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'Responsible investment needs to develop a futures market for ESG ratings'

Michael Torrance and Marc Lijour think 2020 should be the year for a more dynamic approach to ESG data

Responsible Resolutions: This is the opening article in a series from sustainable finance practitioners about their hopes for the New Year.

2020 should be the year the responsible investment community finally harnesses the predictive power of markets to create better, real-time,  and forward forward-looking ESG data. A pilot study called Sustainability.Exchange – one of nine global projects accepted to the UK Financial Conduct Authority’s (FCA) Green Fintech Challenge – hopes to make it happen, and may one day pave the way to establishing a functioning ESG Futures market. Applied to ESG, a prediction market offers the ability to fill the gaps in current ESG information and generate unique insights on ESG momentum. 

Applied to ESG, a prediction market offers the ability to fill the gaps in current ESG information and generate unique insights on ESG momentum.

Capturing ESG Momentum

There is growing evidence that ESG momentum is a key ingredient of financial outperformance. A 2019 Harvard study found that companies with annual positive change in ESG ratings of more than 10% performed better in financial performance than those with either neutral or negative ESG momentum. 

This is an amazing insight, making a case for the financial materiality of ESG, but it begs the question: how can this momentum be assessed empirically and in a timely enough way to be decision useful?

The challenges are significant. ESG ratings are increasingly sophisticated, but unlike financial markets where robust up-to-the-minute data exists, ESG data is typically updated and published periodically (often annually). Methodologies emphasise integration of historical events and disclosure to assess performance. In this context, real-time and forward looking insights are near impossible to achieve. 

Even with the growth of technological advancements like Artificial Intelligence (AI), leveraged by ESG ratings providers like TruValue Labs, nothing has yet replaced the power of markets to aggregate, analyse and generate ‘decision-useful’ information. 

A Market Driven Approach

Prediction markets may offer a solution, forecasting future events (like published ESG ratings) by offering the opportunity to ‘buy’ and ‘sell’ contracts and reward correct predictions. Predictions from such markets are generated in real-time by market activity, sourcing the wisdom of the crowd as information is processed by highly decentralised participants. 

Markets will be analogous to futures markets, where an ESG rating is substituted for the price of a commodity.

These markets have been leveraged in other settings to make forecasts of all kinds, from election results to the outcomes of business decisions or scientific discoveries. They have also been shown to aggregate information with less bias than traditional forecasting methods such as polls, and predict outcomes more accurately than the analysis of small groups of experts. Famously, prediction markets forecasted delays in the production of the Boeing 787 Dreamliner, diverging from public statements of the company executive. 

Applied to ESG, a prediction market offers the ability to fill the gaps in current ESG information and generate unique insights on ESG momentum. 

Design and Output

Testing this theory is the goal of the Sustainability.Exchange, a blockchain-based prediction market for ESG ratings which gamifies and rewards accurate, forward-looking predictions of ESG performance. 

While the back-end is complex, the basic functionality is simple. The platform will have markets for hundreds of global companies. Each company will have three markets, related to the ‘E’, ‘S’ and ‘G’ ratings, asking the question “Will the [E, S or G] rating for [rated company] be higher or lower than [current predicted rating] on [resolution date]?” Users on the platform will hold market tokens, called ESGX, analogous to arcade tokens or poker chips. They will be able to wager tokens on the ‘higher’ or ‘lower’ predictions, winning tokens if they are right.

The predicted rating will be recalculated continually by the underlying algorithm with every trade – since the act of trading will indicate a directional view for the rating. After a trade is made, the value of that trade will change as the predicted rating moves. Users will also be able to sell a trade back to the market, to realise its current value at any time. This activity too will affect the current predicted rating derived from the market.  

The rating of a leading ratings provider will determine if predictions made by users were indeed correct (winning) or not (losing), verifying the predictions and determining rewards. Markets will be analogous to futures markets, where an ESG rating is substituted for the price of a commodity. Like any market, governance rules will be implemented to address misuse.

Deep analytics on trading activity and predicted ratings could find signals of market sentiment and be compared with current financial data to consider questions of financial materiality. Previously impossible analysis derived from real-time market perspectives should become possible.

Conditional markets can also be created on the platform, asking the market how adoption of a certain sustainability initiative will have an impact ESG performance outcomes, for example. Such markets could benefit both investors and corporates in their ESG decision making, understanding market views on what initiatives to prioritise for maximum impact on performance ratings.

More and more we recognise that ESG has the power to move markets. We want to see if markets have the power to move our understanding of ESG.

Incentivising Participation

There are three models that will be explored for incentivising participation. One is pure gamification. Asset owners or managers keen to encourage integration of ESG and to collect insights from disparate networks of analysts and managers can create can create a programme for insight aggregation through the platform. Anonymised leaderboard functionality will allow for the tracking of token earnings, translating to internal kudos or rewards for strong predictive performance. Access to the platform and use of data could be conditioned on minimum trading activity. A cooperative mindset may arise between investment firms agreeing to share insights and encouraging others to do so, creating a virtuous circle for industry insights. 

Alternatively, the platform could provide actual monetary or other rewards to high individual or team performers – rewarding them for their predictive abilities. Other prediction market platforms like Pynk.io have begun to successfully employ this strategy, funded by the end users of the data being generated. 

Users will not only be human. AI token holders will use machine learning to develop winning strategies. No incentives will be required for these participants. The more users the better the wisdom and insights created by the market. 

The goal of the 2020 pilot will be to test these market solutions ESG challenges. More and more we recognise that ESG has the power to move markets. We want to see if markets have the power to move our understanding of ESG.  The potential is there, the technology exists, it now just needs to be tested. One day, who knows, the Sustainability.Exchange could even evolve into a real ESG futures market. 

Stay tuned.

Michael Torrance is Founder of the Sustainability.Exchange and Chief Sustainability Officer at BMO Financial Group. The opinions expressed in this article are his personal ones, and do not reflect those of BMO. Marc Lijour is Chief Technology Architect of the Sustainability.Exchange and heads Capacity and Innovation Readiness at the Information and Communications Technology Council, a Canadian centre of expertise on the digital economy.

 

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