INSIGHTS Research

Systematic credit has drawn more limelight over the years as electronic trading of various credit instruments gained in volume and share of the market. The increased availability of high-quality data and growth of liquidity has made it possible for us at Arabesque AI to consider an expansion into credit instruments.

Both the valuations of corporate credit and equity are dependent on the overall health of a company. Therefore, they share many performance drivers, which we have already implemented into our equity analytical models. This makes it an interesting use-case for us to investigate our models’ transfer-learning capabilities. Through various proofs-of-concept over the past year, we have demonstrated the ability to build analytical models for corporate credit bonds.

Naivety and Challenges

From a theoretical, machine-learning perspective, where we have built a strong pipeline of models for our equity predictions, the application to the credit universe is a simple problem. It can be solved by creating a new dataset and a new label which we then use to train and evaluate a baseline model from our existing pipeline. However, the reality is a lot more challenging, due to 1) heterogeneity of data in clustering and across time, 2) high dataset imbalances, 3) trustworthiness of data and last, but not least, 4) entity and exchange mapping challenges. Let’s briefly look at each of these challenges.

Heterogeneity of data: When we consider equities, we can naively group them by the geographies they trade in, the sectors they conduct business in, etc. Ultimately, most of these instruments are non-preferential shares, otherwise known as common shares. Hence, they are comparable in a way. Corporate credit is awash with little details that makes it hard to compare. Some bonds are callable or puttable, which gives either the issuer the right to redeem the bond before the maturity date, or it gives the holder the right to demand the paying back of the principal amount before the maturity date. In stocks, options are separate financial products and therefore don’t need to be considered in pure stock-price forecasting. Further, the maturity dates of bonds are not aligned, one company can issue various types of bonds, such as secured bonds or convertibles and, to make it even more complicated, some European bonds are eligible for the ECB’s asset purchase programme. Hence, the grouping of “similar assets” for training is a harder task in bonds if one wishes to adjust for all these granularities.

To make matters worse, equities can almost always be assumed to be perpetually existing unless in the case of corporate events. On the other hand, bonds can almost always be assumed to expire at some point in time, except in the occasional case of perpetual bonds. This means that the universe refresh rate is exceedingly high. This presents many challenges for machine learning algorithms, not least limited to inconsistent dataset sizes or the unknown extent of survivorship bias vs. maturity effect. Datasets, therefore, need to be asset-agnostic to a certain degree and carefully constructed to maintain comparability.

High dataset imbalance: In equities we can either frame the problem as a price prediction or a returns prediction, either of which can be calculated by the prices of the equities (split/dividend-adjusted, which are still just intrinsic datapoints of the equities). In bonds, we can either frame the problem as a price prediction or a credit spread prediction. The former is a bond datapoint and the latter a combination of the bond yield versus the risk-free rate, typically a US Treasury bond. Here, we are implicitly predicting for “interactions” between two different assets— the bond and the risk-free rate. Moreover, when we train for a target label of a minimum spread widening/narrowing, we find stark class imbalances. These are more pronounced than the same setting in equities of minimum return requirements. The imbalance often calls for the need of readjusting the loss function where for trading cost reasons we would value one class over the other. For example, it is easier going long on a bond than to short a bond compared to the equities world.

Trustworthiness of data: The challenges above are compounded by the deteriorating quality of data in bonds of lesser-known issuing entities or lower credit ratings. In a trading landscape where OTC trading still contributes a significant share of the liquidity, bid/ask data and volume recorded from electronic markets are sometimes misleading, and worse, untradeable. This not only influences the training of the models but also the executability of credit trading signals. Often, this means sanity checking the data manually. The trustworthiness of data also feeds back to design on the type of trading decision horizons and therefore the target labels for the credit model.

Mapping of entities: Many commercial data providers carry their own asset mapping IDs. As bonds are issued by firms that, most of the time, also have issued their own shares means that we have an incentive to link equity IDs to the bond IDs. The mapping is important for understanding where the bonds lie on the capital structure and what credit risks they bear. This is less of a problem when one sources data from the same data provider but quickly becomes a tedious task when mapping across databases.

