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Quantifying Token Risk on FiRM

Naoufel
Naoufel

Head of Analytics

FiRMtransparencyRiskAnalytics

5 min

Cover Image for Quantifying Token Risk on FiRM

DeFi has revolutionized the traditional financial industry by providing a permissionless and trustless alternative to traditional banking services. With DeFi protocols, users can lend, borrow, trade, and earn interest on their crypto assets without intermediaries. However, the DeFi ecosystem is not without risks, as the market is highly volatile and prone to hacks, scams, and exploits. To manage these risks, DeFi protocols have been developing robust tools and models to assess and manage risks effectively. In this context, Inverse Finance Analytics & Risk Working Groups have developed a comprehensive asset scoring model to evaluate token risk before adding them to its fixed-rate lending market, FiRM. This model is a significant step towards effectively assessing token risk in decentralized finance lending protocols.

Methodology

The Total Asset Score, or “TAS”, considers six essential factors: market capitalization, trading volume, price volatility, project fundamentals, token utility, and token distribution. By assigning weights to each factor, the model provides a comprehensive risk score to rank tokens according to their risk profiles.

  • Market Capitalization Score (MCS): This factor considers the token's market capitalization, with Wrapped Ether (wETH) used as a reference for the maximum market cap. The MCS is calculated using the following formula:

MCS=min(10, (Token Supply * Token Price * 200) /( wETH Supply * wETH price) * 10)

  • Trading Volume Score (TVS): This factor assesses the token's liquidity and overall market interest, with wETH used as a reference for the maximum daily trading volume. The TVS is calculated using the following formula:

TVS =min(10,  (30 Day Avg Token Trading Volume USD * 200 / 30 Day Avg wETH Trading Volume USD) * 10)

  • Price Volatility Score (PVS): This factor examines the token's USD price volatility, with wETH's USD price volatility used as a reference. The PVS is calculated using the following formula:

PVS =min(10, 10 - (Token Log Price Volatility / wETH Log Price Volatility) * 9)

  • Token Distribution Score (TDS): This factor evaluates the token's distribution among its top 50 holders, to better account for the risk tied to smart contracts and large holders’ addresses, with wETH used as a reference for the minimum - or best- Gini coefficient. The TDS is calculated using the following formula:

TDS = min((1- Token Gini Index) * 10 / (1 - wETH Gini Index);10)

  • Project Fundamentals Score (PFS): This factor is a subjective evaluation based on team experience, technology, roadmap, and partnerships. The PFS is calculated as the average of the scores assigned to 13 different criteria.

Subjective evaluation (1-10) based on team experience, technology, and roadmap

  • Token Utility Score (TUS): This factor is also a subjective evaluation based on use cases, functionality, and emissions policy. The TUS is calculated as the average of the scores assigned to four different criteria.

Subjective evaluation (1-10) based on token use cases and functionality

Together, these six factors make up the Total Asset Score (“TAS”).

 TAS = MCS * 0.2 + TVS * 0.15 + PVS *0.15 +TDS * 0.1+PFS*0.2+TUS*0.2

Application:

By using a combination of objective and subjective evaluations, the asset scoring model provides a comprehensive assessment of token risk. This enables Inverse Finance DAO to make informed decisions when adding tokens as collateral to FiRM. Moreover, the simplicity of the model ensures its applicability to a wide range of tokens, considering the diverse types of tokens available in the market (wrapped, staked, pool tokens, etc.). This makes the model particularly useful for DeFi protocols that have to consider a wide range of tokens for use as collateral.

The Total Asset Score is a “TL;DR” of sorts for the Risk Working Group’s risk assessments. While ensuring that only lower-risk tokens are added as collateral to FiRM would limit our growth prospects, this framework will be instrumental in assisting the RWG with making recommendations to match the risk profiles of the various tokens with appropriate launch market parameters (collateral factor, daily borrow limit, supply ceiling, liquidation factor, etc). Our adoption of the framework will help promote stability and security within the protocol, and transparency of our processes with our community and the DeFi ecosystem at large. To date, the RWG has assessed the underlying tokens making up the four live markets (excluding wETH which acts as a benchmark), according to the model and has provided changes to their recommendations summarized in Proposal #108. These tokens score as follows: wETH - N/A, stETH - 9.14, CRV - 7.79, gOHM - 6.95, cvxCRV - 5.20.

Beyond FiRM:

With the DeFi ecosystem continuously expanding, the need for robust and comprehensive tools and models to evaluate token risk becomes even more critical. The DeFi space is dynamic and rapidly evolving, and new tokens with unique use cases and functionalities are constantly being introduced. As such, DeFi protocols must be vigilant in evaluating the risks associated with each token added to their platform.

Moreover, the use of asset scoring models is not limited to DeFi protocols. Investors and traders in the DeFi space can also benefit from these models by using them as a tool to evaluate the risk of tokens they are interested in investing in. This can help them make informed decisions and manage their risk exposure effectively.

Overall, the asset scoring model serves as an excellent example of how DeFi protocols can take a proactive approach to manage risks and promote stability and security within the ecosystem. As the DeFi ecosystem continues to grow, we can expect to see more protocols adopting similar models to evaluate token risk. This will ultimately lead to a more secure and stable DeFi ecosystem, which benefits all participants in the space.

- Co-Authored by Edo from the Risk Working Group


Naoufel
Naoufel

Head of Analytics


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