Source: Geofrey Moore

Quants’ Quandary: Crossing the Chasm BY RICK ROCHE, CAIA

Rick Roche will explore and analyze the acceptance and diffusion of quantitative investment management.

Roche makes practical recommendations for financial advisors’ and analysts’ due diligence on quantitative investment strategies and makes suggestions for the propagation and promotion of quantitative investing.

July 2019 ABSTRACT

This paper explores and analyzes the acceptance and diffusion of quantitative investment management. The author upends widely-held erroneous assumptions and media assertions of widespread quant investing.

Contrary to mainstream media portrayal of widespread adoption of quant fund investing, the author documents that a surprisingly small sum of individual and institutional dollars has been allocated to quant strategists. The author places into its proper context the amount of quant-managed fund allocations and describes, in general, potential benefits and drawbacks associated with quantitative investment.

Over a 14-month period, the author conducted over 40 in-person surveys on quantitative investment recommendations by financial advisors and analysts. The author makes novel contributions to the literature on quantitative investing by applying Everett M. Rogers’s Diffusion Of Innovations model to its diffusion (and lack thereof). Further, the author describes research on models of innovation resistance to decipher investor reluctance to invest in quantitative strategies and funds. The author also uses management consultant, Geoffrey Moore’s Crossing The Chasm model (1991) to illustrate currently where quantitative investment lies on the “Adopter Categorization” S-curve first posited by Dr. Rogers in his landmark diffusion bible. The author makes practical recommendations for financial advisors’ and analysts’ due diligence on quantitative investment strategies. Finally, the author opines and makes suggestions for the propagation and promotion of quantitative investing where appropriate for suitable investors.

Keywords: quantitative investing and investment management, in-person surveys of quant fund allocations, diffusion of innovations, resistance and reluctance to adoption of innovations, financial advisors (CFPs), financial analysts (CFAs and CAIAs), quant fund due diligence and propagation, and promotion of quant investment strategies Quants’ Quandary: Crossing the Chasm

quan * da * ry n. a state of doubt, confusion or uncertainty (1st known use was in 1579).

In a 2017 global fund survey, Preqin, an alternative investment industry researcher and publisher, stated there were ~3,600 quantitative investment strategists who managed $1,019 billion in assets.1 A quant research report sized the quantitative investment industry 70% larger by including quant-managed mutual funds and ‘smart ’ exchange traded funds (ETFs). Morgan Stanley research estimates that there was $1.7 trillion in quant/factor investing as of June 30, 2018. Morgan Stanley tallied $821 billion in quantitative mutual funds, $472 billion in quant hedge fund assets and $433 billion in smart beta ETFs.2

In May 2017, a Journal headline blared “The Quants Run Wall Street Now.” This headline was preceded by Forbes magazine trumpeting that “The Quants Are Taking Over Wall Street” in August 2016.3

Here are the working definitions in this article to describe quantitative investment strategists versus discretionary or ‘qualitative’ investment managers. Quantitative refers to whether the required inputs to a manager’s allocation and portfolio construction process are predominately objective and quantifiable. Quantitative investment strategists employ trading models and trading signals generated by computer algorithms. Qualitative asset management refers to whether the required inputs are predominately subjective (discretionary) and not quantifiable. Qualitative security selection or asset allocation may be arbitrary whereas quantitative strategies follow a rules-based, disciplined investment process.

So, while $1.7 trillion in asset under management (AuM) is nothing to sniff at, one needs to put this figure in its context. As of this writing (06/30/19), the United States’ capital markets are valued at roughly $73 trillion. The “outstanding United States debt” was $43 trillion (1Q 2019) and $30.18 trillion for the Wilshire 5000 Total Market Index (widely regarded as one of the broadest measures of the U.S. equity market cap).4

The Investment Company Institute (ICI), the mutual fund industry trade and research group, estimated that in 1Q 2019, there was $50 trillion in worldwide regulated open-fund assets.5 McKinsey & Company released their latest estimate of global in November 2018. McKinsey placed the size of global investment assets at $88.5 trillion as of year-end 2017.6 So quant investment strategies command a mere fraction—under two percent (2%)—of the amount of global investment assets. Hardly a quant takeover of Wall Street and global capital markets as alleged by mainstream media conglomerates!

Here is the quants’ quandary. Although mainstream media and even (informed) financial trade press have declared that quants are taking over Wall Street, the facts show otherwise. These hyper claims are reminiscent of “Gartner’s Hype Cycle” which highlights over hyped ideas and technologies and projects when (and if) these trends will reach maturity.7 On the Hype Cycle, there’s a trigger—in this case the birth of quant investing—that provides visibility into an innovation. This trigger leads to a “Peak of Inflated Expectations.” The Peak is followed by a “Trough of Disillusionment.” Quant pioneers and enthusiasts are still stuck in a decades-long trough where quantitative investing has yet to cross the chasms that separate innovators from the mainstream market of institutional and individual investors.

For a moment, compare quantitative investing’s adoption trajectory to the $5 trillion Exchange Traded Funds (ETFs) industry.8 ETFs were born in Canada in 1990. The first exchange traded product (ETP)—the Toronto 35 Index Participation units (TIPS)—was listed on the Toronto Stock Exchange in March 1990.9 Then along came a SPDR . . . ETFs were raised in the USA. In January 1993, the American Stock Exchange and State Street Global launched the Standard & Poor’s Depositary Receipts—SPDR (symbol SPY).10

An adolescent industry, smart beta funds are only a dozen or so years old. (INVESCO launched the FTSE RAFI US 1500 on September 20, 2006.)11 Yet in December 2017, smart beta funds’ assets crossed the $1 trillion mark, only 1/5th of the time it took quantitative funds to reach the $1 trillion AuM milestone.12 So

1 here’s the bottom line question: given the size of the U.S. and global capital markets, ETFs widespread diffusion and rapid adoption of smart beta funds, why isn’t the quantitative investment fund share much bigger than it is? Why are advisors hesitant to recommend and investors reluctant to invest in quant strategies?

The Survey Says?

“Whenever you can, count.”13 – Sir Francis Galton (1822-1911).

From October 2017 through May 2019, we conducted ad-hoc surveys into the number of financial advisors and analysts who’ve made client allocations to quantitative investment strategies and funds (funds of all types, hedge funds, mutual funds, SMAs and ETFs). Maybe we shouldn’t have been, but we were otherwise genuinely surprised to find that in 45 FPA, CFA and CAIA meetings (total audience ~ 2,100), a very small percentage of advisors had made recommendations or client allocations to quantitative funds. Only single digits of the Certified Financial Planners (CFPs), Chartered Financial Analysts (CFAs) and Chartered Alternative Investment Analysts (CAIAs) in attendance raised their hands in response to the question – “Have you made an allocation to a quantitative investment fund?”

We’re not suggesting our advisor/analyst sampling of quantitative investment use meets a rigorous standard of a scientifically valid survey protocol. Maybe the advisors/analysts in attendance who came to learn about artificial intelligence in asset management (and earn an hour of CE credit) are not representative of the entire universe of financial advisors? Or just maybe, the quant aficionados stayed in their offices because they already know a lot about quant funds or don’t need another hour of CE credit?

But our expectation was that easily 10 to 15% of these accredited financial professionals would have conducted due diligence on quant strategies and have already made allocations to client portfolios. Why the resistance or reluctance when it comes to adopting quantitative investment strategies?

2 Machine learning algos used to predict stoc and indexes. Convertible Arbs airs The Island-ATD-RenTec Quants use Alternative Data in Trading usion models to see elusive .

Statistical Hi-Speed Deep Algorithmic Learning/ 1970s Traders Mid-2000s Neural Nets

2012 - Beyond 1969 Mid-1990s High Frequency Fully Systematic Traders (HFTs) Trend Followers Predictive Algos

Buy Winners Sell Losers Trades in Microseconds 23rds .S. Stoc Trades © 2019 rickroche

Quant Fund Refresher

“The investor’s chief problem—and even his worst enemy—is likely to be himself.” - Benjamin Graham, The Intelligent Investor.15

In the opening section, we described our working definitions for Quantitative investment strategists versus discretionary or ‘Qual’ asset managers. While quants come in all shapes and sizes, we’ll borrow Man FRM’s simple two types of quantitative strategies (Man FRM is a UK-based, global alternatives investment specialist).15

The quantitative fund universe can be broken into two broad categories, based largely on the source of individual trading ideas:

Micro-based Strategies include (StatArb), equity , trading ideas and alpha-capture models that primarily trade equities.

