I have always enjoyed maths; I like the precision and the certainty it provides. At university I chose to study subjects such as maths, statistics, econometrics and financial mathematics alongside economics. Going into the City after university I was sure that these skills would provide some advantage, particularly as around that time computers were moving from being academic tools to being available on office workers’ desktops. This meant we could now build complicated models of the financial world.

Over time reality has shattered those naïve early expectations. One of my first experiences in the fund management industry was the aftermath of the 1987 crash, where people were questioning why the portfolio insurance models actually led to the crash they were meant to protect against.

A few years later we had the rise and fall of LTCM. The ‘brightest finance PhD’s’, including two Nobel prize winners, had been gathered together to run a hugely leveraged hedge fund based on really complex financial models that showed they were taking minimal risk. Once something occurred outside their models, in this case the Russian financial crisis, the firm went rapidly bust, nearly taking the entirety of Wall Street down with it.

I was working at the time as an analyst, building beautiful and complex forecasts and valuation models for the stocks I was charged with following. While satisfying, all this energy being expended was an exercise in creating false confidence and certainty, as was starkly highlighted by the ‘Dot Com bubble and crash’. Analysts were projecting confidently the future for companies and their models were providing the evidence for huge valuations. Ultimately reality trumped the models, and we had the dot com crash. One prominent victim of this was Enron, whose use of ‘mark to model’ accounting was one factor in its demise.

A period of relative stability prevailed in the aftermath of the ‘dot com’ crash, but this period was one during which a large amount of financial innovation was taking place. Mortgage markets had been deregulated in most jurisdictions and these newly created mortgages were being packaged into collateralised mortgage obligations, which themselves were then sliced up into tranches and sold off as very low risk investments. Even the riskiest mortgages, which involved no income appraisal, (known even at the time as ‘liar loans’), could be packaged up as low risk assets on the basis that that the risk of a diverse portfolio of such loans was less risky than any individual loan. Banks were being encouraged to make loans into less privileged markets by regulation, so the stage was set for a housing bubble and subsequent crash.

The same was true of CLOs and CDOs (collateralised loan obligation and collateralised debt obligations); poor credits could be added together into pools to turn the result into a strong credit, the maths proved it. Sadly, the maths ignored the fact that they might all go wrong at the same time, due to the existence of economic and market cycles. I saw firsthand how a supposedly low risk asset could almost instantly become worthless because something occurred that the model didn’t allow for.

Much more recently we had the UK pension fund crisis, where the pension funds had been borrowing against their, supposedly, low risk long dated government bonds, in order to invest in more attractive ‘higher return’ assets. A great way of enhancing returns, whilst still meeting their obligation to hold long duration assets to meet their regulatory requirement, the models proved it. This all worked very well until the spike up in bond yield worldwide, and the lenders called in their collateral. This led to forced selling of UK government bonds, and the Bank of England having to step in to prevent a rout.

Why am I writing about these historical events now? As a consequence of our advocacy for using natural income as a pre and post-retirement strategy, we often come up against the objection that unit encashment and natural income should give the same outcome, if the investment returns are the same. This is true, the maths can show this very simply. The problem is that modelling of the past, making assumptions about future returns and volatilities can only give false certainty about the future. These numerous examples from my career show the risks of relying on assumptions put into clever financial models. The income a portfolio is generating today is a known fact, and it has a tendency to grow over time.

This chart shows the dividends and price of the MSCI World Index, during volatile times it demonstrates that dividends were much more reliable than capital values.

Dividends have been much more consistent than capital values.

Source: Bloomberg 29.12.1995 – 28.06.2024.
Past performance is not a guide to future returns.

The natural income a portfolio generates looks to be a much more reliable basis for a comfortable retirement than a reliance on the output of financial models, however clever and sophisticated.