Category: Analytics

If you entered the world of work after 2008, your experience is probably one of sustained growth where things have only gotten better.

Since the financial crisis, which resulted in markets falling to a low in 2009, the world has seemed to be on a path to recovery. Stock markets have risen, houses prices have remained stable and many people are employed.

The FTSE, shown in the chart above, has risen from under 4,000 in 2009 to over 7,000 today, an increase of 75%.

But, as Santayana said, those who cannot remember the past are condemned to repeat it.

We live in a world where valuation and value are not the same thing.

The dot com crisis at the start of the millenium resulted from ascribing absurd valuations to companies that were little more than dreams and wishes.

The financial crisis in 2008 happened when complex contracts designed to spread risk turned toxic and concentrated it instead, corroding trust – the grease of modern economies – and bringing the whole financial system to a grinding halt.

And now we come to 2017.

Is the steady rise we have seen over the last seven years because of a real recovery or because of something else?

The internet has grown up after all – it’s now transforming how we live our lives and do things. So perhaps the new digital connected economy is helping economies change and grow?

An alternative explanation is that governments have pumped vast sums of money into the system to keep things working.

This money is injected through financial institutions and has to go somewhere – it has to be “invested”. So the money goes and accumulates in certain places – companies, commodities, bonds that promise returns.

The issue is that there is more money chasing these assets that have returns, and so people are willing to pay more for them, and as a result their valuation increases.

Since BREXIT, the UK index has risen because a weaker pound makes the largest businesses in the UK more competitive globally, increasing expectations that their profits will rise and pushing up their valuations.

Conversely, when the exchange rate drops, the valuation of these businesses falls as well – the FTSE 100 is inversely correlated with interest rates.

So, what should you do now? Should you sell everything and walk away from markets? Should you double down and buy some more?

As Blaise Pascal said (almost), all of humanity’s problems stem a person’s inability to sit quietly in a room alone.

The markets will go up and down. Given where they are, it makes sense to be prepared both psychologically and financially in case they fall once again.

The thing to remember is that percentage changes are not symmetric. A 75% rise only needs a 43% fall to wipe out all your gains.

At this point, it might make sense to invest with a mindset that is prepared to have your investment take over 10 years before it shows a profit.

If you were rational, this is how you might make a decision:

1. Set out the alternatives – what are the choice you have?
2. For each choice, what is the payoff – what are you likely to get?
3. Again, for each choice, what is the probability that it will happen?
4. What is the expected value of the option (probability x payoff)

With choices that lead to other possible choices, you need a decision tree and the ability to work out sequences of expected value.

You then choose the approach that results in the highest expected value.

This approach, however, is not intuitive, and most people are not wired to approach decision making in this way.

In addition, it’s a little old. The statistical basis for this approach lies in the work of Thomas Bayes in the mid-1700s. Our knowledge of people has moved on a bit since then.

There are two situations people face often when making personal and business decisions.

The first situation is when they know the chances of winning or losing.

For example, lets say you entered a game where you could win £10 or lose £5 on a coin toss. There is a 50% chance of either, and you might be tempted to take a punt at this level.

Most people would not take the bet if the option was between getting £1,000 or losing £500. The fear of losing would overwhelm the prospect of winning.

The other situation is when they don’t know what might happen and the risks that could emerge.

Quite often, the next thing to go wrong is completely different from the ways in which things went wrong before – and all the planning and controls that were in place to avoid the last disaster fail to prevent the next one.

A more human approach to making these decisions is based on Plausibility Theory and in particular the idea that you may take a risk as long as your downside is capped.

In other words, you may be willing to take a decision that you expect to be profitable, as long as the loss if you are wrong is limited to a certain level.

This approach became popular around 15 years ago as the concept of Value-at-Risk (VaR). Using this approach, you put in place a management system that ensures that you limit your loss to a particular level, say 1 or 2% of your portfolio value, and then work to get the most profit out of the opportunity.

So… you avoid the ugly end result, limit the worst case to a bad result and work on achieving a good result.

But… mathematicians ruin everything.

VaR was quickly adopted in many financial models – from standard portfolio markets to energy, and complex models were used to justify the products that were being introduced. They even formed part of the Basel II rules used to regulate international banking.

Which then failed rather spectacularly to prevent the global financial crisis that kicked off in 2008.

Although arguably that was down to smart people who figured that they could use the methods to try and take greater risks with other people’s money while at the same time reducing their own personal risk to almost nothing.

After all, how many executives of banks have been tried and convicted for their role in the crisis?

So it looks like the people managing your money applied Plausibility Theory rather well, except they did it to benefit themselves rather than you.

The takeaway is perhaps that the next time you make an investment decision, figure out what will happen to you if things go horribly wrong before being enticed by the promises of future returns.

