My starting point is that, one way or another, investing is and will remain a fundamentally human activity.
To be more specific, investing is and will remain a cultural phenomenon that has evolved in a way which contributes to the growing role of intelligence in the universe. Fairly obviously, that evolution is itself evolving different tool sets to help do the job. Quantitative modelling has developed a whole sert of tactics, strategies and expressions which are new branches on the tree, but the tree remains fundamentally the same.
That the task has remained fundamentally the same is not obvious: as each new tool has arrived, intelligent people have feared that the game is changed. (Here's how Robert Mayo, then head of the Chicago Fed discussed with the FOMC the rise of the options market in the 1970s: 'My elbow is still uneasy about this. There is a counterculture, if you want to distinguish it that formally, developing now with the evolution of puts, that will counterbalance the calls, and the market will be better again. But this is getting into real mystique. I don’t think anyone really knows what he’s talking about.')
Even when computer-driven trading represents the majority of stockmarket activity, I'm prepared to take it as axiomatic that investment will remain a fundamentally human activity. That's partly because no matter how much data is fed into the algorithms, market pricing will retain its ability to discover information which can be discovered in no other way (Hayek's thesis). Indeed, if computer programs alone were able to remove unprogrammable surprise element from pricing, then there would be essentially no purpose to markets in the first place - we'd be better off simply allowing the computers, not markets, to allocate resources. We know how that ends, and it's not well (ironically, we might say the data is in on it.)
The ultimate impact of computer-driven trading in all its variety is mitigated by the enduring fact that at both the top and bottom of the investment-decision tree, humans get involved. At the bottom of the tree, we have humans making decisions about whether to invest and how to invest. Having made the decision to save, and save in equities, what governs the choice between single stocks, mutual funds, ETFs, family offices, hedge funds, or CDFs and derivatives of various kinds? Price, fashion, advice?
When an institutional vehicle is chosen, the way that institutional vehicle runs itself is constrained by irreducible human factors, including fashion, 'respectability', theoretical justification (portfolio theory etc). the regulatory environment, and the sensitivities of investors and trustees. And finally, at the top of the investment decision, the entire investment environment is affected by the decisions of the 12 people on the FOMC. History provides abundant evidence of human intelligence and fallibility in their decisions.
Essentially then, investing is a human activity, and therefore an expression of human thought.
Which brings us to Daniel Kahneman and Amos Tversky and the book 'Thinking, Fast and Slow'. My guess is that you'll have recognized the reference in this piece's title. Kahneman's book gave birth to the field of behavioural economics and in its wake there have been a large number of usually unsuccessful attempts to deploy the insights of behavioural economics (ie, loss aversion etc) into investment strategy.
But it may be more useful to consider what he has to say more widely. At the heart of 'Thinking, Fast & Slow' is the description of two types of thinking. System 1 thinking is fast thinking, what we do, very often unknowingly or instinctively, to make snap decisions about situations we encounter all the time. By contrast, System 2 thinking is slow and deliberative, demanding an expenditure of energy in conscious effort. (And System 2 thinking is expensive: as someone said, 'Most of the world's problems can be solved with five minutes thought. But thinking is hard, and five minutes is a long time.')
It strikes me that since investing is an essentially human activity, we should understand that the results will always represent a mixture between the results of System 1 thinking and System 2 thinking. The suggested corollary is that if you rely solely on System 1 thinking (ie, you are a day-trader) you will sooner or later get beaten out of the market by the results of System 2 thinking/investing. But it's probably no less true that if you rely only on System 2 thinking, investing only in line with your carefully worked-out strategic insights, you will spend a lot of time getting killed by the 'irrational' activities of System 1 day-traders (who might well turn out to be algos anyway).
In other words, if investing is a fundamentally human activity, you need to have an approach which acknowledges and incorporates both System 1 and System 2 investing. You need to think about Investing, Fast & Slow.
Investing Fast & Slow: Fast
I don't mean frantic day-trading, chasing micro-trends before they evaporate. Rather, I mean working to create insight into what drives System 1 thinking about investment. If a portion of market-pricing at any one time is the result of System 1 thinking, we should expect it to be the result of impressions and feeling and instincts which eventually surface as the explicit beliefs expressed in the deliberate choices of System 2 thinking.
But these impressions and feelings will not be random. To quote Kahneman System 1 type thinking 'has also learned skills . . . and understanding nuances of ... situations. Some skills . . . are acquired only by specialized experts. Others are widely shared.' The approach I adopt, then, is to work out what generates those impressions and feelings, and the understandings they generate. If my beliefs about the inputs into System 1 investment thinking are correct, it opens the possibility that a careful tracking of those inputs will allow me to understand and anticipate the results of System 1 investment choices.
This is what I have done, using as feedstocks the shocks & surprises indexes generated by my unusually broad and detailed tracking of global economic data. The very name 'shocks and surprises' conveys the hint - that these are precisely the data which System 1 type thinking is likely to notice and react to. Yet as Kahneman warns 'the automatic operations of System 1 generate surprisingly complex patterns of ideas,' and the intuitive belief that movements in asset prices will simply mimic the trajectory of shocks & surprises indexes turns out to be wrong. Such a simple interpretation will, alas, lose you money.