Measuring the quality of a systematic credit model

For any system, there must be a way to conduct quality checks. For machine learning systems, we can rely on metrics such as accuracy, error sizes, f1-scores etc. However, these might not be sufficient for models that produce forecasts for more illiquid holdings. On longer holding periods, it is important to understand the models from a fundamental analyst’s perspective. This means 1) understanding the behaviour of different machine learning systems and algorithms, 2) understanding the contribution and importance of different input features, and 3) understanding the variability of model outputs.

Model response to datasets: We know that different algorithms respond differently to the same dataset. Training an ARMA model will yield different outcomes as a Gaussian process model. Therefore, we need to monitor the performance of each model for the same dataset on their out-of-sample prediction power. Given known issues with input data and potential clustering of erroneous data, it is also important to understand how the algorithms respond to corrupted data at various segments of the datasets, i.e., response to adversarial attacks. As different models have different data requirements, i.e., i.i.d. variables for some statistical models, and large enough datasets for neural nets, we also investigate the models’ performance when varying sizes of datasets. However, this sometimes results in overgeneralizing and glossing over key differentiating features of bonds. Understanding these aspects is key to choosing models given our aforementioned challenges of persistently wide datasets in credit space.

Feature importance: As we vary the models, the large number of data points we feed into the models makes it hard to differentiate which really contain information, and which are simply noise. We can select features by comprehensively searching through the perturbation of features to identify gains in e.g., accuracy. But this is extremely computationally expensive and only works for one instance of the {model, dataset} set when we could possibly have multiple datasets over the years and different clusters. We can map the feature importance easily when using an XGBoost model through LIME/SHAP algorithms, but these are not necessarily applicable to the other models; the same goes for statistical tests on model coefficients. A hack is to combine a leave-one-out algorithm with a blanket blackbox model representing the entire system of models to map from a subset of features to our produced signals.

Variability of model outputs: Models produce signals that can change as fickle as I change my mind when choosing flavours in an ice cream parlour. A common way to deal with this is to smooth signals over time through moving averages. For systematic credit strategies, however, we need to intuitively understand the fickle signals – if we smoothen the signals, surely that means we cannot be that confident about the models’ decisions? To deal with the volatile signals, we can look at measuring the uncertainty of predictions via inductive conformal prediction which also nicely avoids the need to consistently retrain models.

About Arabesque AI

​Arabesque AI was founded in 2019 as part of the Arabesque Group. We developed our proprietary Artificial Intelligence Engine to forecast stock prices globally on a daily basis. On top of our AI, we built and launched AutoCIO, a platform that creates bespoke investment strategies. Using AI and Big Data, AutoCIO offers hyper-customization, enabling investors to align their investment and sustainability criteria. At Arabesque, AI is not only a buzzword. We advise over $450mn on our platform, proving that AI is ready to be used in practice.

INSIGHTS News

Arabesque recently created an AI and data engineering centre based in Singapore. The centre is supported under the Financial Sector Technology & Innovation – Artificial Intelligence & Data Analytics (FSTI – AIDA) scheme, which aims to strengthen the AIDA ecosystem in the Singapore financial sector. The FSTI – AIDA scheme is funded by the Financial Sector Development Fund, administered by the Monetary Authority of Singapore. Arabesque team members work in close collaboration with the wider Arabesque team, primarily in London, but also in Tokyo, New Delhi, Frankfurt and Boston. 

A key differentiator of the centre is its engineering-first approach. This has a number of benefits; for example: we can quickly experiment and iterate our ideas; our work can be tested at-scale; and we can move our code to production quickly. We continue to maintain our strong engineering partnership with GCP, enabling us to remain at the cutting-edge. See a recent example of our outreach with Google on March 11th here. 

Team members based in Singapore support the production of our key deliverable, AutoCIO. AutoCIO is an easy-to-use platform that builds customisable investment strategies. For example, by using AutoCIO, investment strategies can be customised to target certain CO2 emissions or certain gender diversity levels. The platform is utilised by Arabesque’s clients- such as DWS, one of the world’s leading asset managers, and BIMB Investment, a Shariah-ESG investment management company in Malaysia.  