We had the dot com bubble at the start of the millenium and the financial crisis in 2008.

Where is the next bubble building and when could it pop?

Markets are now highly correlated – information is plentiful and most people know most of the same things about what is going on, and so make pretty much the same decisions about what, when and how much to buy.

Whether it’s shares, bonds or commodities, the large players all have more or less the same approaches and strategies.

And that leads to a problem.

If everyone were suitably diversified, and held enough different things, then no one thing should be enough to cause a crash.

But this isn’t how it seems to work in practice.

The financial crisis showed that all the major financial players were exposed to the same kinds of toxic products that they didn’t understand.

Like elephants in a rowing boat, when they all tried to get to the other side to escape, the whole thing tipped over and was in danger of sinking.

Governments had to step in and bail them out.

Apparently its easier to let markets blow bubbles and pick up the pieces when everything falls apart than it is to try and stop them before they get out of control, according to Alan Greenspan, once the U.S Federal Reserve Board Chair.

Since bottoming out after the financial crisis in 2008, stock markets recovered steadily and hit new highs.

Many investors, wary of overvaluated stock markets, began piling into bond markets.

That has led to higher bond prices and lower yields. Many bond yields are in fact negative in real terms.

The actual yield, however, is not always the main criterion. Having bonds in your portfolio acts as a hedge against the stock component.

If you could have had a 100% stock portfolio and it would have fallen by 50%, you might feel relatively happy if you actually had half your portfolio in bonds, and the actual drop was 25%.

Bonds have traditionally been your friend when things go wrong.

It’s not at all clear whether high valuations are a threat to markets.

Some people say that if interest rates rise, then all bets are off, markets will crash and values will fall.

Others say that just having valuations move higher is not the problem – the risk comes from increased lending for dodgy purposes.

Or in a slightly more technical words from Jim Chanos, a prominent short seller, “Bubbles are best identified by credit excesses, not valuation excesses.”

The existence of new and risky lending practices, however, is often hard to pinpoint until after they have wreaked havoc on the system.

Until then they are likely to be hailed as the best thing since sliced bread.

The rule of thumb is that a crisis happens every decade and a depression every seventy or so years.

Ten years after the last crisis, we might do well to be wary.

The world’s largest corporations are increasingly investing in projects to try and use data for better decision making in their businesses, to improve how they work from marketing to operations.

Why is it then that nearly three-quarters of big data projects are unprofitable?

One explanation is put forward by Tricia Wang in this TED talk.

We use data to help us understand how the world around us works, and we hope that this understanding will help us predict what is going to happen in the future.

But, depending on how we approach the idea of data, this results in different tactics by different organisations.

The “hot” approach is the one of big data.

Everything is connected – the internet of things (IOT).

Data is collected automatically, recording everything from your browsing history to when your toaster turns on and off.

Tricia Wang has invented the term “thick data” for an approach to collecting data by observation – something done by the likes of ethnographers and anthropologists.

This is a modification of the term “thick description” that tries to explain behaviour and the context in which the behaviour takes place.

So, in big data, computers collect information from customer “touchpoints” – the places where you interact with the machines.

In thick data, people collect information by observing and interacting with other people.

Tricia’s example of how this results in different outcomes is the case study of Nokia.

The huge amount of data collected by Nokia from its customers and market research failed to alert them to the possibilities of the smartphone.

Tricia’s research showing that low-income Chinese people were willing to spend half their monthly income on buying a phone convinced her that the smartphone would take off.

And we all know what happened to Nokia when the iPhone took over the world.

In a big data world more is better – sample sizes are huge.

We collect millions of data points and store these in the cloud.

Tools like IBM’s Watson help you analyse and evaluate this data for not just quantitative insights but also, through natural language processing, for emotional components and behavioural predictions.

With thick data, we have a small number of data points.

Someone has to spend time with people, observing what they do, where they do it and draw conclusions on what that means for the future.

Big data helps you quantify the world.

All the measurements you take give you the ability to look at how people interact with your business in a level of detail beyond anything that was possible before and express this in numerical terms.

Thick data helps you explain why people do what they do.

Taking time to watch and interact with people gives you insights into the way they think and behave and, crucially, what they might do next.

The point is that it is not an either-or situation.

Using just big data is not enough.

Combining the power of big data to quantify and the power of thick data to explain can give you a better understanding of the situation.

Take a simple example of thick data in action.

If you have watched The Social Network, you’ll remember a scene where Zuckerberg is trying to figure out what to do with his system.

Over a drink, his friend comments that it would be great if people had a badge that said there whether someone was single or attached.

Zuckerberg has a flash of insight and adding that feature to facebook causes subscriptions to rocket.

In other words – if you work out how use both big data and thick data in your business, you are more likely to be able to better understand and predict the future.