The perils of forecasting and the need for a disciplined investment process
I am regularly called on to provide forecasts for economic and investment variables like growth, interest rates, currencies and the share market. These usually come in the form of point forecasts as to where the variable that is being forecast will be in, say, a year’s time or its rate of return. Such point forecasts are part and parcel of the investment industry. In fact, forecasts about all sort of things – from the environment to economics to politics to sport – have become part of everyday life.
Economic and investment-related forecasts are useful as a means of communicating a view, as an input to the construction of budgets and as a base case against which to assess risks and formulate economic policy. But one of the big lessons I have learnt over the years is that relying too much on precise forecasts when making investment decisions regarding the asset allocation of multi asset funds (ie, funds that have exposure to a range of assets like cash, bonds, property, infrastructure and equities) can be dangerous. This was amply demonstrated during the global financial crisis (GFC), but has always been apparent.1 In particular, there is often a big difference between being right – ie, getting some forecast right – and making money. And of course, as Ned Davis has pointed out, for investors the key is to make money, not to be right.
If forecasting was easy I wouldn’t be writing this…
…and you wouldn’t be reading it! We would be very rich and sipping champagne in the south of France (or something like that!). As my first manager used to tell me “forecasting is difficult because it concerns the future”. The difficulty of getting economic forecast right is reflected in the long list of jokes about economists and their forecasts. Here’s some:
- Three economists went target shooting. The first missed by a metre to the right, the second missed by a metre to the left and the third exclaimed “we got it”.
- Economists were invented to make weather forecasters and astrologers look good.
- An economist is a trained professional paid to guess wrong about the economy.
- An economist will know tomorrow why the things she or he predicted yesterday didn’t happen.
- Economic forecasting is like driving a car blindfolded and getting instruction from a person looking out the rear window.
- Economics is the only field in which two people can share a Nobel Prize for saying the complete opposite.
- For every economist there exists an equal and opposite economist.
- Economists have predicted six of the last two recessions.
- There are two classes of forecasters: those who don’t know and those who don’t know they don’t know (J.K. Galbraith).
Hit and miss
Surveys of economic forecasts are regularly compiled and published in the media. It is well known that when the consensus (or average) forecast is compared to the actual outcome, it is often wide of the mark. This is particularly so when there has been a major change in direction for the variable being forecast – such as around events like the tech wreck in the early 2000s and the GFC. This applies not only to economists’ forecasts for economic variables, but also to share analysts’ forecasts for company profits and to most forecasts across most disciplines except those where precise linear relationships apply (where A = B, eg in predicting the date and time of the next eclipse as opposed to the non-linearity in economics and investing and most things people like to forecast where a slight shift in the balance can result in A = B or C or…).
And of course the bigger the call, invariably the bigger the miss. There are numerous examples of gurus using grand economic, demographic or financial theories – usually resulting in forecasts of “new eras” or “great depressions” – who may get their time in the sun but who also usually spend years either before, or after, losing money. For example, the gurus who foresaw a “new era” in the late 1990s – with books like Dow 36,000 – looked crazy in the tech wreck bear market of the early 2000s. And many of those who did get the tech wreck or GFC “right” were bearish years before and would have lost their fortune if they had shorted shares when they first got bearish.
Grand prognostications of doom can be particularly alluring, and wrong. Calls that the world is about to bump into some physical limit, causing some sort of “great disruption” (famine, economic catastrophe – all those sort of things!), have been made with amusing regularity over the last two hundred years: Thomas Malthus, Paul Ehrlich’s The Population Bomb of 1968, the Club of Rome report on The Limits to Growth in 1972, the “peak oil" fanatics who have been telling us for decades that global oil production will soon peak and when it does the world will be plunged into a Mad Max-style chaos. Such Malthusian analyses underestimate resources, the role of price increases in driving change and human ingenuity in facilitating it. And when you’re reading books like those from Harry S Dent about The Great Depression Ahead (2009), The Great Crash Ahead (2011) and The Demographic Cliff (2014), all of which had disaster happening well before now, just recall that there has been a long list of prognostications for a great depression, often linked to a debt-related implosion, the bulk of which turned out to be wrong. Amongst my favourites are Ravi Batra’s The Great Depression of 1990 – well, that didn’t happen so it was just delayed to The Crash of the Millennium that foresaw an inflationary depression, which didn’t happen either. Google “the coming depression” and you’ll find 72.3 million search results!