Macro-based Strategies include Commodity Trading Advisors (CTAs), risk parity and managed futures that trade futures and derivatives across a wide array (hundreds) of commodity, currency, equity index and fixed income index contracts and markets.

Within each strategy, asset classes traded, holding periods (milliseconds to months), markets, and models used vary widely. Sometimes the boundaries between these two broad categories blur and larger quant shops and hedge funds often use both types.

Before attempting to answer questions on resistance and reluctance to adopt quantitative investment strategies into client portfolios, a quick primer of generic quant funds’ “potential” benefits follows:

3

• The Law of Large Numbers. The Law of Large Numbers. Quantitative investment strategists use automated systems to objectively evaluate a wide swath of securities and potentially benefit from a wider opportunity set. According to Sanford Bernstein Research, a plurality of equity quant managers hold at least 500 securities.16 A recent academic survey of 4,223 distinct mutual fund portfolios found that the median number of holdings in equity funds was 75 positions.17

While many quant funds have at least 500 names, some have thousands of holdings. In its most recent SEC 13F and 13D filings, disclosed that it had 3,415 total holdings including positions and derivatives.18 Quantitative Investment Management, a Virginia-based quant hedge fund, disclosed that its program had “over 1,500 active positions at any point in time and expects to make over 1,500 trades per day.”19 In 2009, Tradebot Systems, a Kansas City-based high-frequency trader/quant shop, had its first billion share trading day.20 Tradebot Systems states that they trade over 5,000 companies every month. On average, quant strategies have six times more holdings than discretionary or fundamental funds.

Of course, not all of a quant manager’s trades will generate profits. However, due to the high number of portfolio positions, a winning/losing trade ratio of 51/49 may be enough to offer a reasonable likelihood of generating a positive return at the portfolio level. (For more granular detail see the “Do the Math!” section below.)

• More Diversified. It goes without saying that quant funds are much more diversified than qualitative or discretionary managers. Acknowledging of course, that in order to achieve genuine diversification, quant strategists need to be mindful of the correlations among securities held. A critical component of a quant model optimizer is its correlation matrix and recognizing that correlations are dynamic. Correlations vary around a long-term central tendency—they’re conditional and depend on market states or regime changes.21 Quant fund portfolios tend to be better diversified, not only across individual securities, but by sector, country, and currency exposures.22

• Bet-Sizing. For actively-managed funds, the best portfolio is usually the one that can be sized most accurately rather than the highest Sharpe ratio or highest expected return. Generally speaking, quant strategists are more adept at position sizing and placing diversified bets than (most) discretionary managers. Full-fledged quantitative strategists use a variety of models, including alpha-seeking, position- sizing, risk management, and transaction cost models. The model that predicts the “side of the bet” (long or short) should be different from the model that determines the ‘size of the bet.’23

4 • Mitigating Human Error/Cognitive Bias. Financial analysts, advisors and portfolio managers (PMs)— just as all highly-evolved humans—are prone to bias. There are numerous primitive, unconscious biases and cognitive limitations that affect and afflict investor decision making. Several examples (that won’t be explored in depth) are confirmation bias, anchoring, loss-aversion, and familiarity bias. Investment algorithms addresses human weakness in speed, attention, fatigue, and biases. Quant models help minimize human errors (notice we didn’t say eliminate because of the potential for bugs and bias).

A key differentiator of quant strategists is the computational advantage that a disciplined approach has in systematically making predictions on entry and exit points in securities transactions. Quantitative models are superior to discretionary managers when cutting short losses and realizing gains.24 In a November 2009 report, noted quant researcher Ludwig Chincarini, PhD investigated the “market timing” skill of quantitative versus qualitative strategists. Chincarini found that “quant funds exhibit a positive timing coefficient” and that “qual funds, on the other hand, have negative and significant timing coefficients.”25

Over the 39.5-year period examined, Professor Chincarini’s research team documented that quantitative hedge funds (HFs) achieved higher returns, lower standard deviation than qual funds, and higher Sharpe and Sortino ratios. While quant and qual HFs performed similarly in UP markets, quant funds did significantly better in DOWN markets. Even over the period when the “Quant Quake” struck (Jan 2007 – Jun 2009), quantitative HFs outperformed their qualitative counterparts by 50 basis points.26

• Systematic Search for Factors/Risk Premia. Systematic specification of inputs for asset allocation models clarify central elements of portfolio construction. Quants search for risk factors, risk premia, and market anomalies. Quants attempt to build models to identify empirical relationships between securities and asset classes.

For example, statistical arbitrage (StatArb) is a quantitative short-term trading strategy. It typically employs mean reversion involving a portfolio of hundreds or more securities. It attempts to exploit price anomalies and is typically non-directional. The theory behind price-mean reversion is that there exists a center of gravity around which prices fluctuate. Quants believe it’s both possible and profitable to locate this center of gravity and quantify the level of price fluctuation that merits making a trade.

“Pairs Trading” is a form of StatArb that seeks relative value and the difference by going long poorer recent-performing securities and selling short outperformers with the expectation that within the near future (in seconds to days) their values will revert to the mean. These portfolios of longs and short positions are carefully matched by sector and region to mitigate market risk.27

• Beta Exposure Management. Financial markets invite quantification. Returns, risk factors, and correlations lend themselves to numerical measurement. When it comes to modulating or mitigating beta exposure, quantitative algorithms work faster than humans and are more consistent. We’d argue that without quant models, the only practical method of managing equity exposure is to go all cash or 100% fixed income.

Low/Negative Correlation (CORR) to Traditional Asset Classes. Historical correlations between quantitative and qualitative investment managers are low—suggesting the potential for investors to benefit by incorporating both approaches in their portfolio allocations. Professor Chincarini’s November 2009 report also examined the correlations of quantitative and qualitative hedge funds. From Jan 1970 – Jun 2009, the correlations of these HF types were quite low at 0.27 and 0.25 for quant and qualitative respectively.28

Select quant funds, specifically “Systematic ,” have very low correlations to equities and bonds. From Jan 2001 – Jun 2017, Systematic Global Macro funds’ monthly returns had -0.13 CORR to the S&P 500, -0.06 CORR to the MSCI All Country World Index (ACWI), and 0.25 CORR to Barclays Aggregate Bond index.

It’s worth noting that although correlations are time varying, the Systematic Global Macro quant strategy

5 was genuinely defensive in that its correlation to MSCI-ACWI declined during the 2007-09 Global Financial Crisis.29 In addition, historical correlations between quant and systematic strategist cohorts are also low—dispelling the notion that “all quants trade the same signals”.30

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.31 So there’s 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 decades or years before his Thorp’s quant fund launch.32

• Benjamin Graham, a pioneer of value investing was a Quant33 • Alfred Winslow Jones, inventor of the modern-day hedge fund in 1949, was a Quant34 • Nobel Prize winner in Economic Sciences, Harry Markowitz, PhD, author of “Portfolio Selection” (1952), has been called The Father of Quantitative Analysis and is a Quant35 • Another Nobel in Economic Sciences’ recipient, William (Bill) Sharpe, who authored “Capital Asset Prices” (CAPM) in September 1964 is a Quant36

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.37 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.”38 This is where Rogers’s Bell Curve of five adopter categories from Innovators to Laggards was first displayed. Ev Rogers classified his five consumer segments using standard deviations in a normal distribution. Rogers’s Innovators/Early Adopters and Laggards categories each made up 16%. (Take note that the “Innovator” cohort was only 2.5% of the total 16%.) Rogers’s Early Majority and Late Majority categories weighed it 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.

(Illustration from Diffusion Of Innovations, Everett M. Rogers, 4th ed. 1995, page 262).

6 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.39 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.40

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. The three most significant are: 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 that 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 worst 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.

