Quants’ Quandary: Crossing the Chasm, Part 2 – Quant Orphans Want Adoption

The first quantitative hedge fund was launched in 1969, so there are almost 50 years of quantitative investment fund experience. Yet very few analysts, advisors and consultants recommend quant strategies to their institutional and/or high-net-worth retail clients. There’s enormous room for growth of the quant fund share of investor wallets and portfolios. In part 2 of his 5-part series, Little Harbor Advisors’ Rick Roche looks at the early years of quantitative investing, where quant strategies lie on the adoption curve, and why investors have been reluctant and resistant to quant strategies.

This is Part 2 of a five-part series. In this installment, Rick Roche shows where quant investing is on an “Adopter Categorization” scale and why investors have been reluctant and resistant to quant strategies. He reveals the birth and early years of quantitative investing and uses Everett Rogers’ classic diffusion model’s “Adopter Categorization” to illustrate where quant strategies lie on an adoption curve. Roche describes functional barriers to product adoption and models that explain consumer resistance and reluctance to innovation.

Read Part 1, “Quantifying Quants in Global Wealth Assets,” here.

Quant Funds Are Sold, Not Bought!

The first quantitative hedge fund was launched by Dr. Edward O. Thorp, a PhD math whiz, blackjack card counter and college professor. In 1969, he co-founded Convertible Hedge Associates, later named Princeton Newport Partners (PNP), the first market-neutral hedge fund. So there are almost 50 years of quantitative investment fund experience.

While Ed Thorp has been called “The Godfather of Quants,” quantitative equity investing stands on the shoulders of major theoretical and empirical contributors who laid the groundwork years or decades before his quant fund launch.

  • Benjamin Graham, a pioneer of value investing, was a quant.
  • Alfred Winslow Jones, inventor of the modern-day hedge fund in 1949, was a quant.
  • Noble Prize winner in Economic Sciences Harry Markowitz, PhD, author of “Portfolio Selection” (1952), who has been called The Father of Quantitative Analysis, is a quant.
  • Another Noble in Economic Sciences’ recipient, William (Bill) Sharpe, who authored “Capital Asset Prices” (CAPM) in Sept. 1964, is a quant.

Yet there’s a distinct minority – relatively speaking, a handful – of analysts, advisors and consultants who’ve recommended quant strategies to their institutional and/or high-net-worth retail clients. There’s enormous room for growth of the quant fund share of investor wallets and portfolios.

For 75 years, American anthropologists have exhaustively investigated consumers’ likelihood to adopt or reject innovative products and services. Readers of this article are likely familiar with the late Everett M. Rogers’s “Diffusion of Innovations” and his “Adoption Categorization on the Basis of Innovativeness.” This is where Rogers’ Bell Curve of five adopter categories, from Innovators to Laggards, was first displayed (see below). Rogers classified his five consumer segments using standard deviations in a normal distribution. His Innovators/Early Adopters and Laggards categories each made up 16%. His Early Majority and Late Majority categories weighed in at 34% a piece. In the following paragraphs, we’ll zero in on the flip side of Innovators/Early Adopters by examining the Laggards and Non-Adopters of quantitative investing.

(Source: “Diffusion of Innovations,” Everett M. Rogers, 4th ed. 1995, page 262)

Different or alternative investment strategies represent change to investors, and resistance or reluctance to change are normal investor responses. Rather than focus on why consumers adopt products/services, we might actually learn more by understanding the underlying reasons for innovation resistance. Social scientists have identified several functional and psychological barriers that result in consumer resistance or reluctance. Functional adoption barriers include usage, perceived value of the innovation, and perceived risk of trying a new product/service. The psychological barriers include the habit (tradition) toward an existing practice or product usage, the image barrier (for example, it’s harder or more complicated for mature consumers to use certain electronic devices) and the information (actually lack of information) barrier.

Of the major resistance barriers to the diffusion of innovations and adoption by a majority of consumers, three are the most relevant when it comes to widespread use of quant investment: 1) Value, 2) Risk, and 3) Information. Two of these barriers – Value and Risk – are functional obstacles. The third barrier – Information – is in a class of its own.

