"Science is nothing but trained and organized common sense…" (Thomas H. Huxley, 1875)
"As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality." (Albert Einstein, circa 1947)
"A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it." (Max Planck, 1948)
"It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong." (Richard P. Feynman, circa 1985)
Over the years, I have been asked many times why I went into a finance career after starting out as a scientist and practicing geologist. The short answer is that I didn't mean to do it: I made a mistake and undertook a first career that (although I loved it) was ultimately so cyclical in nature that I eventually got taken out by one of a series of severe economic downturns in my favorite sector, energy. The long answer I will save for another occasion. Anyway, I have often thought about what a wrenching experience it was to change from a field where presumably logical and independent, objective thinking are rewarded, to one where objective thinking about completely irrational behaviors in many other people is rewarded. Not that scientists are actually rational themselves, because they are humans first and scientists second, so they come with all the faults and errors to which humans in general are subject. Rather, scientists are trained to be more rational, on average. I (quite possibly) changed from right-brain dominant activity to left-brain dominant activity, at least for a while, after I changed careers, and it was not easy. But I made and continue to make many investing mistakes, just like everyone else I've ever met. Mine tend to be due either to the usual human factors to which we are all prone, or to my early training and experience in science. That is, I sometimes try to be logical about inherently irrational things like the markets, when the better policy is to be merely objective about what particular behavior is observed, regardless of whether it makes any economic or financial sense.
George Washington Carver
Certainly though, I have learned over time, ironically in both science and investing, that almost nobody is completely rational for any meaningful length of time. Instead, in my opinion, people have episodes of rationality, and they have subject areas where they are more rational than otherwise. I have met many people who are rationalists, especially in the professions (science, math, engineering, law, or medicine). These often include people who believe they are generally quite rational and objective under a wide range of circumstances. However, behavioral science does not support their claims (more on that later); in essence they have not resigned from the human race, nor can they do so by willpower alone. So, unfortunately, the human zoo, while fascinating to watch, is not always a pretty sight to behold. My thesis here is that it pays to be skeptical of consensus, of received wisdom, of impressive credentials, even of data, and this skepticism can make a big difference to you as an investor. I have a few anecdotes and examples that I will use to try to illustrate what I mean by this, and why it is relevant to investing.
Many years ago, I published two scientific papers that thoroughly debunked two regional research studies published by a world famous geophysicist ("WFG") who was working on plate tectonics, my chosen dissertation topic. This guy wasn't just world famous - he had helped to originate the theory of plate tectonics (I should add that my Ph.D. advisor had also been one of the earliest workers in that field and was equally famous, which gave me the backing I needed to go wherever my ideas and research took me). In a series of meetings at international scientific conferences, I presented several papers that made "WFG" at first uneasy, and then extremely uncomfortable. This is because I seemed to be disagreeing with his most recent publications both in writing and in public presentations and discussions.
Because I was new to the field, "WFG" initially thought he would uncover mistakes in my thinking or my methodology, a not unreasonable assumption given our relative credentials, so he decided to openly question my results. This is not in any way unusual in science, so I was prepared for it. When it was my turn to speak, I used his own data and methodology to provide evidence that I was correct in my interpretations, which by implication meant that he was wrong. This naturally enough did not go down very well. However, I was a bit of an iconoclast and was ready to take him on however great his reputation, if I could prove I was scientifically sound in my thinking. It was critical to my own work that I sort this out, so I did.
My antagonist, "WFG," who really was a great and venerable scientist, but by then somewhat set in his ways, had perhaps become a bit lazy or careless in his full maturity, which I believe is what led to our differences of opinion on the proper interpretations of these regional studies. He had a real dilemma though in our brief public debates, because of the fact that I was quoting him, and using his own data and his own pioneering methodology to make my case. This was simply because for some reason he had chosen to ignore his own earlier works, while I, being relatively inexperienced, thought I should follow the great man's methods very carefully to avoid making obvious blunders (Please note also that the work that made him famous was not being questioned at all).