7 Some observers question whether standard models of consumer behavior apply equally to institutional investors and their investment consultants. This writer, with nearly four decades of investment product industry experience and prior research, believes that institutional investors and consultants share the same consumer resistance and reluctance barriers outlined here. Investment committees’ and consultants’ decision-making processes are hampered and hamstrung by similar Functional Barriers and identical Psychological Barriers.40

Some academics (not this author) have called into question the value of investment consultants altogether. In a 2014 paper—”Picking Winners? Investment Consultants’ Recommendations of Fund Managers”—three academics stated they could find “no evidence that these [investment consultants] recommendations add value.”41 Authors contend that “Investment consultants have largely avoided the attentions of academics, reflecting the fact that consultants have disclosed too little data to allow rigorous analysis of their activities.”42 They used 13 years of Greenwich Associates’ survey data of consultants’ recommendations of institutional funds. (Greenwich Associates is a leading global provider of data and analytics to financial services firms.) In the “Picking Winners” conclusion of “why consultants’ recommendations fail to add value, we find a tendency of consultants to recommend large funds, which perform worse.”43

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 (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%44). Satisfaction with the status quo doesn’t appear to be the cause for investor resistance or indifference to quant fund investing. How do these functional and psychological 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.” – attributed to Italian Astronomer Galileo Galilei (February 15, 1564 – January 8, 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.45 Bloomberg has published research showing that the majority of equity quant funds (~30%) have four times as many holdings—500 or more.46 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— being right slightly more often than wrong. Their quantitative models identify large number of trades where 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

8 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 to seek favorable market exchanges and lower trading costs. 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—focus 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.47 But the 30% of the winning trades that are successful are much larger than the losses that occur. Their winning trade to losing trade ratio is usually at least 2:1.48

When it comes to quants being long or short a security, there’s a conversation retold by a renowned quant, , PhD of AQR. In a 2008 CFA Institute article, Mr. Asness relates a conversation he overheard by 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 of 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”.49

When considering investor resistance to quantitative investing—as opposed to consumer resistance and reluctance to product or service adoption—the value barrier may be even higher because of quant fund fees. The aforementioned “Comparison of Quantitative and Qualitative Hedge Funds” documented that “qualitative hedge funds” charged management fees that were ten basis points (10bps) higher than quantitative HFs! The performance fees were roughly the same for both quant and qual hedge funds.50 Lower management fees have for sure been a key driver of the tsunami flow of monies into passively- managed index funds and ETFs.

Roughly 60% of quant-managed assets reside in hedge fund vehicles. The ‘Two and Twenty’ (2 & 20) fee structure has almost vanished. A JP Morgan/Chase survey of 227 institutional investors found that only 5% of these investors paid a management fee of two percent or higher.51 But fees matter and may increase the hurtles required to overcome the Value Barrier.

On a micro view, weigh the quant fund manager’s management (and performance, if one) 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.52 We’ll return to this topic later in the text (see below).

Storm the Barriers!

Here we consider the four-letter word—RISK—in a different context. For the moment, don’t equate the Risk Barrier with variance, the , 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

9 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.”53

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 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 standalone basis can improve the portfolio’s Information Ratio (more on this topic below).

Info Gap – The Biggest Barrier

One of the universal truths of adoption intention is the lower the communicability of an innovation such as quantitative investing—the higher the resistance and reluctance to adopt. The biggest myth about quantitative investment is that it’s a Black Box!

Our view is quite the opposite. Quantitative investment is defined as rules-based, systematic, repeatable and sustainable (while recognizing that all models/markets are subject to model and alpha decay by arbitrageurs). Relying on automated trade signals from computer models has the effect of removing human emotion and can mitigate or minimize behavioral biases from the trading process. Whereas discretionary traders can always try to justify a gut decision on a story that opportunistically uses a number of variables to explain why they made the trade (usually with the benefit of 20/20 hindsight).

Some of the better performing quant funds offer remarkable degrees of transparency. In the quant world, that does not mean seeing position-level data every single day! It is unrealistic to expect that the quant firm under consideration share every line of code in their trading models.

In this context, quant fund transparency means sharing details about their research process. What types of asset classes, instruments and markets do they trade? What’s the typical holding period (days, weeks or months)? How and who builds their alpha models? What types of models, markets, and trading ideas have worked, and more importantly, examples of ones that have failed? Is the quant firm “eating its own cooking” — that is, how much personal funds do they have invested? Transparency is about having the quant shed light on what they do and how the strategy works to deliver consistent superior risk-adjusted returns on a repeatable basis.

In fact, once a quantitative manager explains their process, they can arguably be more transparent than their discretionary peers. Because it is the human brain—the brain of a discretionary manager or trader—that is the real black box. One of the most famous discretionary global macro traders is George Soros, the “billionaire who broke the British pound.” On Black Wednesday, September 16, 1992, Soros’ Quantum Fund made over £1

10 billion by pounding (selling short) the Pound Sterling. Why proffer George Soros, a legendary fundamental discretionary manager, as Exhibit A for human as black-box?

Here’s how his son, Robert Soros, describes his dad’s investment process:

“My father will sit down and give you theories to explain why he does this or that. But I remember seeing it as a kid and thinking . . . at least half of this is bull . . . I mean, you know the reason he changes his position on the market or whatever is because his back starts killing him. It has nothing to do with reason. He literally goes into a spasm, and it’s this early warning sign.” - Robert as written in The Crash of 2008 and What It Means, by George Soros54

When you open the Black Box, you’ll find quantitative investment to be disciplined, scientific, reliant on applied mathematics, statistics, and Bayesian probability. Quant investing is transparent to the programmers who build quant models with explainable coding, programming procedures and capital markets domain expertise. And just because it’s transparent to them and the firms they work for doesn’t mean they’re willing to share their source code with you!

We’re Only Human . . .

There is a quiet revolution taking place around the world. There’s an ever-increasing number of organizations —private and publicly-traded companies and governments at all levels—that have embraced algorithms to make what were traditionally human-based decisions. Why? Primarily because algorithms are less biased than their human counterparts.55 Humans are born and bound to make mistakes (credit to The Human League).

In exploring the reasons behind algorithmic ascendance, we’ll use the same approach used above in the diffusion of innovations. Rather than focus on the adopters, we trained our sights on resistance, reluctance, laggards and non-adopters. Here we’ll look at a world without algos. Rather than point out the (given) flaws and shortcomings of algorithms, we’ll compare their performance with human beings in a handful of domains.

What’s an algorithm anyway? An algo is a sequence of instructions that are carried out to perform a specific task. In investment management, an algorithm or algo is a “mathematical recipe” that harnesses models, data, computers, and telecommunications to buy or sell securities.

Algorithms have been used for centuries—even millennia. Around the same time that American anthropologists started researching diffusion, social scientists began to compare the performance of humans to algorithms. In 1954, a University of Minnesota psychologist, Dr. Paul Meehl, published “a disturbing little book” documenting 20 research studies that compared the predictions of well-informed human experts to simple algorithms.56

The score was Algos 20 – Humans 0. In each of the twenty cases, simple algorithms based on observed data such as past test score and past treatment modalities beat the human experts. And get this, Paul Meehl’s study was done over 60 years ago. Algorithms since then have advanced beyond belief. Following the late Dr. Meehl’s findings, there have been 200+ studies that have compared statistical prediction rule (SPR) algorithms’ performance to human experts. In most cases, statistical prediction rules beat subjective judgment. In the handful of cases where they don’t, they usually tie!57

Cognitive scientists, Richard Nesbitt and Lee Ross bluntly stated, “Human judges are not merely worse than optimal regression; they are worse than almost any regression equation.”58 Unlike advanced algorithms powered by machine learning, human cognitive abilities are largely unchanged over recent millennia.

The breadth and depth of domains where algorithms are less biased than humans is instructive and insightful. Here is a sampling of illustrations:

11 • In 2000, a meta-analysis of 136 studies on the prediction of human health and behavior, algorithms outperformed human forecasters by 10% on average and it was far more common for algos to generate accurate forecasts than human judges.59 • In 2002, a team of economists studied the impact of fully-automated underwriting algorithms in mortgage lending. The “automated underwriting systems more accurately predict defaults than manual underwriters do . . . this increased accuracy results in higher borrower approval rates, especially for underserved applicants.” • When a job-screening algorithm at a software company decided which applicants got job interviews, the algorithm favored “nontraditional” candidates much more than human screeners. Compared with human resource folks, the algorithm exhibited significantly less bias against candidates that were underrepresented at the firm. • In pre-trial bail hearings, a team of computer scientists and economists found that algorithms have the potential to achieve significantly more equitable decisions than judges who made bail decisions. There were jailing reductions of up to 41.9% with no increase in crime and simultaneously reduced racial disparities.60 • Studies comparing clinical (human) versus actuarial (statistical) predictions show that algorithms frequently outperform experts in predicting the survival of cancer patients, predicting heart attacks, and in assessing different kinds of pathologies.61

Much like certain unconscious human cognitive biases, algorithms can be biased, however (e.g., credit scoring and facial recognition).62 Algorithms can reflect the biases of programmers and datasets—from data selected, collected, and omitted—to train the model. We’re not suggesting that algorithms should be blindly or indiscriminately accepted or followed.

On balance, algorithms have multiple, built-in advantages of increased decision-making capacity, lower costs, minimization of errors (compared to humans), consistency, and when required, anonymity. If anything should concern us, it’s why so many important decisions are being made by humans who, prone to unconscious and conscious biases, are overconfident in our own judgments, and have phenomenally poor track records when it comes to decision making.

