The term quantitative investing has received significant airplay in investment contexts in recent years as its popularity among investors has grown. The approach is now used extensively by hedge funds, money managers, ETFs, and even mutual funds, which have typically been associated with traditional stock picking approaches centered on long-term buy and hold strategies. Now, with easily available investment vehicles such as ETFs and mutual funds employing quantitative investing techniques, individual investors can access many of the same strategies as ultra-wealthy accredited investors and major institutional investors such as pension funds and endowments.
In his role as a leading proponent of alternative investment strategies and vehicles, Gareth Henry has had a front row seat to the spread of quantitative investing throughout the investment world. As the former head of investor relations for investment firms Fortress Investment Group and Angelo Gordon, and now working with alternative investment managers, he has had a bird’s eye view of the development of quantitative investing as a practice and of the various strategies adopted by its practitioners.
The Origins of Quantitative Investing
Quantitative investing could be said to go back to the first so-called “technical” traders, who used measures of market movement, rather than purely “fundamental” factors related to a company’s earnings power or industry position, to guide their buy and sell decisions. These early technicians, or technical traders, typically used charts based on indicators such as moving averages, advance/decline lines, and chart formations such as “double bottoms” or “head and shoulders patterns,” etc. to guide their trading.
In the 1980s, these methods were coupled with computerized placing of trades in what became known as “program trading.” Using formulas based upon various market indicators, sophisticated investors were able to execute large trades almost instantaneously. Program trading was thought to be responsible for dramatic price changes in individual securities or even whole markets, as the massive order flow it created rattled markets unused to handling such volumes in a short period of time.
While it is impossible to measure just how profitable program trading was for its early participants, its spread among institutional traders provides a sign of its success in this regard. In a post on the rise of quantitative investing on the popular blogging site Medium, Gareth Henry refers to “the fact that the technique was quickly replicated by others giving rise to an explosion of program trading” as persuasive evidence that the practice was highly profitable. “Invariably, capital is drawn to the source of excess profit,” he added.
While the technique of program trading encountered setbacks along the way, most famously the blowup of hedge fund Long Term Capital in 1998, the practice continued to gain in popularity until it eventually transitioned into the modern strategy known as quantitative investing. What exactly does this technique entail? Henry defines it as “trading strategies based on quantitative analysis. This means creating and relying on mathematical computations and number crunching to identify trading opportunities.”
According to Henry, quantitative analysis (QA) analyzes human behavior utilizing “mathematical and statistical modeling, measurement, and research.” The goal of this analysis, as he sees it, is to enable those using it to take into account a wide variety of factors, such as the prices of assets, trading volumes and the like as well as the impact of events in the real world, on asset prices. This modern version of program trading is currently in wide use by financial institutions and hedge funds. Transactions used to implement quantitative investing strategies are generally sizable, involving buying and selling hundreds of thousands of shares at a time.
Explaining the Spread of Quantitative Investing
A major factor in the spread of quantitative investing has been the tremendous increase in computing power in recent decades. Enhancements in computer power have enabled quantitative traders, often called “quants,” to react with lightning-speed to any tradeable opportunities identified by the computer algorithms they use to try and find an edge in the market.
Quants look for a trading edge using quantitative techniques to analyze market behavior during past events as a way of projecting its behavior during similar current or future events. This type of quantitative analysis can be applied to any subject which can be expressed using numbers. As a result, QA is used in a variety of fields, including science, sports, and finance. The technique is also employed by governments to evaluate the effects of economic and other policy actions. Central banks especially look to QA to garner insights into the health of a country’s economy.
As quantitative investing has spread among large, institutional investors, individual investors have looked to take advantage of the technique as well. They have done so via apps such as QuantStart and other similar services that provide a panoply of QA services. These include backtesting, risk management, and strategy analysis, providing individual investors the opportunity to pursue approaches based on QA similar to those used by institutional players.
The exact extent of quantitative investing in modern markets is hard to pinpoint. In his blog post on the strategy, Henry cites the work of Alex Foster, VP of Quantiacs, on the subject as follows:
- Approximately 90% of trading volume on public markets in the U.S. is accomplished via quantitative methods.
