DarcMatter recently participated as a Technology Partner of the Battle of The Quants, a conference focused on the various disciplines and issues surrounding the quantitative approach to finance. Hedge funds typically attend the conference to meet investors interested in allocating to quantitative systematic-based strategies, while investors attend to learn about what’s new in the quant space and meet quality fund managers. It’s a fascinating and ever evolving space. The overarching question is whether investors would rather have robots handling their money and investment decisions, or humans with the discretionary ability to trade. Traders oftentimes make decisions, good or bad, based on gut instinct. Obviously, systematic strategies don’t possess such intuition or instinct. However, the development of machine learning, or the ability for machines to learn from data and subsequently adapt to changing environments, is enhancing the attractiveness of the quant space. This is increasingly being applied to the hedge fund space, where trading on algorithms are becoming less static and more adaptive.
So what are the types of strategies utilized by quant hedge funds? Quant hedge funds focus on patterns surrounding the movement of a security regardless of industry, market cap, geography, and other attributes usually considered when an individual invests in a security. Quant strategies are all based on a particular model or algorithm that is back tested through years of historical market data. The underlying model will synthesize the data and then create a portfolio of securities. There is a multitude of quant strategies, ranging from simple fundamental quant strategies to what is referred to as “black-box” strategies. For example, fundamental analysis strategies based on P/E ratios or price to book value relationships can be utilized by quants. On the opposite spectrum are high frequency quant strategies attempting to capture minute mispricing’s in the market at any given millisecond, as well as black-box firms that employ complex algorithms with trading strategies. These pure-quant managers are oftentimes very lucrative, but inherently volatile and opaque.
According to a white paper by investment consulting firm NEPC, fundamental quants, which are quant strategies modeled to incorporate balance sheet and price analyses, returned 3.5% over ten years ending June 2013. Multi-Quant hedge funds incorporating both fundamental and mathematical algorithms returned 6.6% over that same period. Finally, your Pure-Quant play, the models that used increasingly complex mathematical computations, returned 7.3%. However, the Pure-Quant strategy presented the highest standard deviation out of the quant strategies at 5.6%.
So where does this leave allocators who are interested in quant funds? It’s apparent that a lot of conservative allocators are hesitant to put money into quant strategies. For example, one of the panelists at Battle of the Quants was an investor for a family office, who explained that it is very difficult to teach family offices about this brave new world. This particular family became wealthy from real estate. Real estate is what they know, and ultimately it is difficult to diversify into equity, let alone quantitative strategies. A lot of family offices have a very long investment horizon and subsequently take a long time to make decisions. The friction involved with explaining and pinpointing quality quant managers sometimes isn’t worth the effort, particularly with some underlying bad sentiments surrounding quant-based firms and traders. Some of these bad sentiments stem from the 2010 Flash Crash and the bringing to light of certain illicit strategies used by high-frequency traders.
Quant hedge funds can take the benefits of fundamental analysis and leverage that with technical analysis computable only by machines. Computers outpace human counterpoints in leveraging patterns out of complex data sets. As machine learning gets more advanced, correlations in micro and macro events can present themselves within huge data sets and be capitalized by quant processes. Additionally, machines don’t have emotion. They don’t have hunches or behave irrationally after a trading loss. This is a major benefit to quant trading in that the human element of emotion, which often kills returns, is taken out of the equation. Also, the reliance on a few select portfolio managers making major decisions is taken out of the picture. Machines don’t need to take breaks and never get sick! Generally, these qualities are advantageous when dealing with the financial markets. Quant hedge funds can provide enhanced diversification benefits that are uncorrelated to market conditions. Computers are only getting more powerful and more important to the process, so it would be to an investor’s benefit to learn about how these funds operate, and listen to quant traders explain about their methods.
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