It would be an understatement to say that Campbell Harvey is a prolific and accomplished finance researcher:

Harvey received the 2016 and 2015 Best Paper Awards from the Journal of Portfolio Management for his research on distinguishing luck from skill.  He has received eight Graham and Dodd Awards/Scrolls for excellence in financial writing from the CFA Institute.  He has published more than 150 scholarly articles on topics spanning investment finance, corporate finance, behavioral finance, financial econometrics, and computer science.  He edited the Journal of Finance — the leading scientific journal in his field and one of the premier journals in the economics profession — during 2006–2012.*

That paragraph is from the preface to an interview, “Examining Quantitative Investment Strategies,” with Harvey that appeared in the Journal of Investment Consulting (a publication of the Investments & Wealth Institute).  The conversation touched upon a remarkable number of important issues at the intersection of theory and practice; some of them are referenced below.

Economic foundations

Despite all of his other work, Harvey is best known for his early research in which he found that the term structure of interest rates is “a reliable forecaster of economic growth.”  Thus, he has garnered quite a bit of attention lately as parts of the Treasury yield curve have become inverted (although not his specific comparison of three-month bills to ten-year notes), admittedly for a model that he called “pretty simple,” with just one variable.

He discovered it after first thinking that equities would prove to be a better predictor:

The stock market seemed like an ideal indicator because we would expect future economic growth to drive anticipated cash flows for companies.  But in the case of equities, a lot of other things are going on.  As a result, there are many false signals.

The distinctive feature of the current economy is an inflation rate far beyond any thought possible by central bankers and market participants.  In the interview, recorded in March 2021 but published more than a year later, Harvey presaged the implications:

Our real interest is in those periods when the inflation rate goes from 2 percent to over 5 percent.  Across these episodes over the past ninety-five years, the real U.S. equity return (which takes inflation into account) is -7 percent on an annualized basis.

We really care about an inflation surprise.

And we have gotten that in spades.

Corporate (and investor) behavior

Harvey founded the Duke CFO Survey, which has collected information from corporate chief financial officers for a quarter century.  A landmark paper stemming from it, “The Economic Implications of Corporate Financial Reporting,” revealed the destructive nature of the relationship between corporate decision making and the demands of investors:

Because of the severe market reaction to missing an earnings target, we find that firms are willing to sacrifice economic value in order to meet a short-run earnings target.  The preference for smooth earnings is so strong that 78% of the surveyed executives would give up economic value in exchange for smooth earnings.  We find that 55% of managers would avoid initiating a very positive NPV project if it meant falling short of the current quarter’s consensus earnings.  Missing an earnings target or reporting volatile earnings is thought to reduce the predictability of earnings, which in turn reduces stock price because investors and analysts hate uncertainty.

Harvey said it was the paper of his that has had the most influence.

Diversification and rebalancing

Professional investors reference the core precepts of academic finance, although their shorthand adaptations of them often ignore important context and caveats.  Take the use of Sharpe ratios:

Even today, investors tend to compare the Sharpe ratios of different strategies and ignore other dimensions of risk.  In Markowitz’s 1952 Nobel prize-winning paper, he acknowledges the assumptions he is making.  One of the assumptions he clearly spells out is that the model does not work if there is a preference for higher-order moments — for example, a skew.

We know that investors dislike downside risk, and we also know that asset returns are not distributed symmetrically.  So it is important for asset managers to explicitly integrate the downside risk or skew in portfolio design.  A lot of portfolio designs do not take skew into account; consequently, the portfolio manager has to rely on risk management as a second process.  I have long advocated that risk management and portfolio design should be integrated.

Harvey expands on that later in the interview:

The downside needs to be taken into account.  Consider two investments:  one with a high Sharpe ratio and one with a lower Sharpe ratio.  The higher Sharpe ratio investment may not be the better choice because it might have a giant downside tail.

Yet we see Sharpe ratios routinely used by professionals as the means of comparison across managers and strategies.