- Quantitative investing is growing in excess of 10.3%.
- According to the Global Algorithmic Trading Market 2016–2020, Quantitative Finance is a $1 Trillion market.
- Also according to this report, 6 of the 10 best performing hedge funds used quantitative trading methods.
While the rise to prominence of quantitative investing is unquestionable, the reason for its ascent can be a subject of debate among market experts. In this regard, Gareth Henry credits the strategy’s ability to divorce trading decisions from emotion as a powerful factor underlying its popularity. “If emotion is truly the enemy of rational decision-making, then Quantitative Investing is the answer,” Henry writes. “Be it fear or greed or just becoming overwhelmed by mountains of data, emotions serve only to stifle rational thinking and that usually leads to losses. Quantitative trading does not have these problems.”
If emotion is truly the enemy of rational decision-making, then Quantitative Investing is the answer. Be it fear or greed or just becoming overwhelmed by mountains of data, emotions serve only to stifle rational thinking and that usually leads to losses. Quantitative trading does not have these problems.
All that being said, Henry does not see quantitative investing as a panacea – a solution for all investors. Because financial markets are constantly changing, so must quantitative models if they are to be successful. A quantitative system developed for one set of circumstances may prove to be ill-suited when conditions change and a different set of circumstances prevails.
Quantitative Investing Strategies
Quantitative investing comes in a variety of flavors. Some of these strategies aim to profit in all types of markets using a wide variety of investment vehicles, while others are more narrowly focused.
Some of the most prominent quant strategies are as follows:
CTA stands for commodity trading advisor, which refers to advisors who can trade commodities such as grain or pork bellies, as well as futures on a variety of financial instruments but typically not individual stocks and bonds. CTA in recent years has become associated with systematic trend following approaches that monitor price volatility to select entry and exit points. Once a certain price level is reached, a position is taken based upon the expected price trend from that level.
This strategy is often used by traders who turn over significant volume and have fairly short-term holding time horizons, generally only weeks or days. Gareth Henry estimates from 60% to 70% of traders using this technique lose money, while the remainder tend to win big using it.
Statistical arbitrage refers to a trading strategy involving the use of rapid computer analysis of big batches of data to seek a trading edge. The strategy can involve what is known as high frequency trading (HFT), where an investment firm tries to place its computer trading link as close to the exchange as possible to gain an advantage over its competitors. This approach looks to find mispricings within or between securities and capitalize on them by placing trades on the expectation that the anomalous pricing situation they have identified will revert to normal.
As with the CTA strategy, holding periods are typically short-term, in some cases mere seconds. High volumes of transactions are often utilized to take maximum advantage of pricing discrepancies as they occur.
Factor investing generally focuses on longer holding periods, sometimes multiple years, than are typically associated with the statistical analysis or CTA approaches. Quantitative investors using this strategy search for factors that have been known to characterize successful investments and invest in the stocks of companies with factors mirroring those characteristics.
Henry characterizes factor investing as more attuned towards risk management, or beta, than an alpha seeking approach such as CTA. The strategy seeks to capitalize on behavioral biases and the investment mistakes they can engender. “For example,” Henry says, people tend to undervalue less-glamorous stocks.” Factors can also consist of risk sources that tend to provide above-average returns to investors who take on that risk.
Risk parity also involves holding investments for longer periods of time. It is an asset allocation approach that focuses on holding positions of equal risk of a variety of asset classes that react in different ways to changes in market conditions.
Thus, holdings of a particular asset class would be determined by risk measures such as volatility or some similar measure, rather than by price. Less risky investments would be given higher portfolio weightings. Portfolio holdings are typically rebalanced on a regular basis in response to changes in their perceived riskiness. This approach seeks to reduce risk in a portfolio during times when markets are turbulent to deliver better risk-adjusted performance.
Systematic Global Macro
This strategy is similar to CTA but typically involves longer holding periods. It features trading over multiple asset classes and geographic locations based on macroeconomic factors. Systematic global macro investors take into consideration factors like changes in consumer spending, inflation, and employment and construct guidelines reflecting the impact of economic cycles on market trends.