When it comes to investing, the $64 trillion question should be: Is a quantitative approach or algorithmic method superior to a discretionary manager’s subjective judgment? In the vast majority of instances, the answer is to go with the algo.

Investors’ Algorithmic Aversion

“The investor’s chief problem—and even his worst enemy—is likely to be himself.” – Benjamin Graham, The Intelligent Investor.63

As made abundantly clear, research new and old shows that statistical prediction rules are more accurate at making forecasts than human forecasters. Yet when forecasters decide whether to prefer a human forecast over an algorithmic one, they often choose the human.64 This phenomenon, called Algorithm Aversion, is costly. If algorithms are better at forecasting than humans, it’d be logical for people to choose to go with the algos. But often, they don’t.

There were a series of five experiments on algorithm aversion conducted by the Wharton School. Researchers documented that humans made 15-29% more errors than algorithms when given MBA student admission data with the task of predicting how well the students would perform in the MBA program. In a second set of experiments, humans made 90-97% more errors than algos when predicting the rank of 50 individual states in terms of the number of airline passenger departures from each respective state in 2011. Unsurprising, the algorithmic model beat the human forecasters in all five studies.

Here’s the rest of the story . . . study participants (roughly 741) were given the choice to place bets (and earn bonus dollars) after seeing both humans and algorithms make errors in forecasts. Seeing a model make

12 relatively small mistakes consistently decreased confidence in the model. Participants who saw the model outperform the human in the first stage of the experiment (610 Wharton School students) were among the least likely to tie their potential bonus to the algo.

Yet when human forecasters made large errors, it didn’t cause participants to lose confidence in their fortune-telling skill. This was true even when human forecasters produced nearly twice as many errors as the algo model predictions. So, while “to err is human” and can be forgiven and forgotten, many people demand infallibility from algorithms.

In these studies, participants applied a double standard when assessing prediction accuracy. They were more likely to forgive an MBA admissions committee than an admission algorithm for making errors. They placed more confidence in man vs. machine on airline passenger state rank forecasts. Just the act of seeing an algorithm perform—even when its accuracy was twice as high as human forecasters—made people less likely to choose it. These graduate business majors chose to place trust in people with a pulse—whose logic and cognitive behaviors are knowingly flawed—over hardware and software that operates (mostly) in a bias-free way.

Although recent and relevant research shows many people exhibit algorithm aversion, it doesn’t explain when or why people pass on algos. There are a number of theories why folks prefer human forecasts over computer-derived predictions. Some researchers theorize that algorithmic-averse folks presume that human forecasters will improve through experience. Others feel that it’s dehumanizing or unethical to rely on algorithms for important decisions or that people desire perfect forecasts (and for some reason believe that humans are infallible at prediction).

There’s a June 2015 Wall Street Journal Op-Ed piece by Two Sigma Co-Chairman/Founder, David Siegel, that summarizes our thoughts on algorithm aversion. Two Sigma is a leading-edge quantitative investment strategist, guided by the scientific method when building its algorithmic models.

“The sooner we learn to place our faith in algorithms to perform tasks at which they are demonstrably excel, the better off we humans will be. If the fear of the unknown really is driving skeptics’ irrational bias against algorithms, then it is the task of the practitioners who do understand their power (and limitations) to make the case in their favor.” – David Siegel, PhD

Code-Dependency/Human + Machine Intel

“No man is better than a machine. And no machine is better than a man with a machine.”65 – Paul Tudor Jones, HF Manager, Wall Street Journal, August 16, 2016.

Even though simple algorithms commonly outperform unaided expert judgment, this doesn’t mean we should “take humans out of the loop.” Data features don’t spontaneously appear in quantitative models. The most frequently used algorithms in financial services—credit extension and mortgages, financial fraud detection, underwriting—are heavily dependent on domain experts who coach and counsel coders who write the programs. Data scientists and programmers rely on the acquired knowledge of domain experts and end users.

In the hands of talented people, particularly individuals with domain expertise, quantitative finance algorithms can produce positive investment results. Machine-learning algorithms can sniff out patterns— even when there are none!66 There’s no shortage of patterns in the history of financial markets, particularly with humans genetically programmed to seek them out. But most have no predictive powers. Quant models can find false positive “discoveries” by overfitting data. While a backtest may indicate an attractive Sharpe ratio, it might fail miserably on out-of-sample data.

13 Quant models often uncover spurious correlations. Apple’s stock price on January 1 of 2007-09 had a 0.999995 correlation with visitors to Orlando’s SeaWorld (see chart above). What do tourist visits to SeaWorld in Orlando have to do with Apple’s stock price? Nothing!67 Discovering a correlation but failing to search for an underlying causation occurs rather frequently in quantitative finance. Spurious correlations have been called, “the kryptonite of our (quantitative finance) industry.”68 Domain expertise is essential to grasp why a premium exists in the first place. Generally, there are three explanations of why premia exist:69

• Risk-Based: The premium is compensation for taking on a systematic risk • Behavioral: The premium occurs due to persistent investor behavioral traits • Structural: The premium results from industry structure, constraints or targets

In addition to these three general reasons that explain why premia exist, academicians have shown that because acquiring information is costly (in time and resources spent), that there are informationally inefficient markets.70 Today, “Next-Generation Quants” are harnessing advanced machine learning algorithms running on cloud-based supercomputers for stock selection and market prediction.71 These AI quantitative pioneers experiment with alternative investment datasets to seek new sources of uncorrelated “Algorithmic Alphas.”72

Similar to the informationally inefficient markets hypothesis, some of these powerful pattern-seeking algorithms defy human explanation when discovering a previously unknown alpha. The reality is that certain patterns escape human attention because they’re either too subtle, too numerous or too fast in the data.73 Some of these purported market inefficiencies will not prove profitable because they’re noise, illiquid, and/or un-investable.

This is where domain expertise is essential to decipher whether an alpha signal is spurious or not. Is the source of return persistent and hopefully sustainable? Are the premia unique, ideally an uncorrelated source of return and accessible? Is it capacity-constrained? If so, what are its limits? Or is it just an alpha mirage that’s illusionary, transient and easily arbitraged away? Generally speaking, there should be an underlying rationale for the source of a market premium, otherwise it’s fool’s gold.

Quant models can decay or become obsolete. Although a model may be static, capital markets certainly aren’t. Markets are dynamic, complex and adaptive. Me-too quants hear about factors and risk premia from word-of-mouth and by reading academic journals. They crowd trades and arbitrage away previously “under- discovered” alpha sources.

Unless the assumptions that underlie their quantitative models remain realistic and relevant in the future, the source of the alpha model’s competitive advantage evaporates overnight or gradually fades away. If humans aren’t there to monitor model performance, to evaluate and make modifications when required, their quant model can run out of steam.

So, we’re not ready to throw in the towel and turn investment management over completely to machines.

14 Investment domain skills such as asset allocation and sector weighting, idiosyncratic security selection, market microstructure knowledge and an understanding of why a risk premium exists are essential ingredients. Think of qualified quants as having dozens of cookbooks that understand market dynamics and hundreds of investment recipes in the form of algorithms.

As Gordon Moore’s Law collides with Murphy’s Law, “whatever can go wrong will go wrong faster and bigger when computer algorithms are involved.” Human and machine intelligence make for an unbeatable combination compared to machine or man alone.

Quants’ Quandary – A Reality Check

Following the Quant Quake of August 2007 and the Global Financial Crisis of 2008-09, quant fund AuM declined from its peak percentages of total global investment assets. The asset management industry’s 800-pound gorilla—BlackRock—reported that quant assets under management dropped 35%. And anecdotes from quant practitioners suggested that in strategies such as “long/short global equity,” the percentage decline in AuM approached 80%.74

When performance is strong, opaque investment processes are less questioned. In fact, complexity is often viewed in a positive vein as a differentiated trait. However, when market regimes experience seismic events, opaque quant strategies that underperform are quickly mistrusted. When the Quant Quake struck a little over a decade ago, quant strategists struggled to explain how and why their models failed to perform and many investors subsequently bailed.

Compared to other investment fund flows, quant funds are the industry’s ugly ducklings. In the first six months of 2018, investor flows into quant hedge funds (HFs) were a paltry $4.6Bn. That’s the most sluggish pace since the Global Financial Crisis when quant HF withdrawals were $35.5Bn.75 To restate a rebuttal to mainstream media’s wild exaggeration, no, quants have not taken over Wall Street—not by a long shot.