Harvey references the two pillars of investment finance — diversification and rebalancing — and says, “These concepts are so basic that almost everyone in finance thinks they understand them.”  As noted above, he questions the appropriateness of common approaches to diversification, and has cautionary words about rebalancing practices too:

The first result is that mechanical rebalancing to 60/40 induces bigger drawdowns.  The reason is simple. In a persistently falling market, buying to rebalance increases the magnitude of the drawdown and introduces a negative convexity.  Buying in a falling market is akin to dynamically replicating a short put option.  Conversely, selling in a rising market replicates a short call option.  Put the two actions together and the outcome is a short straddle, which has, by definition, negative convexity.  So, rebalancing is like adding a short straddle to a 60/40 portfolio and that short straddle induces extra risk.

That concept is expanded upon in a paper, “Strategic Rebalancing,” and book, Strategic Risk Management, which he co-authored.  The recommended course involves using trend-following to strategically time rebalancings:

Importantly, waiting to rebalance does not cost anything.  The models we present are incredibly simple — models for three-month momentum or twelve-month momentum — and are straightforward to implement.  The payoff is substantial in that the size of the drawdown is reduced when compared with the drawdown from using a mechanical strategy.

Research methods and incentives

There has been a blending of academic and practitioner research in finance, so it’s hard to draw a clean line between the two (to wit, Harvey is associated with Research Affiliates and the Man Group).  But there are different incentives and potential problems to address with each.

“The replication crisis” has become a focus across academic disciplines.  Harvey has addressed it specifically in regard to finance in a paper, “Replication in Financial Economics,” and he argued that “it is likely that more than half of our empirical findings are false.”  The pressure to get research into top journals leads to “data mining and publication bias. . . . The incentive to find a factor that works is extreme.”

He thinks that the standards that have led to the so-called “factor zoo” need to be rethought:  “If 400+ factors can clear this hurdle, we need a higher hurdle.”

One problem is that a researcher can’t wait around for out-of-sample evidence:

We know that stock returns are quite volatile, and the signal-to-noise ratio is really low.  Twenty years might not even be enough.  Again, researchers need to consider prior beliefs in the context of their being based on economic fundamentals.  We simply do not have enough signal.  The researcher has to be disciplined by economic theory.  That is a way to minimize the effects of data mining.

One problem is that academics ignore “real-world frictions, such as trading costs.  With reasonable trading costs, the effect may go away.”  Astonishingly, Harvey says, “Even with the research on 400 factors, no study (that I know of) includes trading costs.”

Including such costs in the analysis is a key difference in the approach of practitioners, who “pour over what is posting on SSRN” to see what ideas might be commercialized.  But, while research might be passed around among academics, that’s not the case at asset management firms:

Replication is going on within the industry, but no one is interested in sharing it because they want their competitors to waste resources doing the replication too.

While data mining is a problem, it is less so than in academia:

The reason is simple:  When a researcher who data mines moves the strategy into live trading, the strategy likely is going to fail.  That means that the company loses reputation, loses the assets under management, and does not earn a performance fee.  For asset managers doing quantitative research, incentives are aligned.

But there are firms that pursue a different strategy:

Some exchange-traded funds, however, are thrown together on the basis of an academic paper.  The manager pitches the fund to clients and explains that the strategy is based on peer-reviewed research.  Some managers will promote hundreds of these types of funds, knowing that more than half the strategies are probably not going to perform well, but collect the fees anyway.

For all involved in coming up with quantitative investment strategies, caution is warranted:  “While we have great data and the latest machine learning tools, there is still endemic overfitting.”

Crowding

Crowding — when an asset manager “is not establishing boundaries, not imposing limits” — is a problem for investors:

The asset manager takes more capacity than is feasible for a particular strategy in order to get more assets under management.  The average alpha is lower as a result.  Again, it is a matter of incentives.  Although crowding is important, measuring it precisely is difficult. . . . That discipline is necessary for an investor to make any excess return.