The Value Barrier is based on the purported monetary advantage of the new product – in the case of quant investors, does the substitute offering or new fund offer performance-to-price (risk-adjusted returns) compared to alternative investment options or funds? If it doesn’t, why bother switching investment strategies? Researchers have found an important reason that many product launches fail is the lack of acceptance by pragmatists who believe the cost of learning about an innovation outweighs the potential benefits it offers.

The Risk Barrier is the degree of uncertainty that accompanies any new investment choice. It’s not the riskiness of the strategy itself (i.e., standard deviation or drawdown risk), but rather the consumer’s perception of the characteristics of an untried service or product. A consumer (in our case, institutional or affluent investors) may feel that an investment strategy hasn’t been fully tested, may malfunction or perform poorly. Uncertainty is synonymous with innovations and there may be potential side effects or unintended consequences associated with adopting a “new and improved” idea.

The Information Barrier is the learning curve – that is, certain technologies (or advanced investment strategies) require a substantial learning effort plus a willingness to acquire, analyze and process data. A limited amount of relevant information (due diligence) or worse still – misinformation – impedes diffusion of an innovation or adoption of a superior service or product. In this writer’s opinion, the Information Barrier is the highest obstacle to overcome when it comes to a widespread use of quantitative investment. It’s a critical role for financial analysts and advisors to fill as the conduits for accurate, current and continuing education for their investor clientele.

Innovation resistance is typically triggered either because it poses change from a satisfactory status quo or because it conflicts with a belief system (habit). Given investor dissatisfaction with many actively managed investment funds’ performance (i.e., active managers’ inability to outperform their benchmarks), you would think that investors would actively seek alternatives. While passive investing certainly lowers cost, it leaves investors highly vulnerable to market turbulence and severe drawdowns (the October 2007 to March 2009 S&P 500 peak to trough decline was 56.8%). Satisfaction with the status quo doesn’t appear to be the cause for investor resistance or indifference to quant fund investing.

So how do these barriers manifest themselves when it comes to quant fund investing?

Do the Math!

“Measure what can be measured and make measurable what cannot be measured.”
–Italian Astronomer Galileo Galilei (1564-1642)

Let’s evaluate them sequentially. First up, the Value Barrier. The Value Barrier goes to the heart of whether investors and the financial advisors who work with them are convinced that quant funds are an economically worthwhile and pragmatic substitute for discretionary fund management. So how do you vaporize the Value Barrier?

First step: quantify the value of quantitative investing. Let’s start with a 360-degree macro overview. One of the straightforward potential strengths of quants is their ability to diversify across trade signals and instruments. Many quants trade stocks, bonds, futures contracts on currencies and commodity, options and stock/bond indexes on dozens or even hundreds of markets. And many quants are indifferent as to going long a position or selling it short.

Quants take full advantage of the Law of Large Numbers. In October 2014, the typical actively managed mutual fund (2,789 funds) had an average of 126 stocks in its portfolio. Bloomberg has published research showing that the majority of equity quants funds (~30%) have four times as many holdings – 500 or more. And nearly as many equity quants had double or triple the number of portfolio positions. Take that, Mr. Conviction Stockpicker!

Effective quant strategists play statistical odds, finding trades that work based on an average tendency – they are right slightly more often than they are wrong. Their quantitative models identify large numbers of trades in which the statistical probability of making a profit is greater than the probability of taking a loss. By identifying hundreds or thousands of trades with even a marginally attractive probability of making a profit (even as lows as 51/49%), they have the potential to generate alpha. Of course, they have to take transaction costs into account and use machine learning-derived algorithmic trading to seek favorable market exchanges and lower trading cost. Then they size their bets – spreading among hundreds or thousands of positions. Calculating quants use a “Trading Expectancy” formula. Here’s a sample one below:

Expectancy = (Probability of Win * Average Win) – (Probability of Loss * Average Loss) – Transaction Costs = Net Profits before Tax

One class of quant traders – Systematic Traders – focuses on making slightly more money on winning trades than they lose on bad trades in order to generate terminal wealth. In fact, some systematic (rules-based) traders use trend-following futures strategies that lose as much as 70% of the time. But the 30% of the winning trades that are successful are much larger in dollar terms than the losses that occur. Their winning trade-to-losing trade ratio is usually at least 2:1.

