Downtrending crypto prices amidst expected interest rate hikes has led to many reconsidering their exposure to risk-on assets, especially NFTs.

The following provides a guide for example portfolio allocations under different market conditions along with some additional considerations when balancing your mix of assets.

The NFT space is nascent and hence assessing the correlation of assets can be difficult. The report also considers both tactical and strategic asset allocations in the context of traditional finance and the NFT space.

Risk appetite will vary depending on personal circumstance such as age, health, financial situation or simply general ability to deal with stressful situations. For example, an investor with a family is more likely to have a conservative portfolio compared to a young single investor with fewer responsibilities or commitments.

Those with mortgage payments and larger bills due to financing or lifestyle may need to ensure they have more cash to be able to meet these short term obligations so as not to liquidate their portfolio at a loss.

There is no one-size-fits-all and it is up to the individual to evaluate their own personal context and evaluate what they believe to be an acceptable level of risk. Some guiding questions may include:

• Do I have enough cash to meet my short term obligations as they fall due?

• Will my personal circumstances change in the short term future?

• Have you established what your capacity for loss is? The largest feasible loss without defaulting on obligations.

Some example strategic asset allocations based on the age glide path could include the following mix of different asset classes.

As a caveat, these are illustrative in nature and should not be taken as financial advice.

These age brackets could also be aligned with these example personal circumstances:

It’s important to view your portfolio in terms of risk weighted returns. The Sharpe ratio is good when considering the risk premium of a particular asset or class of assets. It removes the risk free rate and considers the standard deviation of return, or volatility in returns.

A Sharpe ratio in excess of 1 is considered acceptable and above 2 is very good. A ratio below 1 should be avoided as this indicates the return is not worth pursuing for the risk involved.

We can see the historical performance of the three NFT projects below have done extremely well relative to their volatility. BAYC, Doodles and Clone X are all considered blue chips though and are relatively stable for the asset class in comparison to other projects. To caveat, the below benchmarking has been calculated on inception to date historical performance with only several data points so is limited.

Forecasting future returns can also help you predict a shift in your asset allocation over time based on the growth of your investments. These may require adjusting periodically to bring this back in line with the preferred risk tolerance. Bowse.eth discusses an expected returns framework for NFTs in a separate article.

If we plot the number of different asset classes on a risk curve, we can observe that riskier assets will generally provide a higher expected return (on average) compared to low risk products so a portion may need to be rotated into lower risk assets to rebalance the portfolio and maintain the planned allocation.

Crypto and NFTs undoubtedly sit at the tail end of this risk curve, above small cap equities. The allocation by age and circumstances for these asset classes are therefore smaller than the lower risk alternatives.

Concentration risk is a term describing the level of risk in an investor‘s portfolio arising from concentration to a single counter-party, sector or country.

Diversification can help defend against this risk by spreading your investments across several assets or classes that are negatively correlated.

In traditional finance, bonds are generally considered low risk and equities high risk. These assets are relatively negatively correlated and hence are weighted to balance the risk/return of a tailored portfolio.

Many pension funds use age as a means of calculating the appropriate weighting of strategic asset allocation. For instance someone aged 30 may have 30% in bonds and 70% equities. The table illustrates a weighting of bonds/equities based on age and personal risk tolerance (glide path).

This is to account for the longer life expectancy and higher expected return over time. For instance there may be some years that have losses but on average the return will generally be higher, so for a longer period of time this portfolio value will compound at a greater weight.

However, correlation risk still exists despite diversification. Nothing can be perfectly negatively correlated so severity of adverse impacts on certain sectors or assets may also have a systemic risk or knock on effect in those other assets considered to be relatively negatively correlated. Beta measures the movement in an asset relative to the market as a whole. A Beta of more than one suggests that the asset is more sensitive to market news or announcements, vice versa for less than one.

We see NFTs and Crypto as highly sensitive to market news or announcements. Take Elon Musk’s tweets as an example to move the BTC market. Or if an NFT project makes an announcement about utility then generally this causes an influx of assets being acquired for that collection.