A paper on that topic connects it to the trend toward team-managed funds, in that “adding new managers brings fresh investment ideas, which implies that any individual idea is less crowded,” and “diversification of team skills is important for reducing the impact of fund size on performance.”  (Forming those teams is something that deserves greater attention within asset management firms.  As Harvey says in the interview, “If the team is just a replica of the original solo manager, adding managers is not much help.”)

Other topics

~ Manager selection:  Harvey believes that “a small proportion of mutual funds are able to outperform benchmarks after fees” and that “it is hard to find those funds.”  He thinks that skilled managers move on to hedge funds where the “rewards are a lot greater” — and there’s a better chance of delivering good performance.  (A view that’s open for debate.)

~ ESG:

I am nervous about a company selling an ESG investment product that promises “ESG alpha.”  Asset managers point to 2020 and say, “Look, these ESG-friendly stocks did really well.”  True, prices went up in 2020, appearing to validate the ESG investment thesis, but what does that mean?  We know that often as prices go up, expected return goes down. I think many investors will be disappointed.  Plenty of greenwashing is going on.  Some managers are more concerned about getting investors to allocate capital to their funds than the quality of the products they are offering.

~ Cryptocurrencies and decentralized finance (DeFi):  Harvey has been early to study these areas, and thinks that bitcoin, for example, should be considered as a potential holding in an asset allocation because of its stated market capitalization, although he says, “arguments I hear about why bitcoin should have a very high value do not make a lot of sense.”  The emergence of DeFi means that “we have come full circle,” returning to a peer-to-peer, barter system.  It offers “enormous” possibilities, including tokenizing strategies like private equity and eliminating intermediaries throughout the financial system.  (Harvey is one of the authors of a recent book, DeFi and the Future of Finance.)

~ According to Harvey, “Short-termism is a fundamental problem with our political system and with the way businesses are run.”  (And with much investment decision making.)

*Harvey was also given the JPM Best Paper Award in 2022, and received his ninth Graham and Dodd Award/Scroll from the CFA Institute.

The previous posting kicked off two series — one devoted to the book Talent by Tyler Cowen and Daniel Gross, and the other (which will grow to be quite extensive over the coming months) about the new world of work in the investment business.

A key theme in the book is that talent is often improperly valued, providing opportunities for those who can get past structural barriers and behavioral biases to see the possibilities.

Human differences

A short opening section of one chapter uses Greta Thunberg as an example to

reveal important lessons about how people with “disabilities” can be startlingly effective not because they “overcame” their disability but because of it.

Thunberg describes herself as having Asperger’s syndrome, which she has in common with some notable investors.  Michael Burry, one of the key figures in The Big Short, said, “Only someone who has Asperger’s would read a subprime-mortgage-bond prospectus.”  Then there are the two most famous fixed income portfolio managers.  Jeffrey Gundlach claimed to be “obsessively regimented in my analysis, borderline autistic,” and Bill Gross believes his Asperger’s diagnosis “helps explain not only why he was such a successful investor for so long but also why he could, by his own admission, rub people the wrong way.”

That dichotomy is what gives people pause and what leads to those misvaluations.  Yet:

To the extent that autistic and Asperger’s individuals remain outside of the usual loops of social pressure and mimetic desire, they may retain strong capacities for original, non-conformist thought.

The authors provide context for the use of the word “disability,” including a “possible” definition:  “human differences in range and/or abilities, which are currently judged to impair essential aspects of functioning, regardless of actual outcomes or achievements.”

What is often missed:

Many individuals respond to that initial deficiency by investing more in acquiring other, different skills.  Disability is thus a potential marker for skill specialization, and skill specialization can be a very potent advantage, most of all in a world that is rapidly becoming more complex.

Common misconceptions about different conditions abound, so education is the first step in preparation for interviewing people who are (or are perceived to be) out of the norm — and for creating a work environment where they can thrive.  The book uses those on the autism spectrum to examine the competitive advantages (even “superpowers”) that they can bring to an organization, as well as the potential challenges.  With that kind of balanced understanding, you can evaluate a range of possibilities, including the use of remote work options that might better fit the needs of an employee.