[Related: “Finance in the Age of Machine Learning, Part 2: Bet Sizing”]

When it comes to quants being long or short a security, there’s a conversation re-told by a renowned quant, Cliff Asness, PhD, of AQR. In a 2008 CFA Institute article, Asness relates a conversation he overheard with a “qualitative” (discretionary) manager who just added Philip Morris stock at a maximum weight. After the “qual” manager excitedly told one of the firm’s quant managers his enthusiasm for max weighting Philip Morris stock, he asked the quant, “What do you guys think?” The quant looked at him and said: “You know, I’m not sure if we’re long or short.”

On a micro view, weigh the quant fund manager’s management (and performance) fees in the context of their risk-adjusted returns. Here’s where the financial analyst and/or advisor’s expertise is indispensable. Candidly, many investors – even sophisticated ones – apply non-proportional thinking when evaluating cost-benefit ratios when trying to choose appropriate portfolio allocations. Behavioral finance researchers have found that non-proportional thinking in financial markets explain a number of investor behavioral quirks, under- and over-reaction to financial news (and consequent drift) and money illusions. We’ll return to this topic later in the series.

Storm the Barriers!

Here we consider the four-letter word – R-I-S-K – in a different context. For the moment, don’t equate the Risk Barrier with variance, the Greeks, Sigma (standard deviation), Sharpe or Sortino. In the context of innovation adoption, risk is the perceived risk of trying or buying a new product or service rather than a characteristic of the product itself. It’s the fear of the unknown that sparks resistance or reluctance to adoption intention, resulting in a wait-and-see attitude of laggards.

One of the proven ways to overcome the Risk Barrier when selling new products and services is to use client endorsements or testimonials. When it comes to investing, that avenue is absolutely closed to investment professionals. Section 206(4) of the Investment Advisers Act of 1940 generally prohibits any investment adviser from engaging in any act or practice that the Securities and Exchange Commission (SEC) defines as fraudulent, deceptive or manipulative.

In particular, Rule 206(4)-1(a)(1) specifically prohibits “publishing or distributing any advertisement, directly or indirectly, to any testimonial of any kind concerning the investment advisers or any advice, analysis or report or other service rendered by the adviser.”

If Plan A was to use client testimonials to promote investment in quant fund strategies, cross that off your list. Another obvious way to overcome resistance to adoption is to offer free samples (not happening in our domain) or product trials. A single-digit allocation to a quant fund is a one way of acclimating investors to the different traits of quant strategists. Of course, a small slice of quant allocation isn’t going to move the portfolio dial. But a solitary quant allocation isn’t made in a vacuum; it’s an incremental and additive process. There are simple correlation matrixes available that allow you to quickly visualize the interaction between various asset managers and investment strategies. Many investors have already added alternative risk premia such as fundamentally weighted Smart Beta ETFs or managed futures to their portfolios.

A final point on adding a dose of quant fund allocation to an investor’s portfolio … What is the marginal impact of adding a quantitative strategy to an investor portfolio? Even if a small allocation to a quant strategy makes a “marginal” contribution to the investor’s portfolio, it’s worth considering. If you allocate a slice of an investor’s portfolio to yet another discretionary manager whose style is only modestly different (or index fund, for that matter), it does little good to improve performance at the portfolio level. Most quant funds have low (or negative) correlation to the traditional 60/40 mix. So even a modest allocation to a quant fund on a stand-alone basis can improve the portfolio’s Information Ratio (more on this topic later).

In Part 3 of this five-part series, “Open the Black Box,” the author, Rick Roche, discusses his take on the greatest obstacles to widespread adoption of quantitative investment. He tackles myths associated with black box investing and aversion to algorithms in general and algorithmic investment. The opinions expressed in this publication are those of only its author, Richard Roche.

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