidly. The success here lies in being able to investigate multiple theories at once, in order to find and validate the right path forward. During the discovery process, we also know that when weighing a decision, it is equally important to know the data and decision-making behind it. Therefore transparency is a core pillar of our company and our products are not “black boxes” – you can drill down every step of the way.
As new trends and industry sentiments emerge the ability to be flexible will be as important as ever. As the global asset management spend on AI is set to reach $8.3bn in the next five years, technology will continue to play an ever-increasing importance.
To this end, we are already developing a product called Insig ESG, set to launch in the third quarter of this year, which will develop our clients ESG strategy and execution. While our future products pipeline will look to continue to enhance the ability to mitigate risk, reduce costs, deliver better returns, and deliver products and services more efficiently. The transformative ability of AI and machine learning on Asset Management is no longer the domain of a few, and Insig AI is the perfect partner to help deliver it, now and in the future.

ai-offers-unparalled-insights-to-the-asset-management-industry

AI offers unparalleled insights to the asset management industry

As the demand for sustainable investing continues its rapid growth, so too does the importance of reliable ESG credentials and analysis. The uncomfortable truth is that not all ethical investments are what they appear to be, and it’s easy to lose sight of key ESG issues in decision-making. The challenge for investment managers, therefore, becomes how to truly see the wood for the trees.
At its heart, the rise of ESG is a response to demand for companies to do what’s right. If this seems hard to pin down, it is because it is – what’s right for one corporate now may not be right for others in the future. To find consensus and gain perspective on the big picture, while also gathering the detail that really matters, are real challenges that threaten ESG’s credibility. They are also where AI and machine learning can play a crucial role.
Traditionally, ESG credentials are based on the findings from a human analyst, meaning they can be skewed by bias and assumptions. These findings are then prone to being highly processed by obscure methodologies to deal with the challenge of aggregating disparate data. Meanwhile, to add a further challenge, the goalposts of what matters to whom are constantly shifting.
Furthermore, investment analysts are facing an ever-increasing amount of complex data and information relating to ESG. Gathering reliable data points, unpicking methodologies and reading between the lines of Sustainability Reports can seem like a monumental task. Going deep is one thing, but after all that hard work, data can still end up meaningless unless seen in the wider context of a decent benchmark.
This is where the ratings agencies such as MSCI and Sustainalytics stepped in to do a lot of the hard work of research, input, benchmarking and scoring. Asset Managers breathed a sigh of relief. But it soon emerged that a disparity in approaches was resulting in a lack of consensus and transparency in the outputs.
This was recently highlighted by analysis by State Street Global Advisors, as part of an 18-month due diligence process which looked at 30 ESG data providers. It found that ratings aligned on only half of the companies covered. As SSGA noted; “in choosing a particular provider, investors are, in effect, aligning themselves with that company’s ESG investment philosophy.” One thing has become clear: a simple ESG rating is no longer enough for asset managers to hang their hats on; proprietary analysis is still crucial in the quest for evidence and transparency to underpin their confidence in ESG investment decisions.
This is where AI and machine learning can add real value to the ESG analyst’s task as well as the investment manager’s confidence. When it comes to drilling deep into a company’s ESG disclosures, for example, machine learning can do in minutes what would take a team of people days. And as for going wide, AI is perfectly equipped to filter, organise and benchmark huge volumes of diverse data in ways an analyst can then digest and explore. AI can do a lot of the hard work, without introducing the same human bias and assumptions that can creep into aggregation methodologies.
Natural Language Processing (NLP) is a great example of AI’s ability to go wide, by harnessing the content of hundreds of thousands of sources for relevant data. NLP can find and contextualise text that is not in a structured format and make sense of it. The result is that relevant insights from live, ‘real world’ sources can be organised, ready for analysis alongside more formally reported information. This provides a new level of context and richness to the picture of an ESG investment opportunity. NLP can also act like an ‘ear to the ground’ in a shifting landscape of ESG materiality and risk, identifying and highlighting emerging trends that may affect a company – beyond what they’ve reported or are even aware of.
While we are still in the early stages of AI’s application to ESG, some key factors are set to rapidly accelerate its use. On climate change, for instance, it’s no huge surprise that some companies have been getting away with greenwashing – for example talking about ‘Net Zero’ without any strategy to get there. AI has the ability to help bring this to light, and public, political and regulatory scrutiny is on the rise; as Raba Foroohar recently pointed out in the FT “individual companies are being held explicitly responsible for the risks of global warming”.
These increasing standards and scrutiny are both overdue and necessary, making it an exciting time in the world of ESG. But the speed at which things are moving presents challenges for both companies and investors, and there’s a risk that transparency, evidence and ultimately credibility get compromised. As a partner to human decision-making in a complex and at times perplexing world, the power of AI is starting to be harnessed for ESG; if done right, it will add both quality and that all-important sense of perspective.