The authors’ bottom line on this topic also sets up the ones to come:

At the very least, please consider and internalize the general lesson that you should not let stereotypes dominate your thinking.

Gender

As other professions have become more balanced between men and women over the last few decades, decision making roles in the investment world have remained dominated by men.  That’s true even though the evidence from study after study is that women in general produce better risk-adjusted returns.

The italics above were used because while there are differences across genders in various attributes, the authors stress that looking past those to the qualities of specific individuals is key, since there are situations where “the returns on talent spotting are going to be high, because other people are too attached to their statistical discrimination.”

If you look at the scores for the Five Factor personality model, there are different ranges by gender — from their scores, “whether a person is male or female can be forecast with about 85 percent accuracy.”  And the evidence shows that personality traits matter more in the evaluation of women than men.  That leads the authors to conclude that “the talent search for women is more difficult or requires a subtler set of skills,” so that “you can take advantage of other people’s prescriptive stereotypes.”

Among the tendencies to be aware of:  people have a harder time being criticized by a woman; female voices more often cause negative reactions; and women are “asked to walk an almost impossible middle line in the workplace,” where they “are supposed to be tough but not too tough, firm but not obnoxious, like men but not too much like men.”

In general, women have different communication styles and lower self-confidence than men.  “Yet labor markets often reward confidence, sometimes even excess confidence.”  (That’s something rather noticeable among investment folks, and the authors cite “evidence from economics . . . that the gender confidence gap comes mainly from male economists making proclamations about areas they don’t know much about.”)

Notably, men aren’t very good at reading and understanding different personality styles — or judging the intelligence of women — and “many talent-spotting mechanisms are more geared toward males, [so] it is easier for super-talented women to go unselected.”

It is, at this late date, still easy to find investment firms at which women appear only at the margins (just as was the situation seven years ago when this essay was written); whatever the reason for the narrow pool of talent, it is likely to hurt those organizations over time.  Cowen and Gross:

It is better for everyone — yourself included — if you side with an emancipatory perspective that improvement is possible and you can be an agent of change.  This holds even if you harbor very strong conservative views about the intrinsic differences between the sexes.

Race

Misperceptions and poor valuations come into play regarding differences in race too, which can be even trickier:

Our first piece of advice — and we mean this for individuals of all races — is not to pretend that you understand race as an issue very well.  Don’t approach the problem, and the issue of bias, with some pet theory about how the world works with respect to race, because the diversity of racial issues, problems, and biases likely will defeat your schema.  Mostly, as an outsider, you want to shed many of your preconceptions, whether explicit or implicit ones, and open yourself up to the talent possibilities in minority communities, particularly communities you may have no connection to personally.

Again, the authors recommend learning above all else, putting yourself in new environments and exposing yourself to content that is foreign to you.  There are knowledge gaps to be closed (or at least narrowed) before you can begin to value the talent available with an objective perspective.

Consider how the lack of familiarity can play out in the search process:

To the extent there is a cultural gap between whites and blacks (or other groups) in an interview setting, it is a common strategic response — on both sides — to take fewer chances.  To be less natural.  To tell fewer jokes.  To reveal less about one’s personal life.  And so on.  It is thus harder to move into the highly productive conversational mode [discussed earlier in the book].  The end result is that you — even if you have no prejudices in the narrow sense of the term — are less likely to see the true talent strengths of the people you are talking to.

Similar dynamics might affect another talent-selection endeavor.  Many minority-owned investment firms and funds have trouble raising assets, even when their results are good (and even though some consultants and asset owners have programs designed to foster them).

Otherness

No doubt there are additional dimensions of otherness that could fit into this paradigm.  Early in the book, the authors reflect on the past fifty years:

Prejudices were — and still are — distorting many of our talent allocation decisions.

We struggle to value otherness, in the hiring process and in the operation of our organizations.  As with the investment process, identifying talent and properly valuing it necessarily depends on the ability to get past traditional methods and deep-seated biases.