Nevertheless, further exploration has borne fruit. Alternating investment choices among the world's major stockmarkets using models approximating System 1 thinking has allowed a consistent and ultimately rather spectacular outperformance of the MSCI World Index since I started this at the beginning of 2014.
Since that period, the MSCI World Index has gained 25.2%, whilst my model has gained 80.7%. The average weekly gain for the MSCI World Index during that period is 0.11%, with a standard deviation of 1.74%; the weekly average for my System 1 model is +0.26%, with a standard deviation of 1.89%.
Conclusion? Investing Fast isn't about turning over your portfolio at rapid speed, it's about understanding that System 1 thinking is part of how market prices emerge in the short term.
Investing Fast & Slow: Slow
Investing: Fast gave some hints as to how we can explore the likely market impact of System 1 type of thinking about economies and markets. The long-term results, I think, speak for themselves, but they are the result only of cumulative short-term investment tactics. System 2 type investment thinking aims at improving longer-term strategic positioning, allowing a longer investment horizon, less costly in terms of day-to-day effort and cost.
If the energy devoted to analytical and deliberative thinking about investment were easily repaid, the ‘strategic advice’ industry would be even bigger than it is.
Conversely, and ironically, it is the very sustained failure of these attempts that keeps the industry afloat: if anyone ever managed to think their way to consistent investing success, they would keep very quiet about it. But the history of technology demonstrates that key breakthroughs often have multiple discoverers who enjoy the same Eureka moment quite independently of each other around the same time. So even the most determined IP lawyer would be unable to stop the market-busting breakthrough finding a wider audience. At which point, there would be no point in markets anyway, since everyone would ‘know’ the information the market discovers beforehand.
So far, so negative. However, whilst no System 2 process will deliver a strategic method guaranteeing 100% success, there are economic tools which which quantitative analysis confirms do add value, systematically raising your chances of being invested in the right places at the right time. Here I’ll introduce two of them (albeit in conceptual terms only) which have survived the quant analysts’ autopsies: first, return on capital; and second, profits.
Both sound not mere familiar but bone-headedly obvious: why would anyone think of investing in equities if they didn’t have a view on return on capital and profits? Answer: because they’re macro-economists, and macro-economics historically has been relatively incurious about returns and profits.
Taken together these two not only help form the foundation of understanding where a particular economy is in its business cycle, but also, of course, builds a basis for comparison between the position and likely medium-term trajectory of other economies competing for your investment.
Return on Capital
I construct a Return on Capital Directional Indicator (ROCDI) by treating GDP as a flow of income from a stock of capital, and as that income fluctuates relative to the stock of capital. This is a simple idea, in macroeconomics related to Incremental Capital Output Ratio (ICOR) and in equity research to the asset turns (Sales/Total Assets) element of the Dupont Analysis decomposition of return on equity.
The key difficulty is estimating the size and growth of capital stock. I make an estimate by depreciating the gross fixed capital formation line-item in quarterly national accounts over a 10yr period. Whilst such a straightforward approach refuses to engage in a number of obvious (and interesting) questions, in practice the results concur closely with the results of more scrupulous surveys, where available.
Crucially, these calculations are made using only nominal data (rather than real): ‘real’ counts of investment in capital goods seem to me to be hopelessly compromised by a) the issues arising from the Cambridge Capital Controversies; and b) the key characteristic of capital goods is that their price fluctuates dramatically with business cycle, but in balance sheet accounting terms they remain on the books at purchase cost. That difference between capital goods market price and book price is, quite obviously, a key element in the business cycle dynamics we want to understand.
The result is that we can see not only the fluctuations in trends in return on capital, but also how investment activity fluctuates (and is likely to fluctuate) in response.
Here, for example, is how it looks for the US.
Getting a grip on profits is less simple, but one can treat changes in corporate profits, or corporate saving, as a function of the changes in net investment plus the changes made in savings flows from the household sector, the government sector, and the ‘rest of the world’. To put it crudely, one sector at a time:
if the household sector’s savings ratio (defined as compensation minus consumer spending) diminishes and everything else is held constant, then corporate savings (ie, profits) will rise by the same amount;
If the government’s net (dis)savings position changes (ie, the fiscal deficit rises or narrows), and everything else is held constant, then corporate savings will fall or rise by the same amount;
If the rest of the world’s saving position with the economy (ie, the trade balance in its wide description) changes, and everything else is held constant, corporate savings will fluctuated by the same amount.
This is, in fact, nothing more than an economic accounting identity. To my knowledge, it was first articulated by a Polish Marxist-influenced economist Michal Kalecki in the 1930s, who initially used it to analyse the exploitation of the workers. Whilst it is not necessarily always easy (or possible) to calculate it with certainty, the results are extremely useful in observing the trajectory and force of business cycle dynamics.