Our engineering centre works on specific KPIs as indicated in our press release, and broadly these cover: 

1. Implementing NLP data to our financial Knowledge Graph; 
2. An AI Financial Analyst, creating new approaches for financial modelling and analysis; and 
3. Understanding data bias with application-to-transfer learning 

1. Implementing NLP data to our financial Knowledge Graph 

Problem: The growth of structured data (like quarterly revenues or sales) and unstructured data (like news or social-media) is problematic if we do not have the appropriate tools to meaningfully organise the data. Such diverse sources of data (and particularly unstructured data) are often rich in interconnectedness despite their apparent heterogeneity. Such interconnectedness is often obscured in traditional database structures, thereby limiting the value and AI-based insights that these datasets may otherwise provide. 
 
In order to address the problem statement above, the project has three key areas: 

1. The Knowledge Graph as a means to organise interconnected data. 
2. The tools to traverse the graph, extracting relevant data and study the correlations of entities in the Knowledge Graph. 
3. The Knowledge Graph being used to train new AI predictive models.

2. An AI Financial Analyst, creating new approaches for financial modelling and analysis 

Problem: Financial analysts typically devote a substantial amount of their time pouring over financial statements, industry reports etc. in order to build complex models for the purpose of corporate valuation, or the prediction of company financials. Aside from the substantial cost of human resource, the absence of modern data-driven AI approaches can ultimately impact the accuracy and integrity of the financial analyst’s output.  
 
Our approach implements a general machine learning approach that can enable a more  
accurate and holistic approach to the work typically carried out by a financial analyst targeting the following areas: 

1. DCF (discounted cash flow) predictions, including company/project valuation. 
2. The inclusion of ESG data within DCF approaches.  
3. Company financials predictions, e.g. quarterly sales, including fraud detection.  
4. Event analyses, e.g. Merger likelihoods; Post-IPO stock price analyses; Supply chain impact analyses.  

3. Understanding data bias with application to transfer learning 

Problem: High-quality, labelled datasets are difficult to obtain or produce because of the large amount of time and effort required to label such datasets. Furthermore, these datasets are often subject to various forms of data bias, thereby hindering trust with a machine learning model using such data. 

In order to address the problem statement above, this project has three key areas: 

1. Data sourcing: To deliver datasets of high quality and quantity suitable for financial machine learning. 
2. Data biases: To develop automated tools and systems for capturing and identifying bias in datasets. 
3. Transfer learning: To make advances in the area of transfer learning.  In particular, applying the knowledge obtained from one market to other markets for applications such as making investment predictions. 

The current focus of the centre is a reimaging of our data processing pipelines to maximise our ability to onboard and process our big data sets. Our current job roles reflect this, and we aim to add additional roles soon. We look forward to continuing to expand our team and leading this initiative to provide innovative financial research in the APAC region.  

INSIGHTS News

This article was originally published by International Financing Review (Investors seek raw ESG data to power up by Tessa Walsh)
 
ESG is moving into a new phase focused on delivering on net zero commitments that will reshape the provision and collection of ESG data, according to Daniel Klier, president of ESG data investment research and asset manager Arabesque.

After last year’s UN COP26 meeting, investors are moving away from single indicators, such as ESG scores or ratings, and are looking for forward-looking data and flexible raw data feeds to power their own work on investment decisions, risk management and modelling and to meet growing regulatory requirements.

“If you want to move ESG out of the ethical corner into the core of the investment process, you need to put your data at the core of the investment process,” Klier said.

“Investors want to use ESG data to address the use case that they have, rather than be told their ESG score by a data provider.”

Klier was formerly HSBC’s global head of sustainable finance and joined Arabesque last June. Arabesque provides technology for sustainable finance and offers ESG investment strategies, data and insights for financial decision-making using artificial intelligence technology.