Some industry experts believe “quants have a public relations problem of their own making.”76 Dan diBartolomeo of Northfield Information Services has stated (paraphrasing here) that many quant strategists are enamored with complexity and willingly or unwittingly reinforce the black box stereotype. There’s ego gratification in cloaking their investment process from the Early Adopter or Early Majority investor types. Dan diBartolomeo believes “the quant community is unwilling or unable to articulate their investment concepts in common language without falling to the temptation to use misleading statistics to exaggerate the expected benefits.”77

Outsiders who are not as mathematically gifted or endowed view quantitative methods with skepticism and distrust. Dan diBartolomeo said that in terms of how quant managers communicated the effects of the August 2007 Quant Quake to the public via the financial trade press was “abysmal . . . an exercise in PR spin that spun out of control and discredited the industry.”78

If quant funds are to transform from misunderstood ugly ducklings into gracious swans, White Swans—not Black or Gray, heaven forbid—what must advisors and analysts do? What role do you, the 21st century advisor/ analyst need to play? Taking a page from the Diffusion of Innovations playbook, advisors/analysts must take on the role of Change Agent.

Diffusion itself is a particular form of communication in which the message content that is exchanged is concerned with a (relatively) new idea, product or service. Many products are not quite “overnight success” stories. For example, it took about 150 years before the widespread adoption of facsimile (fax) machines. The fax was invented in 1843 by Alexander Bain, a Scottish clock maker (fax came after the telegraph but before the telephone).79 But it wasn’t until the 1980s that fax machines reached critical mass as the price of machines came down. While fax transmission has been widely displaced by email, there are still tens of millions of fax machines in use, especially in doctors’ offices and hospitals (some blame HIPPA).80

15 To date, the quantitative investment industry has followed a decentralized model as described by Ev Rogers (see display above).81 Inarguably the first quant fund manager, Edward Thorp, is a central character in the diffusion quantitative investment. Thorp shared his quant concepts (and even the model itself with a handful of folks) and helped spread the “Gospel of Quant.”

Among the luminaries in the quant world, Ed Thorp worked with Gerry Bamberger, who is generally credited as a founder of statistical arbitrage (StatArb) while working at Morgan Stanley.82 Thorp influenced, coached, counseled and/or seeded Bill Gross (Bond King), Ken Griffin (Citadel), Blair Hull (Hull Trading) and the principals at TGS Management, a quantitative fund and the fourth largest philanthropic foundation in the United States.83

In the decentralized diffusion model, new ideas spread horizontally via peer networks. There is a high degree of experimentation and re-invention that occurs as innovations are modified to fit the needs of innovators and early adopters. A decentralized diffusion system works best when its users are highly educated and technically competent practitioners. Participants in a decentralized diffusion ecosystem have a sense of control and freedom to make modifications to address specific desires and preferences. As empirical evidence, there are thousands of Thorp quant progeny and variants. But relatively speaking, there are far fewer investors and a lot less AuM in quant strategies versus discretionary-managed funds.

More Moore Laws

“However, not everything that that can be counted counts, and not everything that counts can be counted.” – William Bruce Cameron (“Informal Sociology,” 1963).

There are two prominent high-tech, high priests named Moore featured in this story—Gordon and Geoffrey —not to be confused with one another. Gordon E. Moore, PhD, (see above in Code Dependency) was a co-founder of Fairfield Semiconductor and former CEO at Intel. In 1965, Gordon wrote a brief article that predicted a doubling of the number of components per integrated circuit every year, later revised to a doubling every two years. It’s called Moore’s Law. Moore’s Law was both an observation and projection of a historical trend to “cramming more components onto integrated circuits.”84

Geoffrey A. Moore, PhD, is a management consultant, author and high-tech sales and marketing executive. Dr. Moore built upon and extended Dr. Everett Rogers’s pioneering work on “Adopter Categorization on the Basis of Innovativeness” (bell curve or S-curve).

16 Geoffrey Moore explains his technology adoption theory in Crossing The Chasm: Marketing and Selling High-Tech Products to Mainstream Customers.85 Moore states that many firms’ business plans are based on Rogers’s “Adoption Lifecycle” where you work the “S-curve” from left to right. You progressively convince each category of users to adopt your innovative product or service. Then you use each “captured” segment as a reference for the next category and on down the line. The traditional model implies or assumes that there’s an inevitability of adoption and diffusion. But oh, how the real world differs from Ev Rogers’s Diffusion Of Innovations textbook definition.

In 1989, when Geoffrey Moore was writing his first draft ofCrossing The Chasm, he used an electric car as an example of a disruptive technology waiting to be adopted.86 (Self-driving cars in 1989? Yep, that’s right. Carnegie Mellon robotics’ engineers were driving a retrofitted Army ambulance around campus. It was called ALVINN—Autonomous Land Vehicle in a Neural Network—and reached speeds of 70 miles per hour and traveled 90 miles to Erie, PA, driverless.)87

When Moore revised his book in 1999, he used electric cars as the poster child for slow adoption again. GM had just mass produced 1,200 EV1 electric cars. The EV1 wasn’t available for purchase, it was leased by GM as part of “real world engineering evaluation”.88 Although customer reactions were positive, GM ended up crushing most of the EV1 vehicles (literally) because GM thought electric cars were unprofitable. In his latest update in 2013, Moore did a ditto, with Tesla appearing as poster child.

Glacially-slow adoption by hesitant and resistant customers, laggards and “when hell freezes over” types (non- adopters) have plagued product and service innovations since the dawn of the Industrial Revolution. There are thousands of discontinuous or disruptive innovations that undergo fits and starts. Even when there are quantifiable advantages and potential benefits from a next generation service such as quantitative investment management, its diffusion and subsequent adoption by investor segments is not inevitable. Geoffrey Moore set out to understand why.

17 The Chasms

Geoffrey Moore’s major contribution to the theory of technology product adoption and diffusion is that there are chasms or cracks in the bell curve that separate Innovators from Early Adopters and an even larger chasm between Early Adopters and the Early Majority. Each gap represents a chance—a risk—of losing marketing momentum.89 If the gap or chasm is not crossed, your new and improved way of doing things will not transit to the next group or consumer cohort. And a marketing strategy that wins over one segment won’t necessarily work with the next cohort on the S-curve.

In Moore’s analysis, visionaries/Innovators have very different expectations from more practical Early Adopters. Moore believed that because there’s disassociation between these groups, that “[there’s] the difficulty any psychographic group will have in accepting a product if it is presented the same way as it was to the group to its immediate left.” Innovators aggressively pursue novelty, they want to be the first to try new stuff. In order to convince Early Adopters and subsequent consumer segments, behavioral changes are required. Investors willing to test drive quant funds must be “quantitatively content” that the proposed benefits of quant strategies offset potential drawbacks, downsides, and disadvantages.

Early Adopters don’t embrace novelty for novelty’s sake. They have a “wait-and-see” attitude. Although Early Adopters buy into product concepts early on, they’re not ‘technologists’ or enthusiasts per se. As shown above, Moore’s first chasm—the Small Chasm—is between these two categories. (Author’s note: other depictions of Adoption Curves show a sixth category—Non-Adopters.)

Each of these two deep and dividing chasms must be crossed in order to facilitate or speed up adoption of innovations. Moore states that visionaries and enthusiasts (the Innovators) who create disruptive or discontinuous technologies (like the Ed Thorps of the quant world), are fundamentally different from Early Adopters. Applying mathematical and scientific approaches initially to games of chance then to capital markets was a consistent, central and compulsive curiosity throughout his life. Yes, Dr. Edward O. Thorp, is different from you and me.

The key to getting beyond the enthusiasts and winning over Early Adopters is to show that the new

18 technology or product innovation is a strategic leap forward.90 Concept promoters and propagators must communicate that this advancement is so compelling that it has intrinsic and sustainable value to appeal to non-technologists. It usually requires a flagship application that showcases the unique value of the innovation and signals Early Adopters to move forward. And in this regard, financial advisors/analysts have their hands tied behind their backs due to the SEC’s prohibition on the use of investor testimonials.

In Moore’s telling, the Early Majority are pragmatists who wait until an innovation has been proven before they accept and buy into a new(er) product or service concept. The Early Majority realize that many newfangled inventions end up as passing fads.91 They want well-established references before buy-in. Because it’s such a large market segment (roughly 1/3rd), persuading the Early Majority is the key to widespread adoption and market success.