There was little he could say that was going to sound rational then since he would be caught in a trap of his own making. And not too surprisingly, given human nature, he completely went off the rails when he responded, on several occasions. That didn't go too well for him, because he was then hoist on his own petard in the ensuing debates; in one case 300 people openly laughed at him in response to debating points I made at his expense. It is not at all unusual for scientific debates to be vigorously fought, but it is very unusual for a junior person to take on a very august and much respected person in open debate. I should point out that my advisor, who as I said was himself world famous in the same field, fully supported everything I did.
Anyway, at another scientific presentation I made later in the same year, my venerable antagonist "WFG" told me I would never get my next study published because he was very certain I was wrong in everything I had claimed; however, I had already published the new study in a prestigious European journal, so I merely handed him a reprint as a rebuttal. He then became somewhat frustrated and told me he had reams of unpublished data that would negate my entire approach, and then stormed out. At which point, a long-time, accomplished and equally venerable colleague of his stepped forward and said quietly, "I know he is wrong - there are no such data and he damn well knows it." Nor did it ever appear.
I tell this somewhat long-winded story because it taught me a very valuable lesson that I've had occasion to use repeatedly in investing: i.e., do not accept ideas, or theories, or even data based on who presents them; that is, based on how famous they are, or what tremendous credentials they have. It doesn't matter if they went to Harvard or Oxford, or if they work for JPMorgan (NYSE:JPM). Rather, examine their evidence yourself and make a judgment call of your own. No one is inherently right at all times, no matter how wonderful they are in general. As I can testify from personal experience, having met a couple of them, even Nobel Laureates put their pants on one leg at a time. And the reciprocal statement is also true: just because someone has been wrong about something important, doesn't mean that they are forever wrong on all subjects. For that reason, I occasionally read the works of people I know are wrong-headed in general, simply because every once in a while, they see something useful that I did not.
Returning to the idea that behavioral studies have shown that professionals are often not rational, I will make mention here of recent work done by psychologist and economics Nobel Laureate Daniel Kahneman. In a recent talk at the Wharton School, he said he had been consulting to various businesses and medical groups, and although he had expected to be "awed" by their decision-making prowess, he was not in fact "awed." He pointed out that large organizations have many procedures and internal guidelines that are "stupid," with "really poor thinking you see all around you." He cited disturbing evidence about the professional judgment of experts: "You put the same x-ray in front of radiologists, and about 20% of the time in some experiments they don't reach the same diagnosis." I would also mention, now that I'm on the subject of physicians, an anecdote that famous thinker Nassim Taleb presented in one of his books. He had a cancer diagnosis, and after a follow-up screening following treatment they pronounced him "cancer free." But they had made a simple error which he doubtless pointed out to them: they couldn't see any cancer, but "absence of evidence is not evidence of absence." People can die when this kind of mistake in thinking occurs. This same human capacity for error applies to all of the professions.
Looking back on my second career (as an investment and wealth advisor), what seems most important now is that counter-intuitively, there have actually been some skills that I obtained as a scientist that have been useful in investing. As I have evolved into a more experienced investment analyst and economics dabbler, I have found my training in skeptical or critical thinking about everything I have heard and read, to be just as useful as it was in science. There are probably also some traps in that way of thinking that must be guarded against; the main one may be the danger of "paralysis through analysis." To me though, the things that seem to be the more positive aspects of how scientists approach problems include: the fact that because they are trained to be skeptical, they tend to seek as much data as possible; to then sort that data vigorously as to quality, separating the wheat from the chaff; to proceed towards conclusions by taking account of what is known and what is not known; to use multiple working hypotheses; and lastly to apply Occam's Razor whenever necessary.
The following example illustrates how I applied this lesson on an issue of great importance in investing. You may reach a different conclusion than I did, but my decision served me well. The issue at hand for me in 2004 and 2005 was what to do about all the evidence I was seeing that there was something wrong with the way Modern Portfolio Theory ("MPT") worked, or in how it was applied. Some months ago, I wrote an article for Seeking Alpha about this that included the following statement (annotated in bold, within brackets): "A basic tenet of "CAPM" (a major component of "MPT") is that risk and reward are directly proportional. This means that as risk increases, so does reward. However, a study in the late 2000s by JPMorgan has shown just the opposite trend for real world data. In other words, when 20 years of actual market (the Russell indexes) data through 2008 were plotted, they indicated a strong linear relationship between risk (standard deviation) and reward all right, but it was reciprocal. Thus, if risk increases in the real markets, reward can actually decrease (for periods of time)."