The firm’s asset management arm uses mathematical models to target ESG investments and Klier is also CEO of its S-Ray arm, which provides data and ESG metrics to assess the sustainability performance of companies.

Private markets push

Reported data on large, listed companies are backward looking and often inaccurate, and the push of ESG into private markets where data are harder to acquire is requiring a new approach to fill large data gaps.

Arabesque launched ESG Book in December, which is a central digital hub for corporate sustainable information that makes raw data available for free and charges for any analytics created. 

Investors and banks can also invite companies to disclose straight onto ESG Book’s platform to gather necessary data. It has been created with a group of founding partners that includes the IFC, Deutsche Bank and HSBC and lenders can incorporate data disclosure into credit agreements.

“The interesting discussion of the moment is how do you get into private markets and how do you use alternative data sources to turn this into insight,” Klier said.

“People want raw data. There’s so much wealth in unstructured data but people need help to create investment sights that help capital allocation.”

Arabesque has recently announced a partnership that integrates its ESG data products with cloud technology company Snowflake, which will allow clients to import ESG data products into their technology.

This will give access to Arabesque’s full data set in real time, which includes sustainability performance metrics and green revenue data, alignment with Taskforce on Climate-Related Financial Disclosures and Sustainable Finance Disclosure Regulation as well as temperature scores and UN Global Compact scores.

These new data sets are expected to be used to develop new ESG funds that target more specific ESG topics with investment propositions such as climate funds and energy transition funds that will help managers to counter accusations of greenwashing.

INSIGHTS Research

The Task Force on Climate Related Financial Disclosures (TCFD) was established in 2015 by the Financial Stability Board to develop recommendations for more effective climate related disclosures. In 2017, the TCFD published a set of recommendations to guide companies in providing better climate related reporting, which has since become the global standard for climate disclosures. The TCFD Alignment Barometer, delivered through ESG Book, supports corporates and investors in understanding the TCFD recommendations and the reporting landscape.

To read the full article, click here.

INSIGHTS News

2022 has kicked off in earnest with busy schedules and the winter holidays seeming like only a distant memory for many of us. ESG regulators have not made an exception in this regard and started off the year with a range of noteworthy developments and announcements regarding ESG disclosure and transparency rules that will be implemented over the coming year.

EU Taxonomy: will nuclear and natural gas make the cut?

The experts at the EU Platform on Sustainable Finance have published a response to the EU Commission opining that nuclear energy and natural gas “could not be considered sustainable” as per the remit of the Taxonomy and their inclusion could pose “a serious risk … undermining the sustainable Taxonomy framework”. The final shape of the delegated act is still to be determined after a lengthy political process. And the ‘greenness’ of nuclear energy and natural gas is still to be decided. Read more

EBA publishes binding standards on Pillar 3 bank disclosures on ESG risks.

On 24 January, the European Banking Authority published its final draft implementing technical standards (ITS) on Pillar 3 disclosures on ESG risks. The standards put forward comparable disclosures and KPIs, including a green asset ratio (GAR) and a banking book taxonomy alignment ratio (BTAR), as a tool to show how institutions are embedding sustainability considerations in their risk management, business models and strategy and their pathway towards the Paris agreement goals. Once approved by the European Commission, EU banks will have to start making ESG disclosures in 2023, with full phase-in by June 2024. Read more

ESMA Consultation on MIFID II Sustainability Guidelines review

EU securities regulator ESMA has launched a consultation on a requirement for financial firms to collect data regarding clients’ preferences for sustainable investment products and subsequently propose products which meet those preferences. The proposals follow revisions to suitability requirements under the MiFID II regulations. The consultation closes on 27 April 2022. ESMA expects to publish a final report in Q3 2022.  Read more

UK FCA Climate related disclosure regime comes into effect

The rules, which are phased in from January 2022, are closely aligned to the Task Force on Climate-Related Financial Disclosures (TCFD) Recommendations. They require in-scope firms to make annual firm-level disclosures relating to how they take climate-related risks and opportunities into account when managing their investments as well as product-level disclosures. Read more