Unfortunately—for quants anyway—we seem to be stuck in the Innovator stage where less than 2.5% of the potential adopters have moved forward with quant allocations. We’ve not yet crossed the Red Sea of Quantdom, much less moved into the mainstream markets of the Early Majority and the Late Majority. There are miles to go, chasms to cross and tens of trillions of investment assets to re-allocate before “Quants Truly Run Wall Street!”

Don’t Do Due Dilly, Willy-Nilly

Given the plethora of quant hedge funds, ETFs, and quantitative-driven mutual funds, what’s the first step before putting on the Change Agent cloak? Quantify quant fund options through a rigorous due diligence process. Do due dilly on quant fund choices to find the best fit for your ideal client. Due diligence should be quantitative, qualitative, and logical—not done in a haphazard way (that is, not conducted willy-nilly).

Due diligence can and should be conducted on several levels. The firm whose two quant fund categories are cited above (Man FRM), provides terrific direction on due diligence of quant managers’ capabilities. Man FRM recommends a four-pronged approach to quant manager examination and investigation. The four broad areas (summarized below) are: 1.) research process; 2.) trade execution capabilities; 3.) investment infrastructure; and 4.) risk management.

1. Research process. Quantitative investing should be guided by scientific methods. Most quant models rely heavily on historical data and the persistence of prior market behavior. Quant strategists must have a well-organized, disciplined and repeatable process. Does the quant firm under consideration have a strategy that the firm’s principals can articulate in common terms? What are the strategy’s “sources of return” and underlying economic motive for excess return premiums? A related concern is whether or not the quant’s sources of market inefficiencies have been extensively researched and how they plan to adjust their strategy when the alpha begins to decay. Are there defined stages for review and sign-off on alpha model changes and modifications by senior managers? 2. Trading execution capabilities. Because trading frictions are an essential consideration (see “Model Errors/Risks” above), what are the quant shop’s capabilities when it comes to trade execution and minimizing “slippage”? Does the quant firm use proprietary execution algorithms or does it rely on its prime broker or custodian’s algorithms? VWAP (volume weighted average price) and TWAP (time weighted average price) algos are tried and true but tired trading techniques that don’t guarantee best price arrival in today’s highly-evolved and lightning-quick market microstructure. 3. Investment infrastructure. Many of today’s most successful quant firms are, first and foremost, technology companies. They employ programmers, data scientists, and investment domain experts. The most successful quant strategists combine scalable technology platforms with advanced algorithms and alternative investment datasets. It’s vital to query quants on how they collect, clean, and analyze data. Do they use a common coding language understood and accessible to everyone on the research team? (Essential to check and verify output if a programmer leaves the team.) How do they mitigate culture clash between millennial programmers and fundamental analysts and PMs?

19 4. Risk management. Although it’s the last item on Man FRM’s due dilly punch list, it might as well be first. Most quants use generic “off-the-shelf” risk and optimization models that might not reconcile precisely with their alpha bets. Risk is typically measured with some sort of multiple-factor model that forecasts portfolio risk and tracking error or a variant of (APT). Third- party, multi-factor models decompose the risk structure of portfolios based on the particular set of factors used in the risk model, but are incomplete if they don’t capture alphas unique to the strategist’s alpha model design.

APT assumes each security can be broken down into exposures to various common risk factors plus idiosyncratic or residual risks. A challenge with using generic risk and optimizer models arises when a quant’s alpha model forecast is imperfectly correlated with associated risks which results in forecast misalignment. Misalignment can result in possibly perverse, undesired, and destabilizing risk exposures and counterintuitive portfolio outcomes.92 Ask the strategist under consideration how they define, measure, and manage risk. Do they define risk as volatility (i.e., standard deviation) or maximum drawdown? How many weeks/months/years to recover from a max drawdown? When and why does a quant strategist pull the plug on a given strategy?

Thorough analysis requires assessing the quant’s qualifications and experience, performance over varied market regimes, and an actual live track record (as opposed to relying on backtests alone). What’s the biggest drawback to the quant’s strategy: including use of leverage, possible drawdowns, operational failures, override mechanisms, and/or model decay? In effect, what is the quantitative strategist’s unique edge or advantage?

In Rishi Narang’s Inside The Black Box, he explains why details matter so much when evaluating quants. Circling back to the “Trading Expectancy” formula described above, there are trend-following systematic traders that lose as much as 70% of the time!93 But the most successful of them have dramatically larger payouts on their winning bets than the losses suffered on losing trades.94 Just because a quant has a PhD in math or physics doesn’t guarantee s/he will be skilled at navigating securities markets. Genius, two Nobel Prize in Economics recipients, leverage, and overconfidence were a recipe for an absolute quant fund disaster at Long Term Capital Management in 1998.95

Some of the best and brightest quants have no advanced degrees at all. What they have is an edge—an edge in research capabilities, an edge in the acquisition and cleaning of alternative investment datasets, or an edge in the particular market or securities they trade. The most successful and consistent performers in quantitative finance compulsively and constantly work to improve their alpha, risk, bet-sizing models, their execution algorithms, and investor relations and communications.

20

Sum Equal to More than Its Parts

Portfolio construction is about allocating to various exposures. A portfolio that contains more exposures— discretionary AND quantitative—is more diversified than one concentrated among fundamental managers who underweight or overweight factors or sectors or engage in shareholder activism. Many (most) exposures can be accessed in traditional asset classes, either via an active manager—discretionary or quantitative—or in a passive or indexed vehicle. There are numerous and sundry alternative asset classes from timber to investment real estate, to private equity and managed futures.

When constructing or modifying investors’ portfolios, it’s essential to consider various elements:96

1. Investors’ risk tolerance and time horizons 2. Inexpensive beta sources 3. Various types of alpha exposures 4. Various bet structures—quants make far more bets than typical discretionary mangers 5. Time diversification besides asset class allocation. Wide variety of strategies such as CTA/HFT97 for short-term (milliseconds/minutes/hours), intermediate term (days/weeks) and longer-time (months/years) duration 6. Stress-testing portfolio risk, volatility, and drawdown 7. Total Trading & Transaction Cost. Alpha - transaction costs = actual return (pre-tax) 8. Correlations among asset classes/strategies; recognizing correlations are dynamic, not static, and prone to “go to one” when market turbulence strikes.

Here’s the bottom line: The principal objective of quantitative fund due diligence is to find a quantitative strategist(s) that has a genuine edge over a discretionary fund choice. Quantitative analysis is time consuming, mentally challenging, and requires great care in evaluating the results. It must be combined with qualitative research and other due diligence measures to form a complete picture. Such efforts are, however, well worth the time and brainpower, and are an important tool to help protect and grow your clients’ investment capital.

Educate. Propagate. Innovate. Change.

“I remember a remark of Albert Einstein . . . . He said, in effect, that everything should be as simple as it can be, but not simpler . . . .” - Roger Sessions: How A “Difficult” Composer Got That Way.98

To a great extent, quantitative investing has concurrently been romanticized, stigmatized, and demonized by the popular press and uninformed regulators. Quants have been blamed for a host of ills including the 2007- 2009 Global Financial Crisis, the May 2010 Flash Crash, and varied economic problems caused by liar loans

21 and sub-prime mortgages packaged together and rated AAA by pay-for-play rating agencies. Rather than causing market turbulence and turmoil, quant investing has suffered due to central banker interventions and geopolitical events that have shaken confidence and rattled markets since 2008.99

With a half of century behind it, quant investment is late in its lifecycle and yet has only a fraction of total investment management AuM. Financial advisors and analysts have not yet effectively communicated the potential benefits of quantitative investment to qualified clients. The communicability of an innovation is the ease and effectiveness with which the results from adopting an innovation can be disseminated to others. What’s required now at an equity market transition or inflection point is education and propagation (that’s propagation, not propaganda). In this context, propagation is the action of widely spreading and promoting an idea or theory.

Propagation of the “gospel of quant” requires direct, personal and frequent communication with individual and institutional investors (with attendant suitability standards applied). If financial advisors and analysts have already earned credibility with clients, they’re in a unique position to clearly quantify and persuasively communicate the potential benefits of quant investing. In light of the obstacles previously discussed in this thought piece, advisors/analysts should anticipate resistance to the adoption of quantitative investing. Advisors and analysts are advised to present quant investment in the context of portfolio diversification by making allocations to both discretionary (qualitative) and quantitative managers.

