In recent years, the demand for quant-based investment products by professional investors has increased at a stellar rate. Today, the product range is diverse with products related to risk parity, smart beta, and factors making their way into the investor lexicon — even at the retail level.
In this blog post, Andrew Perrins, CEO of Savvy Investor, examines the evolution of the quantitative landscape for the modern quant professional with reference to trend following, factor investing and emerging markets. Savvy Investor www.savvyinvestor.net is the world’s leading knowledge network for institutional investors, aggregating investment white papers from practitioners, asset managers, and academics. Membership is free.
Although quantitative investing has existed in various forms for decades, its evolution has come on leaps and bounds from the days of manual calculations, logarithm tables, and graph paper. As paper and pen gave way to binary and silicon in the 1960s, the quant researcher was able to crunch data at great speed thanks to new computing power and data input from Standard & Poor’s Compustat tapes.
Possessing a mathematical skillset during this period in history wasn’t commonly associated with matters of investment, however. Indeed, many of those considered quants today, would have been engaged in data conundrums such as how to land a man on the moon and how to harness and distribute nuclear power.
"Pursuit of knowledge, understanding and statistical edge are the raison d'être for today’s quant."
For the modern quant attending the Quant Conference, one may argue that the mathematical research of financial markets has its origin in the inspiring story of Edward Thorpe. A pioneer in the research of mathematical games, Thorpe was able to fit a tiny computer in his shoe, which was programmed, not only to calculate the velocity of a roulette ball, but also the likelihood of it landing on a particular area of the wheel. Understandably, his “research”, which gave him an “edge”, made him unwelcome in Las Vegas and he found himself adapting his skills to financial markets some years later.
For the quant professional, Thorpe’s casino game research could be considered as innovative and brilliant. For the layman, his investigation: beautifully rebellious. Yet his pursuit of knowledge, understanding and a statistical edge mirror the raison d'être for today’s quant practitioner.
Inside the Mind of a Quant
Where there are misconceptions from the broad financial community regarding what quants actually do, the essence of current quant thinking and how practitioners approach their craft is well represented by SSGA’s podcast Inside the Mind of a Quant. This podcast offers insights that dispel the myths surrounding supposed quantitative short-termism. The participants highlight that it isn’t all about the short-term exploitation of market inefficiencies. Although high-frequency strategies are certainly employed by some, a myriad of comparatively longer-term, multi-product and multi-timeframe approaches are also commonplace.
As the panel in this podcast discuss, since the nature of product offerings can be determined as much by investor appetite as financial results, strategies can and do differ with reference to investment holding periods and overall time horizon of the strategy. For example, Quality and Value factor strategies have a decidedly longer holding period than Momentum. As a result, drawdown depth, length and pay-off periods can differ markedly.
The participants also discuss how to guard against big data mining and the so called “golden age of quant” where the ease of access to vast swathes of data can have benefits and drawbacks.
This big data phenomenon and its characteristics have also been identified by Ying L. Becker and Marc R. Reinganum from their CFA Institute report. The authors highlight that big data ought to be subject to the four Vs maxim: volume, variety, velocity, and veracity.
"Big data is often discussed in terms of the four Vs: volume, variety, velocity and veracity."
The implications of the four Vs are even more relevant today where the ownership and uniqueness of data can be extremely valuable. Those who have exclusive access to data can devise strategies to benefit from its properties. Moreover, those willing to sell that data can rest safe in the knowledge that they can command a scarcity premium.
It is now conceivable that we reach a point where exclusive access to unique data may devalue certain government statistics. Access to debit or credit card transaction data may one day eliminate the need for some market participants to study government retail sales statistics. No doubt this could be a trend to observe in the future.
As market trends, ebb and flow, so too do the profitability and hence the popularity of certain quantitative strategies. Readers of the Turtles trading story will recall Richard Dennis instructing his protégés on how to implement a channel break-out strategy in a variety of futures markets in the 1980s. This method of trend following was both popular and profitable.
Since this time however, trend following has declined in use. The most recent high-profile embargo on its deployment came last year from Winton, who scaled back their exposure to the strategy from 50% to circa 25% in their main fund.
This trend away from trend following is further illustrated by AQR, who points out in a recent analysis, that the strategy has witnessed a significant drawdown in recent years delivering lower returns in the current decade compared to its multi-decade history. As they detail, it appears that the vast reduction in average size market moves between decades and across global markets has quashed trend following performance exemplifying an industry needing to adapt to a post GFC, volatility suppressed world.
"Trend following’s underperformance in recent years is due to a paucity of large risk-adjusted market moves."
For institutional managers, it can be argued that if trend following has become cyclical, it ought at some point to experience a resurgence in popularity. Factor popularity and other alpha-seeking quant strategies will experience periods of “crowding” and periods of rejection. The “crowding” phenomenon as it pertains to investment factors is analysed by MIT SSGA in a co-authored research piece where a proposal for managing exposure to bubbles is discussed.
Quant in EM
Although quantitative approaches are firmly entrenched in developed markets, emerging markets (EM) are a natural draw for those keen to examine new data sets. It should be noted, however, that not all EM markets exhibit the uniform market structures present in developed markets (DM). As a result, strategies need to be adapted. "The lack of arbitrageurs in EM exacerbates the price moves."
As described by Robeco in their Quant Quarterly there is a lower level of natural liquidity in EM making all aspects of round-turn costs higher than DM. This obviously has an impact on returns and the viability of any quant-based EM strategy. EM equities are also traded by institutions in significant blocks. This is true of Brazil, Mexico, Chile and Colombia where block trading is commonplace - unlike the US or Japan. For quantitative strategists wishing to transact in certain EM markets, either in size and/or relatively frequently, further research may be warranted.
Additional market characteristics to be aware of relate to EM countries that charge stamp duty e.g. South Africa and where capital gains tax (CGT) is levied — India’s CGT being 10%. Finally, since there is sizeable passive EM exposure globally, there can be liquidity issues when stocks are added or deleted from widely followed market indices. During the process of index rebalancing price moves can exacerbate due to the paucity of arbitrageurs in the marketplace.
From the days of Edward Thorpe, it is clear to see that the world of quant is continually evolving. As new strategies and new big data sets arrive, the quantitative practitioner will undoubtedly have their hands full hunting down investible edges. For managers and researchers wishing to stay up-to-date on thought leadership and research in quant and the world of finance, please join us at Savvy Investor. It is a knowledge network for institutional investors, which aggregates the best investment white papers from across the investment industry. Papers are intelligently tagged, categorised and rated, allowing our 40,000 members to quickly and freely search and identify the most popular content on any investment topic.