India set to become the first country to regulate ESG ratings providers

The Indian securities regulator Securities and Exchange Board of India (SEBI) published a set of draft rules on 24 January 2022, according to which providers of ESG ratings and commentary would have to obtain accreditation from SEBI, to be reviewed every two years. The initiative focuses on scrutinizing the processes and policies each data provider follows regarding transparency, methodology and conflict of interest. The deadline to respond to SEBI’s consultation is 10 March. It is likely that other regulators such as the UK’s FCA, ESMA and the European Commission might soon follow suit towards regulating the ESG data market. Read more

US SEC expected to introduce new ESG disclosures in 2022

The US Securities and Exchange Commission (SEC) is expected to propose mandatory ESG-related disclosure rules in early 2022. Even without specific requirements, any ESG-related material impacts should be disclosed under existing SEC rules. Some of the priorities and areas of interest include: disclosure and compliance issues related to investment advisers and fund ESG strategies, greenwashing and proxy voting. Read more

Other news

• The Hong Kong Monetary Authority (HKMA) published the results of its pilot climate risk stress test (CRST). Read more
• Bursa Malaysia announces the enhanced requirements in the Main and ACE Market Listing Requirements (“the Listing Requirements”), aimed at further strengthening board independence, quality, and diversity.  Read more
• International Sustainability Board (ISSB) commences its streamlining of the sustainability disclosure landscape. Read more

 

INSIGHTS Research

Our aim at Arabesque AI is to create customisable, actively managed, AI powered investment portfolios. To do so means it must be able to undertake any mandate and not specialise in one area. This generalisability is a core problem in AI research and has many dimensions; it can represent time, geography, or context. For example, in the time dimension you need your models to continue working from the pre- to a post- pandemic world (and during too). Geographically, you need to achieve performance anywhere from Peru to Russia and contextually, you may to apply to both start-ups and established companies. A generalisable AI can adapt to the environment it is in.

Our AutoCIO platform covers 25,000 stocks, in 60 countries and over 80 exchanges with data spanning back in time across many market regimes. AutoCIO allows you to create hyper-customised strategies and explore potentially millions of configurations depending on your investment aims. Creating such generalisation is a difficult task— but even more so in financial data— so what are some of the obstacles modelers face?

Covariate Shift

One problem which makes generalisation difficult is covariate shift. This is when, although the relationship between inputs and outputs remains the same, the distribution of your inputs changes. Imagine training a facial recognition device on spotless photos of you, perhaps headshots or dating profile pictures. When we attempt to generalise to your face first thing in the morning, pre-coffee, it might fail. Your face has not changed, but the context has, and so previously learned relationships may not hold.

Another example, given in this paper by researchers at Facebook, concerns the classification of images of cows and camels. In your training set the animals are in their natural environments; that is cows are in fields and camels in deserts. When we attempt to generalise to unnatural habitats, perhaps cows on beaches or camels in the park we fail spectacularly.

Cows on a beach may seem farfetched, but to those dealing in real world data far stranger things occur. You need to able to spot opportunities or risks however they are presented. Despite training across as wide array of environments as possible it is infeasible to innumerate all environments that will be encountered. The usual mantra of more data does not necessarily help as there are no guarantees the sample will contain the relevant information. Upon entering a new environment not seen before, there are fundamental limits to letting the ‘data talk’.

Changing Relationships

Covariate shift describes the process of when the context changes, but the input-output relationship does not. A more intractable problem is when the input-output relationship does change as is often the case with financial time series. In facial recognition this is the case of ageing, injury or plastic surgery, your mother may recognise you, but a naïve machine may not. For cows and camels this is perhaps the new breeds of super cows which more resemble Arnold Schwarzenegger than a typical Aberdeen Angus, recognisable to a farmer but not necessarily your classifier. The rules which you previously learned, whatever the context, may no longer hold.

In finance, rules change as markets move through volatility regimes, rate cycles, expansions, moderations, and contractions. However powerful your method is, the data you observe may be just the remnants of forgotten relationships. A seasoned trader who has survived for decades may recognise a market shift better than a machine trained only on recent data. This is the aim for AI also.