Given the major barriers to the adoption of innovations described above, advisors and analysts intent on propagating the “gospel of quant” should strive for the following:

• Advisors and analysts’ credibility are paramount to successful propagation. • Strive for clarity and simplicity when educating clients about quantitative investing. Avoid buzzwords and highly technical terms that may lead to discomfort and resistance. • Open the Black Box! Quantitative investments are powered by repeatable, sustainable rules-based investment algorithms. Quants use a disciplined, scientific approach that seeks to exploit persistent statistical relationships between markets and select securities. • Like Einstein recommended, explain quant concepts simply in a common language that even unsophisticated retail investors can understand. Use metaphors and analogies to explain anomalies! Transform black box strategies into “Glass Box” strategies by communicating the particular portfolio thesis of the quant strategy you’re promoting. Is it a “micro-based” or “macro- based” strategy? What are the principal risk premia or market anomalies the quant’s alpha model is seeking to exploit? • Compare the relative advantages associated with quantitative strategies to discretionary or qualitative investment management. Three distinct differentiators favoring quants are the Law of Large Numbers, rules-based algorithms’ abilities to mitigate human errors and cognitive biases and the low correlation of most quants to discretionary managers. • Find and communicate similarities of quant strategies to other types of alternative investments. Most investors are more comfortable moving forward if an investment concept is tried and tested by others before them. • Seek to inform investors about the potential benefits and risks associated with quant investing. The higher the level of informativeness, the lower the innovation resistance.100 • Even adding a dedicated slice (5-7%) of a portfolio’s strategic allocation to a quant strategy can be additive. Although single digit exposures would barely move the dial, a portfolio that contains different exposures—discretionary and quantitative—is more diversified than one concentrated among four or five fundamental managers.

Successful Quant Chasm Transit

Crossing the Chasms requires moving investment innovations from the comfort of visionaries to countering the skepticism among pragmatists. It means transitioning from the enthusiastic support of quantitative evangelists to an unfamiliar ground of investment-wary generalists.

22 As opined in the “Info Gap” section above, this author believes that the Information Barrier is the most significant obstacle that has impeded quantitative investment adoption. Many occupations can assume the role of change agents to modify customers’ preferences and close the gap between product adopters, laggards, and resistors. Teachers, consultants, agricultural extension agents (who spread adoption of hybrid corn/seeds), salespeople and financial advisors and analysts can facilitate the flow of information and diffusion of innovations.101

Change agents wouldn’t be needed if there weren’t barriers to the adoption of innovations—but there are! By understanding investors’ objectives, risk tolerance, and time frames, credentialed advisors and analysts can selectively transmit relevant information that may result in superior investment outcomes. Change agent success in motivating investors to consider quant strategies as part of a portfolio’s composition is directly related to the frequency and clarity of their communications.

Now of course, as fiduciary advisors, recommendations made must be compatible with clients’ needs and experience. Effective and persuasive change agents must be empathetic by placing themselves in the roles of counseled clients. In studies of innovation diffusion, the one variable most closely related to success is the frequency of contact with the change agent.102 Repetition is the mother of successful innovation adoption. Repeat, repetition is the mother of adoption . . . .

Some Closing Thoughts . . .

This discussion complements theoretical reflections on investor and advisor resistance and reluctance to making allocations to quantitative investment strategies in an effort to create a broader perspective. The intent is to make a novel contribution to literature on quantitative investment by applying Everett M. Rogers’s Diffusion Of Innovations model and subsequent research on models of innovation resistance to decipher investor reluctance to investing in quantitative strategies and funds. In the process, this author upended widely-held and erroneous assumptions by dispelling the myth of widespread quantitative investment management.

Readers of financial trade press can be forgiven for being fooled into thinking that “Quants run Wall Street now!” Although the mainstream media may have reported a quantitative groundswell, the mainstream markets have not yet bought into quant investing. Even a rudimentary examination of investment industry facts contradicts that fallacy. With quant investing at the half-century mark (Ed Thorp’s fund launch in 1969), it represents under two percent (2%) of the world’s global wealth assets. For quants’ sake, hopefully it won’t take a 150-years as it did for facsimile machines to reach critical mass among the mainstream market.103

Investment innovations such as Exchange Traded Funds (ETFs), Separately Managed Accounts (SMAs), High Frequency Trading (HFT), and alternative investments have been and will continue to be sources of investment progress. Investors resist innovations because they conflict with prior habits or they seem unduly complex, or because the investors lack information or they don’t appreciate the potential value of the innovative product. But investor resistance to quantitative investing can be lowered by credible advisors who communicate, clearly, the potential advantages and risks. A starting point is to begin to understand the causes of consumer resistance to innovations in general and apply lessons learned to propagate the “Gospel of Quant.”

There are encouraging signs on the future of quant investing. Recent surveys of institutional investors indicate that over half of investors surveyed (57%) plan to significantly or moderately increase their allocations to quantitative strategies over the next three to five years. Pensions and institutional investment consultants indicate an even higher appetite for future allocations to quant investment strategies.104 All they need is a little push from their advisors and analysts.

Quantitative investment due diligence is hard work. There are no easy answers or shortcuts. Our expectation is that the “Quants’ Quandary” primer is one installment in a due diligence process.

23 AUTHOR

Richard (Rick) P. Roche, CAIA. Rick Roche has been a Managing Director at Little Harbor Advisors, LLC since April 2013. Rick is also the Founder/AI Analyst at Roche Invest AI, LLC, a consultancy that specializes in research, integration, and promotion of AI and Machine Learning in investing and the use of alternative investment data in the investment management industry.

Rick Roche has 38 years of experience in the investment management and distribution industries. Rick is a Chartered Alternative Investment Analyst (CAIA), Member of the Society of Quantitative Analysts (SQA), and Boston QWAFAFEW (Quantitative Work Alliance For Applied Finance, Education, and Wisdom). Rick Roche holds FINRA Series 7, Series 63, Series 3 (Commodities), and Series 65 (Investment Adviser Representative) licenses.

The opinions expressed in this publication are those of only its author, Richard Roche, and not those of Little Harbor Advisors, LLC. Any errors or omissions are the sole responsibility of Rick Roche.

Sources

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24 Cummings, CEO of Tradebot Systems, 2016 21 “Conditional Correlation”, by Nick Wade, Northfield Information Services, Nov 2009; “There Was Nothing I Could Do – All The Correlations Had Suddenly Gone to One!”, M. Barton Waring & Laurence B. Siegel, Journal of Portfolio Mgmt; “Quantifying the Behavior of Stock Correlations Under Market Stress” T. Pries, D. Kenett, H.E. Stanley, et al., Scientific Reports, Oct 18, 2012. 22 “Systematic versus Discretionary”, AQR, ‘Alternative Thinking, 3Q17. 23 Red-Blooded Risk: The Secret History of Wall Street, Aaron Brown, John Wiley & Sons, 2012, p. 82; “Ten Financial Applications of Machine Learning”, Marcos Lopez de Prado, True Positive Technologies, June 16, 2018. 24 Prediction Machines, A. Agrawal, J. Gans, A, Goldfarb, HBR Press, 2018, chapter 6, pp. 53-69. 25 “A Comparison of Quantitative and Qualitative Hedge Funds”, by Ludwig Chincarini, PhD, Nov 30, 2009, section A Raw Performance, pages 8-13 26 Ibid, “A Comparison of Quantitative and Qualitative Hedge Funds”, Raw Performance, page 9 27 “Statistical arbitrage”, Wikipedia, accessed on 12-11-2018 28 Ibid, “A Comparison of Quantitative and Qualitative Hedge Funds”, Raw Performance, page 8 29 “Global Macro” by Roberto Obregon & W. Brian Dana, Meketa Investment Group, Nov 2017, pp. 7, 12 30 Ibid, “Systematic vs. Discretionary”, AQR, 3Q17. 31 A Man for All Markets, Edward O. Thorp, Penguin Random House, 2017, Appendix D 32 The Quants: How a Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It”, , Crown Publishing, 2010, Chapter 2, “The Godfather: Ed Thorp”, pp. 13-26; “The Riveting Story of Edward Thorp”, by John Maxfield, www.fool.com, Jan 26, 2017 33 CFA Institute, Ben Graham Was a Quant, author Steven P. Greiner, Book Review by Ronald L. Moy, CFA, Greiner cites Graham’s 1949 classic, The Intelligent Investor, which lists seven criteria that defined the “quantitatively tested portfolio”; Warren Buffett in Columbia B-School video tribute to Ben Graham 34 “The Jones Nobody Keeps Up With”, Carol J. Loomis, Fortune, April 1966 35 Ibid, Inside the Black Box, Rishi Narang, page 88 36 “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, William F. Sharpe Source: The Journal of Finance, Vol. 19, No. 3 (Sep., 1964), pp. 425-442 37 Diffusion Of Innovations, Everett M. Rogers, The Free Press, NY, NY 4th ed 1995, pp. 40-45. 38 Ibid, Everett M. Rogers, 4th edition 1995, pp. 261-66. 39 “Innovation Resistance Among Mature Consumers”, T. Laukkanen, S. Sinkkonen, et al., Jrnl of Consumer Marketing, 24/7; 2007, pp. 420-421 40 “Information as a Barrier to Innovation Adoption”, T. Laukkanen, S. Sinkkonen, et al., AU-ZN Marketing Academy Conference, Dec 2007 41 “Picking Winners? Investment Consultants’ Recommendations of Fund Managers”, T. Jenkinson, H. Jones, & J. Vicente Martinez, Jrnl of Finance, Sep 2014; “The Contested Role of Investment Consultants: Ambiguity, Contract, and Innovation in Financial Institutions”, Gordon Clark & Ashby Monk 42 Ibid, “Picking Winners”, page 2 43 Ibid, “Picking Winners”, section 4. Conclusions, page 33 44 “S&P Bull & Bear Market Tables”, Yardeni Research, Inc., Feb 13, 2018, p. 4 45 “Actively-managed, But More Index-like: Infographic: Mutual-fund Portfolios Have Become More Liquid”, University of Chicago “Booth Review”, May 15, 2018 46 Ibid- “Rise of the Robots”, Bloomberg Chart compiled from research from eVestment and Sanford Bernstein, July 28, 2017. 47 Ibid, Inside the Black Box, 2nd ed., p.222 48 Ibid, Inside the Black Box, 2nd ed., p.222; “Demystifying Systematic Macro Hedge Fund Strategies”, Alex N. Kamunya, CAIA, Senior Research Consultant- NEPC, October 2014, p. 49 “The Past and Future of Quantitative Asset Management”, Clifford S. Asness, Founding Principal-AQR, CFA Institute, 2008, p.47 50 Ibid, “A Comparison of Quantitative and Qualitative Hedge Funds”, Nov 30, 2009 51 “Hedge fund fee model morphs from ‘two and 20’ to ‘one or 30’”, by Chris Flood, FT, March 31, 2019 52 “Can the Market Multiply and Divide? Non-Proportional Thinking in Financial Markets”, by Kelly Shue & Richard Townsend, April 10, 2018 53 “Guidance on the Testimonial Rule and Social Media” U.S. SEC – Division of Investment Management, March 2014 | No. 2014-04 54 The Crash of 2008 and What It Means, by George Soros, and; “The Trade of the Century: When George

