Technology Commands The Terms. How Hedge Funds Are Adapting to Modern Realities
Contemporary computing power has improved our daily life, entered many areas of the economy and industry. Hedge funds were no exception. Leading founds employ not only professional managers but also mathematicians and programmers to work with data sets.
Let’s figure out what modern hedge fund is and why they hire math specialists.
How quant hedge funds work
Hedge funds, whose securities are chosen based on numerical data compiled through quantitative analysis, are called quant. Mathematicians solve these tasks. Their research is based on mathematical and statistical models using Data Science, Machine Learning, Big Data approaches.
Specialists in quant hedge funds can explore the same data as in traditional funds, but calculation models are systematic, automated, and cut down human-factor risks. Algorithms and patterns in historical data often dictate investment decisions. Such well-known funds as Renaissance Technologies, Citadel Advisors, Bridgewater Associates, Millennium Management, etc. use quant strategies.
Examples of quantitative analytics:
- Analysis of transactions of payment systems allows specialists to estimate the approximate revenue for the scanned company before this data is presented in the periodic report.
- The number of cars parked near chain stores can be tracked by satellite images. This information can assess the dynamics of changes in the store’s clientele.
Possibilities and risks
Quant hedge fund assets grew rapidly in the first half of the last decade, and their annual return according to PivotPath’s Equity Quant Index was 9–15%.
But the pandemic, unpredictable by mathematical models, has made its adjustments. The return on quant strategies for 2020 was -5.57%, while traditional funds showed growth.
At the moment, the average return for the last three years of quant funds according to PivotPath is 1.3% versus 8.8% of PivotalPath’s Hedge Fund Composite Index, which includes quant too.
Unsuccessful return on investment allows a fresh look at the contemporary approach and defines the risks. The main risks of quant strategies include:
- Misunderstanding of the black box calculation model.
- Lack of human factor. With the increased usage of artificial intelligence, different quantitative funds may inevitably start making the same decisions in unison, which could bring about contagion issues for financial markets.
- Quant strategies are based on historical data and cannot predict black swan issues.
Quant strategies have proven themselves in a growing market, but have failed to demonstrate profitability in unpredictable situations, as it was at the beginning of the Covid-19 pandemic.
Therefore, the question remains open: is it worth trusting the AI and making decisions models, or should “Beware of geeks bearing formulas,” as Buffett said in his 2008 letter to investors.
Key takeaways
Many contemporary well-known hedge funds adopt quant strategies in their work. This approach has worked well in a growing market and showed double-digit interest rates.
At the same time, quant strategies contain risks; hence, managers must validate investment decisions of algorithms.