A New Approach

A pervasive problem in AI scenarios is lack of understanding of your input data and its context. In the cow and camel example, if say 90% of all pictures were taken in their natural habitat your machine may be able to minimise error by simply classifying anything in a green landscape as a cow and anything in a brown landscape as camel, with 90% accuracy. The problem is that you have not uncovered the causal mechanism that makes a cow a cow, you have identified a spurious correlation that will not generalise. Instead, we want to find robust patterns.

These problems are leading AI researchers to rediscover the power of structured models, driven by domain-specific knowledge. Understanding your data features, their context, structure and meaning can lead to more applicable results. The paper which contains the cow/camel example shows one example of this, an approach they call Invariant Risk Minimisation (IRM). In this approach the data is split into two environments – natural (N) and unnatural (U). Instead of having a model (like the cow-camel example above) that performs with 100% success in N and 0% in U (for a total of 90%) we instead encourage the learning of relationships that are stable across environments. We may end up with an accuracy of only 75% in each of N and U but we have forced the model to focus on those immutable characteristics which make a cow a cow wherever it is – invariance to environment signals that you have found a true causal relationship.

At Arabesque AI we continually research and update our models and when we do, we want to see improvements across all environments, meaning it is more likely we have found true relationships instead of spurious correlations. As a closing example consider another aspect of our business – sustainability. A company that has achieved great success by unsustainable (or simply corrupt) practices is unlikely to continue the performance you see in sample out of sample. However, applies even more as the rules of business change, behaviour that was profitable before may no longer work. Diverse, sustainable, and well-governed companies are better placed to survive across market scenarios, they are more invariant to the environment. Therefore, at Arabesque we believe that carefully constructed AI and sustainability will help to navigate through the environments which lay ahead.

INSIGHTS News

• As part of this strategic partnership, ABG acquires a minority stake in a leading provider in the fields of digitalisation and ESG
• The two companies are pooling their expertise in order to expedite the key concept of sustainability in the real estate sector
• With the recent introduction of the ESG Book, Arabesque Group has set a global standard for ESG data platforms
• ABG plans ‘Manage-to-ESG’ funds

Frankfurt/London, 25 January 2022 – ABG Real Estate Group has entered into a strategic partnership with, and acquired a minority stake in, Arabesque Holding Ltd, a leading international financial technology provider. The two companies are pooling their expertise in order to expedite the key concept of environmental, social and government (“ESG”) aspects in the construction and real estate sector together with the construction company Goldbeck, which has already invested, and to establish objective criteria for sustainable investments. The partnership brings together ABG’s long-standing experience across all segments of the German real estate sector with the international ESG resources offered by Arabesque. Ulrich Höller, Managing Partner at ABG Real Estate Group, will be represented on the Advisory Board of Arabesque Holding. As one of the leading ESG data providers in the financial services sector, Arabesque uses artificial intelligence and Big Data to create transparency regarding the sustainability of businesses. At the end of 2021, for example, Arabesque set a new standard for availability and objectification of corporate sustainability data, through the introduction of ESG Book, a global data platform launched in cooperation with international institutions such as the UN Global Compact.

ABG is a signatory to the UN Principles for Responsible Investment (UN PRI), and a member of numerous initiatives pursuing sustainability in the real estate industry –including ECORE (ESG Circle of Real Estate), the German Society for Sustainable Construction (Deutsche Gesellschaft für nachhaltiges Bauen – “DGNB”), and the German Institute for Urban Architecture (Deutsches Institut für Stadtbaukunst).

Ulrich Höller, Managing Partner at ABG Real Estate Group, said: “Our partnership combines Arabesque’s global access to data, their strategic ESG resources and technological expertise with our real estate know-how, in order to further develop the concept of sustainability in real estate investments. For 2022, we are planning to launch a ‘Manage-to-ESG’ fund. The partnership is a key milestone in ABG’s sustainability strategy – both in our nationwide project development activities and in ABG Capital’s investments.