25 Soros Broke the British Pound”, Robin Dhar, Priceonomics 55 “Want Less-Biased Decisions? Use Algorithms.” By Alex P. Miller, Harvard Business review, July 26, 2018 56 Clinical Versus Statistical Prediction: A Theoretical Analysis and Review of the Evidence, Dr. Paul Meehl, University of Minnesota Press, 1954 57 Superforecasting: The Art And Science of Prediction, Philip Tetlock & Dan Gardner, Crown Publishers, 2015, p.21 58 Human Inference: Strategies and Shortcomings of Social Judgment, Richard Nesbitt & Lee Ross, Prentice- Hall, 1980, p.141 59 “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err”, B. Dietvorst, J. Simmons, & C. Massey, The Wharton School, published in Jrn of Experimental Psychology, 2014 60 Ibid, “Want Less-Biased Decisions?”, HBR, July 26, 2018 61 “When Do People Rely on Algorithms?, JM Logg, PhD, & Prof. Don A. Moore, U-CA-Berkeley, 2017 62 “Facial Recognition Is Accurate, If You’re a White Guy”, Steve Lohr, NY Times, Feb 9, 2018 63 The Intelligent Investor, Benjamin Graham Revised Edition, Feb 2006, p. 8 64 Ibid, “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err”, 2014 65 Paul Tudor Jones, HF Mgr, WSJ, Aug 16, 2016 66 Ibid, “Ten Financial Applications of Machine Learning”, Lopez de Prado, June 16, 2018, pp. 2 & 17 67 Spurious Correlations website, Tyler Vigen, PhD, APPL stock price on Jan 1 vs. SeaWorld visitors 68 “Spurious correlations are Kryptonite of Wall St’s AI Rush”, Robin Wigglesworth, FT, Mar 14, 2018 69 “Equity Risk Premia: An Alternative Approach to Equity Market Beta, , Oct 2013, p. 5 70 “On the Impossibility of Informationally Efficient Markets”, by Sandy Grossman and Joseph Stiglitz, American Economic Review, 1980 (70): 393–40 71 Big Data and Machine Learning in Quantitative Investment, Tony Guida, editor/contributor, John Wiley & Sons, 2019 72 The Unrules: Man, Machines and the Quest to Master Markets, Igor Tulchinsky, Wiley, 2018 73 Ibid, Big Data, “Do Algorithms Dream About Artificial Alphas?”, by Michael Kollo, chapter 1, page 4 74 “The Near-Death Experience of Quant Asset Management”, by Dan diBartolomeo, Northfield Information Services, Inc., July 8, 2013 75 “Quant Hedge Funds Lose Their Allure as Performance Sags”, Robin Wigglesworth & Lindsay Fortado, Financial Times (FT), July 25, 2018, FT source for figures is Hedge Fund Research (HFR) 76 Ibid, “The Near-Death Experience of Quant Asset Management”, page 1 77 Ibid, “The Near-Death Experience of Quant Asset Management”, page 10 78 Ibid, “The Near-Death Experience of Quant Asset Management”, pages 2-3 79 Ibid, Everett M. Rogers, 4th edition 1995, pp. 325-26 80 “Why Do We Still Have Fax Machines? Just the Fax, Please”, by Eleanor Cummins, Popular Science, May 3, 2018 81 Ibid, Everett M. Rogers, 4th edition 1995, pp. 364-69 82 Ibid, The Quants, Scott Patterson, 2010, Chapter 2, pp. 41-42 83 “The $13 Billion Mystery Angels” by Zachary Mider, Bloomberg L.P., May 14, 2014 84 “Cramming More Components onto Integrated Circuits””, by G. E. Moore, Life Fellow, IEEE, Electronics, April 19, 1965, pp. 114-17 85 Crossing The Chasm: Marketing and Selling High-Tech Products to Mainstream Customers, Geoffrey A. Moore, Harper Business Essentials, 3rd., 2013 86 Ibid, Crossing The Chasm, Geoffrey Moore, 3rd ed., page 11 87 “Meet ALVINN, The Self-Driving Car from 1989”, by Andrew J. Hawkins, The Verge, Nov 16, 2016 88 “General Motors EV1”, accessed on Wikipedia on 01/06/2019 89 Ibid, Crossing The Chasm, Geoffrey Moore, 3rd ed., page 21 90 Ibid, Crossing The Chasm, Geoffrey Moore, 3rd ed., page 22 91 Ibid, Crossing The Chasm, Geoffrey Moore, 3rd ed., page 16 92 “Programmed Obsolescence: The Generic Paradigm in Quantitative Equity Investing and Why It’s in Trouble”, by Tony Foley, formerly CIO at D.E. Shaw Investment Management, L.L.C. Nov 22, 2010 93 “Demystifying Systematic Macro Hedge Fund Strategies”, by Alex N. Kamunya, CFA, Senior Research Consultant-Hedge Funds at NEPC, October 2014, page 2 94 Inside the Black Box: Simple Guide to Quantitative and High Frequency Trading, by Rishi K. Narang, John Wiley & Sons, Inc. 2nd ed. 2013, p.222 95 When Genius Failed: The Rise and Fall of Long-Term Capital Management, Roger Lowenstein, Random

26 House, 2000 96 Ibid, Inside the Black Box, 2nd ed., p.231 97 CTA – Commodity Trading Advisor/HFT – High Frequency Trader; BRK.A – Berkshire Hathaway Class A stock symbol 98 Roger Sessions: How A “Difficult” Composer Got That Way, Frederik Prausnitz, Oxford University press, Aug 2002, page 230 99 Ibid, Inside the Black Box, 2nd ed., p.306 100 “A Model of Innovation Resistance”, by S. Ram, Univ. of Arizona, Association for Consumer Research, Volume 14, 1987, pp. 208-212 101 Diffusion Of Innovations, by Everett M. Rogers, Free Press, 4th ed., 1995, pp. 336-39. 102 Ibid, Diffusion Of Innovations, pp. 347. 103 Ibid, Diffusion Of Innovations, pages 325-26 104 “Periodic Survey of Hedge Fund Investor Sentiment”, Credit Suisse Mid-Year Investor Survey, Summer 2017, page 6