Psychology and forecasting
Forecasts for economic and investment indicators can be useful but quite clearly need to be treated with care:
- Like everyone, forecasters suffer from psychological biases including the tendency to assume the current state of the world will continue, the tendency to look for confirming (not contrary) evidence in new information, the tendency to only slowly adjust forecasts to new information and excessive confidence in their ability to foresee the future.
- Quantitative point forecasts – eg, that the S&P 500 will be 2450 by December 31 – convey no information regarding the risks surrounding the forecasts. They are conditional upon the information available when the forecast was made. As new information appears, the forecast should change. Setting an investment strategy for the year based on forecasts at the start of the year and making no adjustment for new information is often a great way to lose money.
- In investment management, what counts is the relative direction of one investment alternative versus others – precisely where they end up is of little consequence.
- The difficulty in forecasting financial variables is made harder by the need to work out what is already factored in to markets. And rules of logic often don’t apply. Benjamin Graham coined the term “Mr Market” (in 1949) as a metaphor to explain the share market. Sometimes Mr Market sets sensible share prices based on economic and business developments. At other times he is emotionally unstable, swinging from years of euphoria to years of pessimism. Trying to get a handle on that and presenting it as a precise forecast or a grand market call is not easy.
In the quest to be right, the danger is that clinging to a forecast will end up losing money.
Why are forecasts treated with such reverence?
So why are forecasts seen by many as central to investing? First, many see the world through the rear view mirror where everything seems clear and assume that the future must be easy to forecast too for anyone who has the expertise. Second, and more fundamentally, precise quantified forecasts seem to provide a degree of certainty in an otherwise uncertain world. People hate uncertainty and will try to reduce or remove it however they can. And if we don’t have the expertise, the experts must know. And finally, prognostications of doom can be alluring because investors suffer from a behavioural trait known as "loss aversion" in that a loss in financial wealth is felt much more distastefully than the beneficial impact of the same sized gain. This leaves us more risk averse and it also leaves us more predisposed to bad news stories as opposed to good news stories. Flowing from this, prognosticators of gloom are more likely to be revered as deep thinkers than are optimists.
What to do? Three things to consider
While I like to think that I and my colleagues are better than the market, I know that if we simply relied on point forecasts for key investment market variables (like the share market, bond yields and the exchange rate) to set our investment strategy, it may not be the best way to make money for our clients. So in embarking upon investing, what should one do? In my opinion, there are three things to consider in the light of this note.
First, don’t over rely on expert forecasts. While point forecasts can help communicate a view, the real value in investment experts – the good ones at least – is to provide an understanding of the issues around investment markets and to put things in context. While financial history does not repeat, it does rhyme and so in many cases we have seen a variant of what may be currently concerning the market before. This is particularly important in being able to turn down the noise and focus on a long-term investment strategy designed to meet your investment goals.
Second, invest for the long term. In the 1970s, Charles Ellis, a US investment professional, observed that for most of us investing is a loser’s game. A loser’s game is a game where bad play by the loser determines the victor. Amateur tennis is an example where the trick is to avoid stupid mistakes and thereby win by not losing. The best way for most investors to avoid losing at investments is to invest for the long term. Get a long-term plan that suits your level of wealth, age, tolerance of volatility, etc, and stick to it. Alternatively, if you can’t afford to take a long-term approach or can’t tolerate short-term volatility, then it is worth considering investing in funds that use strategies like dynamic asset allocation to target a particular goal – be that in relation to a return level or cash flow.
Finally, if you are going to actively manage your investments, make sure you have a disciplined process. Ideally, this should rely on a wide range of indicators – such as valuation measures (ie, whether markets are expensive or cheap), indicators that relate to where we are in the economic and profit cycle, measures of liquidity (or some guide to the flow of funds available to invest), measures of market sentiment (the crowd is often wrong) and technical readings based on historic price patterns. The key to having a disciplined process is to stick to it and let the “weight of indicators” filter the information that swirls around financial markets so you are not distracted by the day-to-day soap opera engulfing them. Forecasting should not be central to your process. My preference is to focus on key themes as opposed to precise point forecasts.
Conclusion
It is always tempting to believe that you or someone else can perfectly forecast the market. However, as Ringo Starr said “it don’t come easy”! There are plenty of investors who have been “right” on some particular market call but lost a bundle by executing too early or hanging on to it for too long. The key is to know where expert views can be of use, but stick to a strategy designed to attain your goals and if you are going to actively manage your investments have a disciplined process.
