The focus on ESG investing continues to intensify in every major market around the world. One big reason for this is a greater recognition of the financial impact of factors traditionally regarded as non-financial, things like the risks around cybersecurity and customer privacy, the global pressure on resources such as water, and the disruptive effects of a transition from fossil fuels to renewable energy sources. The list is long.
But the recognition of financial impact is not the only thing driving change. There’s also a growing awareness of the central role played by the investment industry in the economy and wider society and of the responsibility that brings. The investment process does not take place in a vacuum; investment decisions have impacts. The collective actions of investors have consequences, for good or for ill.
Many people would like to see those consequences considered in the investment process. Others disagree. Opponents point out, for example, that there is no universal agreement on what broader goals are desirable. And it’s true that humanity does not all share the same values or the same vision of what a better world would look like. Then again, the differences are pretty small compared to the overlap. Consider, for example, the seventeen sustainable development goals set by the United Nations General Assembly in 2015, wide-ranging goals such as good health, sustainable production patterns, and energy that is affordable and clean. Those goals have an associated 169 targets (“by 2030, reduce the global maternal mortality rate to less than 70 per 100,000 live births”, “substantially reduce corruption and bribery in all their forms” and so on). And, since these 169 targets required international consensus, there’s not much on the list that could be seen as controversial. So there’s plenty of scope to define broader goals that are widely accepted.
The more substantial argument against non-financial objectives is that they must necessarily come at a price. Because at first sight, it might seem that any attention paid to social or environmental goals must – even if only by a tiny amount – compromise financial ones.
After all, portfolio construction is, at heart, an optimization process. It’s about finding the mix of securities that has the highest utility or the highest risk-adjusted return or whatever other measure you like to use for the portfolio that is more likely to meet your financial goals than any other. It’s the highest point. And there’s only one highest point.
The highest point on a utility curve is not a Matterhorn-like sharp tip, though. It’s more like the top of a gently rolling hill. A very gently rolling hill. I’ve provided an example of optimization at the end of this article. In that example, moving 1% of the portfolio from stocks into bonds (or vice versa) reduces the risk-adjusted return of the optimal portfolio by one two-thousandth of one per cent. To describe that difference as immaterial hardly does its smallness justice.
And if you feel that sub-optimal means sub-optimal whether the difference is one per cent, one two-thousandth of one per cent or one two-billionth of one per cent, let me remind you that investment is a social science, not a physical science. No model of the market is even close to precise enough to justify treating its output as anything other than one of many possible good answers. The inputs to the process are at best educated guesses and the uncertainty around them exceeds by a huge margin the loss of utility that results from small changes in the portfolio.
That’s why no serious investor really believes that there’s a single right answer to the portfolio construction challenge.
And behavioral finance, for once, provides encouraging news. It turns out that while the human cognitive process is in many ways not ideal for financial decision-making, the pursuit of multiple objectives comes very naturally to human beings. We have no problem – most of the time – aspiring to life, liberty and the pursuit of happiness.
So some consideration in the portfolio construction process of environmental or social goals is possible without automatically compromising the financial side. The scope to pursue these goals is not unlimited, of course; where there are multiple goals, then at some point they come into conflict. But, up to that point, the goals can co-exist.
In short, environmental or social objectives do not necessarily have to come at the expense of financial goals.
Endnote: Optimization, sub-optimality and margins of error
The following example is an optimization exercise at its most basic. It is based on historical average returns, standard deviations and correlations of US stocks and US bonds, using the data of Aswath Damodaran of NYU Stern at http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/histretSP.html. This data covers the 92-year period 1928-2019:
Stocks: 9.55% a year return, with a standard deviation of 19.49%.
Bonds: 4.89% a year return, with a standard deviation of 7.67%.
Stock-bond correlation: -0.01.
Under these assumptions, a portfolio consisting of 60% stocks and 40% bonds has an expected return of 7.69%, and an expected standard deviation of 12.06%.
To optimize the portfolio, we also require a risk tolerance parameter to define the desired trade-off between risk and return. If we set that parameter to .8785, for example, we can calculate the utility, or risk-adjusted return, of the 60/40 portfolio as:
.0769 – .1206 ^ 2 / .8785 = 6.03%
It can be shown that any other portfolio mix produces a lower expected risk-adjusted return. If, for example, 5% of the portfolio were to be moved from one asset class to the other, the utility of the portfolio would fall to 6.02%. If stocks are the overweight asset class, then both risk and return are increased. If bonds are overweight, both risk and return are decreased. In either case, utility falls.
Hence, under these return assumptions, the 60% stock/40% bond portfolio turns out to be optimal for an investor with that particular risk tolerance.
The key point here, though, is that in order to reduce risk-adjusted return by one-hundredth of one per cent, we have had to move fully 5% of the portfolio between stocks and bonds: fundamentally different types of asset. If we were to move 1% of the assets, the loss of utility would amount to just one two-thousandth of one per cent.
Meanwhile, the assumptions themselves contain margins of error many times larger than this. You almost certainly didn’t worry that I rounded the return assumptions to one hundredth of one per cent in my example; even that level of precision is probably unjustified. And if we were to base our return inputs on the data history up to 2018 rather than 2019, then the average return on stocks would decrease by .22% and on bonds by .05%. In other words, the margin of error in our return assumptions far exceeds the loss of utility that is caused even by a material shift in asset allocation. Similar statements could be made for the standard deviation and correlation assumptions. And the margin of error in the risk tolerance assumption is, if anything, even larger.
All of which means that good is good: but optimal is not necessarily better.