This price sensitivity hasn’t deterred well known institutions from seeking or recommending exposure to NFTS. The Singapore public pension fund recently recommended including NFTs as part of a diversified portfolio.

Strategic allocation (SAA) is longer term, usually a more passive form of asset allocation while tactical asset allocation (TAA) is a deviation from this long term allocation and consists of more active investing.

SAA is commensurate to an investor's risk profile and financial goals while TAA is aligned with external factors (short or medium-term expectations for economic conditions, valuations or market cycles). Many use a reference as ‘time in the market’ versus ‘timing the market’.

Example TAA portfolios are:

It’s worth noting that timing the market through TAA can be counterproductive and that academic literature supports an SAA approach with direct cost averaging into the assets, equally weighted with your target allocation.

An example of SAA is:

At times it is better to cut losses for capital preservation.

Opportunity cost needs to be taken into consideration when trading illiquid assets as the time to sell typically takes longer. During this time a more lucrative opportunity may arise that contains more upside potential.

As an example within the NFT space, there was an allowlist period where there was asymmetric upside risk in minting projects and selling post launch into volume. This type of trading yielded better returns than holding larger expensive single assets.

Conversely, the flight to quality or blue chip projects may be preferable in more turbulent markets. We touched on this topic in the April monthly Origins report.

As a community, Origins prides itself on ensuring we have the tools and resources available to support our members through both the good times and the more testing periods.

If you are feeling lost or need someone to discuss your circumstances with please reach out and we will be happy to chat.

If you’re feeling overcommitted to risk on assets based on a model you’re currently using, reduce it a little so you can sleep at night.

Past episodes of sudden declines in market liquidity and sharp asset price declines have often been associated with the loss of investor risk appetite.

This is exacerbated in the NFT space whereby there is no traditional order book to provide liquidity. Floor prices plummet, but sometimes waiting for market sentiment to change and removing emotion can aid a better trading result.

To conclude that in general, investors are usually compensated more for systematic risk. So constructing your own preferred SAA can allow you to revisit your portfolio to align it with your personal risk appetite and theoretically provide a better long term return.

Downtrending crypto prices amidst expected interest rate hikes has led to many reconsidering their exposure to risk-on assets, especially NFTs.

The following provides a guide for example portfolio allocations under different market conditions along with some additional considerations when balancing your mix of assets.

The NFT space is nascent and hence assessing the correlation of assets can be difficult. The report also considers both tactical and strategic asset allocations in the context of traditional finance and the NFT space.

Risk appetite will vary depending on personal circumstance such as age, health, financial situation or simply general ability to deal with stressful situations. For example, an investor with a family is more likely to have a conservative portfolio compared to a young single investor with fewer responsibilities or commitments.

Those with mortgage payments and larger bills due to financing or lifestyle may need to ensure they have more cash to be able to meet these short term obligations so as not to liquidate their portfolio at a loss.

There is no one-size-fits-all and it is up to the individual to evaluate their own personal context and evaluate what they believe to be an acceptable level of risk. Some guiding questions may include:

• Do I have enough cash to meet my short term obligations as they fall due?

• Will my personal circumstances change in the short term future?

• Have you established what your capacity for loss is? The largest feasible loss without defaulting on obligations.

Some example strategic asset allocations based on the age glide path could include the following mix of different asset classes.

As a caveat, these are illustrative in nature and should not be taken as financial advice.

These age brackets could also be aligned with these example personal circumstances:

It’s important to view your portfolio in terms of risk weighted returns. The Sharpe ratio is good when considering the risk premium of a particular asset or class of assets. It removes the risk free rate and considers the standard deviation of return, or volatility in returns.

A Sharpe ratio in excess of 1 is considered acceptable and above 2 is very good. A ratio below 1 should be avoided as this indicates the return is not worth pursuing for the risk involved.

We can see the historical performance of the three NFT projects below have done extremely well relative to their volatility. BAYC, Doodles and Clone X are all considered blue chips though and are relatively stable for the asset class in comparison to other projects. To caveat, the below benchmarking has been calculated on inception to date historical performance with only several data points so is limited.