seeing-the-wood-for-the-trees-with-the-help-of-ai

Seeing the wood for the trees with the help of AI

The recent high-profile acquisition by Philip Morris International (PMI) of Vectura, a health tech company that produces inhalers, has been dominating headlines. Heated debate has been spurred as to whether such an apparent conflict of interest should be allowed, or whether it’s a sign that ESG momentum is biting and business is being forced to pivot for the better.
On one side, there is a sense of moral outrage that a company should profit from solutions to problems it contributed towards. There are also serious concerns expressed by health academics who believe the acquisition will allow Big Tobacco to wield undue influence on UK health policy. This response seems to boil down to a question of whether businesses with damaging legacies deserve to thrive now our eyes are open to their impact – are they beyond becoming sustainable.
On the other side of the coin is the more pragmatic view that these influential companies have a role to play in the transition away from harmful practices. This side argues that reactive divestment in bad news assets is not the answer; washing your hands of poor ESG stocks merely allows them to be snapped up by the less discerning. Ultimately, this leads to our environment and society being no better off.
So while PMI seems to have pulled off a highly strategic move that’s probably key to its survival and the whole thing makes us deeply uncomfortable, it begs the question: Does the motivation really matter if the overall outcome is positive? Sustainability is fundamentally about the long term view – how will we look back at this moment in its evolution?
It’s impossible to say, but critically, the first part of answering this question is to ensure that the outcome is positive and to do that, companies need to be held to account in fulfilling their ESG promises. It is the same logic when it comes to greenwashing. If a company sets long-term targets for emissions, ethics and equality but nobody checks in on whether they’re delivered upon, the company enjoys all the benefits of sounding virtuous without ever having to deliver or face the consequences.
Part of the problem is that littering reports with generic wording, vague long-term targets and catch-all terms has become dangerously easy. So much so that the EU has introduced new anti-greenwash rules forcing the asset management industry to categorise and qualify their ESG claims. And the EU is not alone, with the UK Competition and Markets Authority (CMA) recently putting business “on notice”. In a release published just last month, the CMA warned businesses they have until the New Year to make their environmental claims comply with the law. Further, it stated it would be carrying out a full review of misleading green claims early next year and “stands ready to take action against offending firms”.
Some see the greenwashing trend as threatening the whole ESG movement; others see it as teething problems that won’t hold back the wave of change. Again, what’s key to getting it right is to enforce accountability and responsibility. Consumer pressure, regulation and convergence of standards clearly have a role to play, but it’s still a tricky space for discerning investors. Assessing credibility and tracking delivery to targets is made challenging by the sheer volume of reporting resources published, and genuine disclosures can get obscured in confusion.
This is where we are starting to see the role technology can play in holding companies to account to having the impact they intend. AI and machine learning are emerging as practical tools that investment professionals can use to identify if corporate promises are being acted upon. Thoughtfully trained algorithms can do a lot of the heavy lifting of organising and processing vast volumes of corporate disclosures so that a researcher can more effectively and efficiently home in on metrics, read between the lines where things aren’t what they seem, and determine from the evidence whether words and actions match where it matters.
We are still in the early days of ESG and, as the PMI dilemma highlights, there are conflicts, trade-offs and strong arguments for principles and pragmatism that can pull in different directions. However, as the complexity we need to grapple with increases, so to do the tools and solutions at our disposal. It is my belief that ESG will create positive change over the long term, and AI and machine learning will play a valuable role in supporting us on the journey.

Insights