Georg Kell, Chairman of the Supervisory Board of Arabesque Holding, stated: “Allocating capital to sustainable investments plays a decisive role in the real estate sector. Our new strategic partnership with ABG Real Estate Group is a key milestone for combining ABG’s in-depth knowledge of the real estate sector with Arabesque’s technology-focused approach to sustainability. The joint objective is to develop market-leading ESG solutions for the real estate sector, which make ESG integration transparent and scalable in order to meet fast-growing demand for sustainability in practice.”

INSIGHTS News

• Agreement will enable financial organisations, investors and companies to integrate Arabesque’s suite of market-leading ESG data products and insights solutions with Snowflake’s Data Cloud 
• Arabesque clients will be able to use Snowflake’s platform to integrate ESG metrics, raw data assets and regulatory intelligence into their technology stacks securely and in real-time  
•  New partnership will enable Arabesque to deliver streamlined data solutions globally at scale, with no additional integration  or extract transform load required 
• Announcement follows Arabesque’s recent launch of ESG Book, the new central source for accessible and digital corporate sustainability information  

 21 January, 2022, London – Arabesque has today announced a new partnership with Snowflake, the Data Cloud company, enabling financial institutions and investors to integrate Arabesque’s suite of data assets and insights into their technology stacks securely and in real-time.   
 
The partnership will allow Arabesque to deliver its market-leading solutions at scale through Snowflake’s Data Cloud, which offers a centralised and streamlined data delivery experience with no additional integration or extract transform load required.  
 
Clients will be able to use the Snowflake platform to access Arabesque’s wide range of sustainability metrics and raw data on corporate greenhouse gas (GHG) emissions and green revenues, together with its proprietary regulatory solutions including the SFDR Data Solution and TCFD Alignment Barometer.  
 
The announcement follows the recent launch of ESG Book, Arabesque’s new central source for accessible and digital corporate sustainability information that is supported by a global alliance of leading organisations based on a mission to create ESG data as a public good.  
 
Speaking about today’s announcement, Daniel Klier, President of Arabesque, said:  
 
“Demand for accessible, comparable and real-time sustainability data is growing exponentially, driven by global trends that are changing capital markets. Through this new partnership with Snowflake, we are able to offer live data assets to our clients with near-instant updates, delivering best-in-class data and insights at both speed and scale. With the use of cutting-edge cloud technology together with our new ESG Book platform, we are committed to re-shaping the future of ESG data. 
 
Kieran Kennedy, Head of Data Marketplace at Snowflake, said:  
 
“As sustainability continues to shape capital markets, client needs are growing for customisable, scalable and real-time ESG data sets that can be efficiently integrated into investment strategies and regulatory processes. With an approach based on big data and machine learning, Arabesque’s solutions go hand-in-hand with the philosophy of Snowflake, and through this partnership we can deliver a powerful customer offering.” 
 
 
 

INSIGHTS News

As we take stock of the major developments in the world of ESG that took place in 2021 and pen down New Year resolutions for the year ahead, below is a summary of the main announcements around the globe during the last month of 2021. These include the release of IPSF’s Common Ground Taxonomy, UK’s FCA push for climate-related financial disclosures, and regulatory advancements on the “Social” dimension of ESG from the US and Netherlands. 

International Platform on Sustainable Finance releases the Common Ground Taxonomy

• The International Platform on Sustainable Finance (IPSF), which was jointly launched by economies including China and the European Union, released the Common Ground Taxonomy focusing on Climate Change Mitigation (CGT). The CGT includes a list of economic activities that are recognized both by China’s and EU’ green taxonomies and have substantial contribution to climate change mitigation. Read more

Financial Conduct Authority publishes rules for climate-related financial disclosures

• The Financial Conduct Authority (FCA) of United Kingdom published rules requiring asset managers, insurers and FCA-regulated pension providers to make climate-related disclosures consistent with the recommendations of the Taskforce on Climate-related Financial Disclosures (TCFD). Read more

AMF/ACPR report on the monitoring and evaluation of climate-related commitments of financial institutions in France