Forecasting future returns can also help you predict a shift in your asset allocation over time based on the growth of your investments. These may require adjusting periodically to bring this back in line with the preferred risk tolerance. Bowse.eth discusses an expected returns framework for NFTs in a separate article.

If we plot the number of different asset classes on a risk curve, we can observe that riskier assets will generally provide a higher expected return (on average) compared to low risk products so a portion may need to be rotated into lower risk assets to rebalance the portfolio and maintain the planned allocation.

Crypto and NFTs undoubtedly sit at the tail end of this risk curve, above small cap equities. The allocation by age and circumstances for these asset classes are therefore smaller than the lower risk alternatives.

Concentration risk is a term describing the level of risk in an investor‘s portfolio arising from concentration to a single counter-party, sector or country.

Diversification can help defend against this risk by spreading your investments across several assets or classes that are negatively correlated.

In traditional finance, bonds are generally considered low risk and equities high risk. These assets are relatively negatively correlated and hence are weighted to balance the risk/return of a tailored portfolio.

Many pension funds use age as a means of calculating the appropriate weighting of strategic asset allocation. For instance someone aged 30 may have 30% in bonds and 70% equities. The table illustrates a weighting of bonds/equities based on age and personal risk tolerance (glide path).

This is to account for the longer life expectancy and higher expected return over time. For instance there may be some years that have losses but on average the return will generally be higher, so for a longer period of time this portfolio value will compound at a greater weight.

However, correlation risk still exists despite diversification. Nothing can be perfectly negatively correlated so severity of adverse impacts on certain sectors or assets may also have a systemic risk or knock on effect in those other assets considered to be relatively negatively correlated. Beta measures the movement in an asset relative to the market as a whole. A Beta of more than one suggests that the asset is more sensitive to market news or announcements, vice versa for less than one.

We see NFTs and Crypto as highly sensitive to market news or announcements. Take Elon Musk’s tweets as an example to move the BTC market. Or if an NFT project makes an announcement about utility then generally this causes an influx of assets being acquired for that collection.

This price sensitivity hasn’t deterred well known institutions from seeking or recommending exposure to NFTS. The Singapore public pension fund recently recommended including NFTs as part of a diversified portfolio.

Strategic allocation (SAA) is longer term, usually a more passive form of asset allocation while tactical asset allocation (TAA) is a deviation from this long term allocation and consists of more active investing.

SAA is commensurate to an investor's risk profile and financial goals while TAA is aligned with external factors (short or medium-term expectations for economic conditions, valuations or market cycles). Many use a reference as ‘time in the market’ versus ‘timing the market’.

Example TAA portfolios are:

It’s worth noting that timing the market through TAA can be counterproductive and that academic literature supports an SAA approach with direct cost averaging into the assets, equally weighted with your target allocation.

An example of SAA is:

At times it is better to cut losses for capital preservation.

Opportunity cost needs to be taken into consideration when trading illiquid assets as the time to sell typically takes longer. During this time a more lucrative opportunity may arise that contains more upside potential.

As an example within the NFT space, there was an allowlist period where there was asymmetric upside risk in minting projects and selling post launch into volume. This type of trading yielded better returns than holding larger expensive single assets.

Conversely, the flight to quality or blue chip projects may be preferable in more turbulent markets. We touched on this topic in the April monthly Origins report.

As a community, Origins prides itself on ensuring we have the tools and resources available to support our members through both the good times and the more testing periods.

If you are feeling lost or need someone to discuss your circumstances with please reach out and we will be happy to chat.

If you’re feeling overcommitted to risk on assets based on a model you’re currently using, reduce it a little so you can sleep at night.

Past episodes of sudden declines in market liquidity and sharp asset price declines have often been associated with the loss of investor risk appetite.

This is exacerbated in the NFT space whereby there is no traditional order book to provide liquidity. Floor prices plummet, but sometimes waiting for market sentiment to change and removing emotion can aid a better trading result.

To conclude that in general, investors are usually compensated more for systematic risk. So constructing your own preferred SAA can allow you to revisit your portfolio to align it with your personal risk appetite and theoretically provide a better long term return.