Trading & Risk

Protecting our investors: A balanced approach to portfolio construction and risk management.


Whilst operating a fundamentally long strategy, our approach to market entry/exit and portfolio construction allow us to optimise for volatility and macro cycles in pursuit of alpha (i.e. improving on a rudimentary buy-and-hold strategy). Front and center are robust and consistent approaches to operational, counterparty and regulatory risk.

In this embryonic market, returns can often be forecast* by numerous factors specifically relevant with regards to cryptoassets. Most notably sentiment such as Google, Twitter and Wikipedia trends are demonstably a proxy for investor attention1,2. Momentum (i.e. prior price movements) exhibits a strong time-series effect that statistically predicts both daily and weekly returns3. To a lesser extent volume is positively correlated with an increase in price and volatility5 significantly negatively affects price.

• Forecast not guaranteed

Portfolio structure

Combining cryptoassets within a portfolio can outperform single cryptoasset investments and portfolios can be optimised for returns by the way they are weighted6 and constructed7. The literature shows that blending three specific factors can improve risk adjusted returns:

►  Initial distribution and rate of inflation for a given cryptoasset
►  Network metrics such as the value of transactions that are conducted on chain
►  Price momentum


We use these factors and other in-house proprietary methods to inform our investment management decisions.
ID Theory’s portfolio construction optimises exposure according to market cycles. The cycles play out at both a macro level with regards to overall investment into the cryptoassets but also at a micro level within different tiers of cryptoassets (our confidence level of a particular project delivering) across all three of our themes.

PASSIVE PORTFOLIO

60%

Passive portfolio distributed through our three themes, we perform strategic, periodic and threshold rebalancing in line with our thesis

ACTIVE PORTFOLIO

40%

Active portfolio to control overall exposure inline with market cycles and event risk where active vs cash holdings is determined by cycle.

Programmatic position entry/exit

This is a highly cyclical market; the size and magnitude of movements in cryptoassets are huge with bear markets resulting in the loss of 60-90% of their value, while bull markets result in x10-100 gains. Each successive bull run went exponentially further than the previous one, and each post-crash low has been a higher low.

ID Theory controls overall exposure to the market inline with the stage of the cycle. We look beyond price to form these opinions and it is evident that these decentralised networks are as healthy as ever as evidenced by numerous network metrics such as on-chain value of transaction, number of new addresses, and number of active addresses, amongst others.

Entry programme for each asset is driven by coin specific metrics, catalyst events and market cycle.

Layered entry and exit programme aimed at smoothing volatility.

Maintenance of low limit orders to catch prevalent corrections and wicks.

Hedging

We are excited about the development of a number of decentralised financial instruments that offer the optionality of reducing exposure, without having to mobilise and sell assets directly. These tools will be utilised inline with our overall exposure management strategy.

References:
[1] Dickerson, Algorithmic Trading of Bitcoin Using Wikipedia and Google Search Volume, 2018
[2] Detzel et al, Bitcoin: Learning, Predictability, and Profitability via Technical Analysis,2018
[3] Liu and Tsyvinski, Risk and Return of Cryptocurrency, 2018
[4] Sovbetov, Factors Influencing Cryptocurrency Prices – Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero, 2018
[5] Osterrieder, The Statistics of Bitcoin and Cryptocurrencies, 2016
[6] Hubrich, An Investigation of Factor Based Investing in the Cryptocurrency Space, 2017
[7] Brauneis and Mestel, Cryptocurrency-Portfolios in a Mean-Variance Framework, 2018