• French Regulators AMF and ACPR released a report on the monitoring and evaluation of climate-related commitments of financial institutions. After publishing a pre-report in October 2021 on fossil fuel sectoral policies and initial estimates of the exposure of financial actors to these sectors, the two authorities also examine in this final report shareholder engagement policies and the role of collective initiatives to which financial institutions adhere, in particular for their “net zero” commitments. The difficulties identified in the assessment of asset managers’ exposure to the oil and gas sector are also outlined. Read more

Brazilian Securities Commission Establishes ESG Information Disclosure Criteria for Listed Companies

• The Brazilian Securities Commission (CVM) issued, on December 22, 2021, CVM Resolution No. 59, which establishes criteria and requirements for the disclosure of information on environmental, social and governance aspects, which was previously a mere deliberation of issuers to attract investors engaged in ESG aspects. Read more

EU’s proposed mandatory Human Rights and Environmental Due Diligence Law further delayed 

• The European Commission has indefinitely postponed directive on human rights and environmental due diligence (HREDD) – more than 150 days after it was first expected to be published. While the reason for the delay is unclear, 47 civil society organizations have penned an open letter seeking “full transparency on the reasons for the delay and on the decision-making process going forwards.” Read more

EU Taxonomy Update: natural gas and nuclear energy in the spotlight

• On 31 December, the EU Commission sent to the Platform on Sustainable Finance and to the Member States Expert Group on Sustainable Finance the long-awaited draft Complementary Delegated Act detailing the technical screening criteria for qualifying natural gas and nuclear energy as sustainable in the framework of the EU Taxonomy. Under the proposal, gas and nuclear power generation would be labelled green on the grounds that they are “transitional” activities – defined as those that are not fully sustainable, but which have emissions below industry average and do not lock in polluting assets. Read more

The FSC and the Korea Exchange launch ESG Information Platform

• Korea’s Financial Services Commission and the Korea Exchange launched an integrated environmental, social and governance (ESG) information platform on December 20, providing a convenient one-stop information service on ESG-related information of listed companies for investors and the public. Read more

Other News

• European Commission publishes FAQ on Article 8 Disclosures Delegated Act: The EC released Frequently Asked Questions (FAQs) document to provide implementation guidance on the content of the Disclosures Delegated Act under Article 8 of EU Taxonomy Regulation (‘Disclosures Delegated Act’). Read more
• The EU’s pensions and insurance regulator EIOPA, announced its sustainable finance activities for the coming three years, aimed at ensuring the integration of sustainability risks into the risk management practices of insurers, re-insurers and occupational pension funds. Read more
• The Netherlands announced plans to introduce mandatory human rights and environmental due diligence (HREDD) legislation at a national level. Read more
• The United States House Representatives passed the ‘Uyghur Forced Labor Prevention Act’. Read more
• The Stock Exchange of Hong Kong Limited (HKEX) published amended Corporate Governance Code (the Code) and Listing Rules. The Revised CG Code and Listing Rules came into effect on 1 January 2022. Read more
• The Singapore Exchange introduced mandatory climate, board diversity disclosures. Read more
  

 

INSIGHTS Research

The COP26 summit is behind us, and although the agreements made by the 196 countries nudged the world closer to a net-zero pathway, there is still a mountain to climb. The Glasgow Climate Pact calls on governments to “Accelerate the development, deployment and dissemination of technologies, and the adoption of policies, to transition towards low-emission energy system”, including “accelerating efforts towards the phasedown of unabated coal power and phase-out of inefficient fossil fuel subsidies”. Greenhouse Gas (GHG) emissions need to fall by 45% compared with 2010 levels by 2030 if the world is to stay on track to reach net-zero by around mid-century. The current trajectory, however, is estimated to be 13.7% above the 2010 level in 2030. The challenge is stark.

This article will outline how to build robust and effective climate pathway strategies using (imperfect) ESG data, analytics, and create technology-generating active market returns in our collective race to Net-Zero. Quite simply, there is no time to wait. 

To read the full article, click here.