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market prediction: The prediction conundrum: What creates market cycles

It all starts with a prediction problem. It is a problem because we think we can predict accurately, when in fact, we cannot.

“Credit ratings help facilitate an efficient capital marketplace. They provide transparent third-party information that’s not only forward-looking but standardised for consistency” says S&P Global Ratings on their website. Ratings range between AAA (the highest rating – obligator has an extremely strong capacity to meet its financial commitment) and D (obligator has defaulted on obligations).

Typically, the AAA rating is reserved for a handful of the world’s most solvent governments and best-run businesses. Just before the global financial crises started (around 2007), S&P had awarded AAA rating to thousands of mortgage-backed securities (they were the financial instruments that allowed investors to bet on the likelihood of someone else defaulting on their homes). A AAA paper signalled that there was only a 0.12 per cent probability (or one chance in 850) that it would fail to pay out over the next five years. As the crises hit, S&P’s internal figures suggested that around 28 per cent of AAA-rated CDOs (collateralized debt obligations) defaulted (some independent estimates are higher). That means that the actual default rates for CDOs were over
two hundred times higher than S&P had predicted.

Let’s take another example. In December 2007, economists in the
Wall Street Journal forecasting panel predicted only a 38 per cent likelihood of a recession over the next year. We know what transpired, but this was especially remarkable because the data would later reveal that the economy was already in recession at the time of the forecast!

In 1971, it was claimed that we would be able to predict earthquakes within the decade, a problem that we are no closer to solving fifty years later. And the list goes on.

The ‘prediction problem’ is then compounded because we are so convinced about our ability to predict that we leverage our bets to the hilt.

In 2007, the total volume of home sales in the United States was only about $1.7 trillion, but the total volume of trades in mortgage-backed securities was about $80 trillion. Every time someone was taking out a mortgage worth one dollar, Wall Street was making a side bet worth $50.

Or take nuclear power plants. World Nuclear Association calculated the life cycle costing of different sources of power and concluded that nuclear power is cost-competitive with other forms of electricity generation. Seismologists then looked at past data and built the Fukushima nuclear reactor to handle a magnitude 8.6 earthquake, because “anything larger was impossible”. In March 2011, Japan witnessed a horrible 9.1 magnitude earthquake and the resulting tsunami damaged the plant’s three active reactors. Loss of backup power led to overheating and meltdowns. According to the International Nuclear and Radiological Event Scale (INES), it was a scale 7 major accident (highest possible accident) and the only one other than Chornobyl to date. After the accident, the authorities shut down the nation’s 54 nuclear power plants. The Fukushima site remains radioactive, and some 30,000 people had to be evacuated. The clean-up job will take 40 or more years and will cost tens of billions of dollars.

In the context of markets, this situation (prediction problem compounded by leverage) gets exacerbated by its unique features. Last week, I had written that the marginal/most motivated buyers and sellers decide the pricing in markets; the majority’s opinion does not matter.

Add to this the fact that technological advancements have compounded that problem. Now, algorithms and fast computers rush to execute the trade if the suggested rule is triggered.

Now let’s put that all together. We ‘predict’ that the stock will rise, talk to several people who agree with our prediction, and look at research by analysts who all believe the stock is a Buy. We then ‘leverage’ ourselves to take the maximum advantage. Predictions are based on things that have happened in the past, but things that have never happened before are happening all the time now. When that transpires, the majority of stockholders who were positive continue to be positive, but the one with the biggest leverage (marginal/most motivated seller) is forced to sell. Algos chime in too because they are told to run if certain rules are triggered. The stock falls below its fair value and the cycle then repeats in reverse. That creates the cycles.

So how does one counter this? First, being aware that cycles exist is extremely critical. For a company that is growing its fundamental value (say EPS or FCF growth) by, say, 15 per cent each year, the assumption that the stock price will also steadily rise, at the same rate each year, is fraught with risks. As the two charts below show, we could be following shorter cycles (as in the case of

), or much longer cycles (as in the case of ). Enter at the wrong time, and we underperform for years, if not decades.

jigar1Agencies

jigar2Agencies

Second, applying the reasonableness filter helps. I might believe that iOS offers better privacy than Android, and hence choose to use the iPhone. But I am a potential consumer only until iPhone is offered to me at a marginal premium to Android. If iPhone costs five times the rival’s price, they lose me as a consumer. What we pay for what we buy matters. As consumers, we know it; as investors (when we are buying a business), we sometimes tend to forget it.

Lastly, as human beings, we are wired to recognise the patterns that we see. We may not have many natural defences – we are not very fast, or all that strong. We don’t have claws or fangs or body armour. We cannot spit venom or camouflage ourselves. And we cannot fly. And yet, we are at the top of the natural food chain, and a baby can recognise the basic pattern of a face within months of birth. It’s not the learning of the individual, it has been learned through evolution. Our investment experience would be much better if we let evolution take over and recognise how these cycles have always existed and incorporate that into our journey instead of fighting it.

(The author is
Co-Founder of Buoyant Capital)

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