Crypto hedge funds: Why quantitative crypto funds work

Crypto hedge funds are becoming increasingly popular. In this guest article, expert Marc Bernegger sheds light on why this is – and where the journey is heading.

Marc P. Bernegger is a serial tech entrepreneur. He founded his first internet company in 1999. Since then, he has already recorded two successful exits to listed companies. He started working with Bitcoin in 2012 and is a member of the board of directors of several companies. These include the Crypto Profit Finance Group, the Swiss Blockchain Association, the Crypto Finance Conference St. Moritz and others. Bernegger is also a member of the World Economic Forum’s expert network on blockchain and the digital economy. He also initiated the platform CryptoFund.News with a focus on crypto hedge funds.

Based on the latest annual „Crypto Hedge Fund Report 2020“, published by auditors PricewaterhouseCoopers (PwC) together with Elwood, the most common crypto hedge fund strategy is quantitative (48 per cent of funds). This is followed by discretionary long strategy (19 per cent), discretionary long/short strategy (17 per cent) and multi-strategy (17 per cent).

But why do almost half of all crypto hedge funds worldwide focus on quantitative strategies? There are a number of reasons for this.

Quantitative crypto funds: advantages and features

• Systematic strategies are superior to human decision-making processes in an environment of irrational and volatile markets.
• The market is still dominated by traders who make their trading decisions by observing price movements.
• This increases the strength of trends and favours a quantitative approach based on time series analysis.
  It is important to note that the models used by quantitative funds usually go beyond the datasets of digital assets. As many quantitative crypto fund managers come from the traditional financial world, their strategies are trained in decades of data from traditional asset classes and are thoroughly tested before being applied to the crypto market.
• The amount of information that can be retrieved by analysing digital asset datasets is quite large. This is especially so when on-chain metrics (e.g. transaction values, miner fees et cetera) are taken into account.
• This is because these can be used by quantitative funds to gain some element of predictability compared to relying purely on technical price data.
• In the case of outliers, most quantitative strategies can exploit the short-term inefficiencies of digital assets and actually profit from outliers. The attraction of many quantitative funds is their informational edge in the market and their hedging capabilities, especially in declining markets. As such, outliers present a challenge, but have proven quite profitable for some quantitative funds.
• A long/short approach that follows trends does not need a forecast of (fair) prices of the underlying market.
• Simple and generic approaches seem to work better and more reliably compared to highly complex analytical methods. This is especially true when applied to the young crypto markets.
  Additional filtering methods to eliminate the volatility of the underlying crypto markets dampen the activity and lead to more stable results.