"Indeed, Eugene Fama and his long-time colleague Kenneth French published a paper in 2004 (which I read in 2005, causing me much rethinking of my approach to investing), showing that for the period from 1923 to 2003, using all stocks on the NYSE, AMEX and NASDAQ, the highest risk (highest beta) stocks considerably underperformed relative to the predictions of the CAPM. The reverse was also true, in that the lowest risk (lowest beta) stocks considerably outperformed relative to the predictions of the CAPM (see Chart 1). Over the long run, there was essentially no relationship between beta and stock returns (This astonishing result has also been discussed in detail by famous value investor James Montier). Yet another study was conducted a few years ago by Jeremy Grantham of GMO Asset Management, who found that for the 600 largest U.S. stocks (for the time period from 1963 to 2006), those with the lowest beta have had the highest returns. James Montier has studied the risk-return relationship for European stocks for the period from 1986 to 2006 as well, with essentially the same result."
Chart 1: Fama and French Data on Risk and Reward ("CAPM")
This discovery that there were problems with the "CAPM" that even Eugene Fama recognized completely changed my whole approach to asset allocation. After further research, I found enough evidence of problems with "MPT" in general that I abandoned it as a theoretical construct or framework for decision-making. Gone from my client presentations and allocation decisions were all references to portfolio optimization, "MPT," "efficient markets," or "CAPM." And in my opinion, this was just in time. Firms I had worked for were heavily reliant on "MPT" in all of their recommendations to clients, such that I felt I had to leave them to be able to properly advise my personal clients. I did leave, and my new firm allowed me to do my own thinking.
Just two years later, the biggest financial crisis and market meltdown since the 1930s struck, but I was well prepared by then. For one thing, because I was skeptical, I did not accept the consensus of the time, which was that the troubles in 2007 and early 2008 would blow over. I instead accepted the wise advice of people like John Mauldin, Gary Shilling, David Rosenberg, John Hussman, and a few others, who all had taken note of the yield curve inversion in 2006 and did not flinch from what it implied (Chart 2). I had also rejected "MPT" long before then, as already discussed, so I was free to allocate as I thought the macroeconomic risk situation required. Thus, in a series of moves starting in the fall of 2007, I had gradually reduced equities and added bonds as the stock rally peaked, fizzled a bit, and then rallied to a secondary peak in the spring of 2008.
Chart 2: Yield Curve Inversions Trump Bull Market Enthusiasm
When both my former firm and my then-current broker-dealer recommended a move to very high-equity allocations (based in part on "MPT") in the spring of 2008, I was thus moving the opposite direction, and was in full bear market defensive allocations long before Lehman Brothers collapsed. It was simply that there was so much evidence by then (if one was able to hear it above the cacophony of the roaring bulls) that something big was wrong, I just couldn't accept industry sell-side propaganda. Just to illustrate how silly some of this was, although not in any way unique, see Chart 3 regarding earnings projections below. I did well for my clients in that instance; obviously, that says nothing about the value of my future advice, but it does say something about the value of reaching your own conclusions. Of course, some might say that "MPT" had nothing to do with why people lost so much money in 2008-2009, but I will simply answer that "MPT" over-promised and under-delivered for a great many people, and the reason is simply that the assumptions underlying the theory are rarely in full operation or valid. Ask the big Wall Street firms that relied on VaR how that worked out for them. It didn't - in fact it blew them up.
Chart 3: Projected S&P 500 Earnings Vs. Reality
Source: Lance Roberts; Realinvestmentadvice.com
We face a hurricane of swirling data, contradictory trends, shouting bulls and worn-out bears now, similar to what we saw in 2008 and 2000. Although there are more people with doubts this time around, many of them have long since gone to cash. For those who remain, it is imperative that we all examine the evidence coldly, with a clear eye and a skeptical mind. A lot may depend on it.
Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
Additional disclosure: This article is intended to provide information to interested parties. As I have no knowledge of individual investor circumstances, goals, and/or portfolio concentration or diversification, readers are expected to complete their own due diligence before purchasing any stocks or other securities mentioned or recommended.