For instance, this strategy often focuses on “carry” trades that involve selling the currencies of low interest rate countries and buying assets in countries with higher interest rates. This is often done in conjunction with diversification across a variety of asset classes to help provide downside protection.
Event Driven Arbitrage
This strategy involves high levels of portfolio turnover and focuses on placing bets on corporate events such as buyouts, restructurings, special dividends, and the like. Utilizing algorithms, firms using this strategy seek to identify and take advantage of mispricings of securities involved in these corporate events.
While strictly quantitative analysis has proven its worth as an investment strategy, it is increasingly being paired with fundamental analysis to gain a comprehensive understanding of investment opportunities. Integrating qualitative analysis with QA typically involves evaluating factors that are hard to evaluate using a quantitative approach. Gareth Henry describes this approach as follows: “Qualitative analysis works by establishing rich descriptions rather than quantifiable metrics. Qualitative analysis seeks to answer the “why” and “how” of human behavior.
An example of qualitative factors might be customer reviews of a product’s quality, or a securities analyst’s estimation of the durability of a company’s earnings. When combined with quantitative analysis, the two approaches can offer a comprehensive picture of an investment’s prospects, taking into account a wide swath of relevant factors.
Challenges for Quantitative Investing
As an increasing number of money managers have hopped on the quantitative investing bandwagon, it has become harder and harder for managers using QA strategies to outperform. At an estimated 90% of today’s market volume, quantitative investing has become the standard rather than the exception among investment firms that actively trade securities. A CNBC report cited by Gareth Henry found that less than 40% of institutional managers do better than their respective benchmarks. With many of these firms likely to be using QA strategies, it seems clear that quite a few of them are not providing the type of superior performance that their clients would like to see.
Data provided by HFR shows that in the first half of 2018, quant equity funds turned in a loss of 1%, which was surpassed on the downside by quant macro funds, which declined by 4.2%. These numbers amount to the worst performance in the past eight years for these strategies. Furthermore, by the end of October, the Barclay CTA Index showed a decline of 2.85%.
In his Medium post on the sector, Henry offers several possible reasons that quant strategies are struggling this year, including:
- Fizzling factors: While factors can also be thought of as risk premiums for taking on extra risk in return for greater return, at times these risks can overwhelm the return premium delivered by such strategies.
- Crowding: Money pouring into quant strategies can boost returns in the short run but ultimately lead to underperformance by pushing up the value of the investments used in the strategy to unsustainable levels.
- Rising interest rates: For decades interest rates have been falling, providing the fuel for rallies in both bond and stock markets. Now that rates have started to rise, it has wreaked havoc on strategies dependent on falling rates for success.
Given that many, if not the majority, of trading systems are designed with reference to past trading patterns, significant changes in market conditions can be problematic for the profitability of firms using such systems. Backtesting based on one set of economic and financial conditions may prove irrelevant under a different set of conditions. Generally, the further back in time you go, the less useful backtesting becomes. As a result, since interest rates have been falling, for the most part, since the early 1980s, there isn’t much useful backtesting data available for periods of rising interest rates. However, as Henry points out, a number of quant funds fared quite well before the financial crisis of 2008, when interest rates were rising.
According to Henry, the best explanation for the recent struggles of quantitative investing strategies is likely “a mix of crowding and the paradigm shift in interest.” While all investment strategies are prone to periods of underperformance, as Henry notes, quants typically tinker with their trading systems on a regular basis, which helps this approach steer clear of extended periods of subpar performance due to changing market conditions.
The latest quant funds employ models that are often more complex than those used by their predecessors back in the days of program trading. However, fund managers using these strategies have suffered from the recent poor performance of their models. Henry cites the rise in average pairwise-correlation of global stocks, which tracks how closely such securities move in conjunction with each other, as a sign confirming that the recent lackluster quant performance may indicate that the design of such strategies may not yet have been sufficiently adjusted to enable them to prosper in the new market environment.
So, what does this all mean? According to Gareth Henry, “investors who have fallen in love with quant funds irrespective of the particular strategy employed, the duration of the holding period, or the implicit risks still need to keep an eye on seismic changes in the interest-rate environment.”
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