At the Blink of an Eye

Imagine yourself in your car on that country road. You are a bit late for your business appointment. Have you brought everything? It is an important presentation! Trees are flying by while you recite to yourself the opening of the speech you are going to give… or not going to give, as round that next corner – out of nowhere and right on your lane – this ridiculous tractor suddenly blocks you! Somehow, in a reflex action, you miss the tractor. But now you are heading exactly towards that massive alley tree. An instant later, you are dead.

Or you aren’t. At the blink of an eye, the airbag has saved your life!

Now that you are not dead, lean back, relax and blink your eyes. Doesn’t take long, does it? It takes us only 100 milliseconds (1/10th of a second) to blink an eye, the airbag to save our life, or modern stock markets to grow or wipe out all our money.

At the blink of any eye, millions of trades are made at the stock exchange, over and over again. No longer by loud-shouting floor brokers or erratically gesticulating floor traders, but by machines.

Wealth Management is Already Digital

Digital wealth management is not the future; it is the present. Advisors, fund managers, bankers who still think of clients’ money as just being “OPM” – other people’s money – and have not implemented financial technology for the benefit of the customer, will soon be a thing of the past. For the financial industry, the driver to implement such technology is shifting from increasing their own net returns with things like high-frequency trading (HFT) to confident and savvy clients who now demand better investment solutions for themselves. After all, it is their money that is at risk. And that risk can finally be managed not just by so-called experience (or gut feel), but by modern risk analysis, backtesting, artificial intelligence (AI) and many more FinTech tools that are available now.

Nowadays we do not buy cars without ABS, seat belts, head rests or airbags. We even opt for more electronic safety features that are possible today: blind-spot detection, adaptive headlights, collision warning. Why should we risk our lives by ignoring modern high-tech in transportation? And why should we risk our hard-earned savings in wealth management by ignoring what next-level robo-advice has to offer:

1. Fact-based algorithms, replacing emotion-triggered discretionary trading, avoiding also influences from greed, fear and panic.
2. Precise, more efficient, automated execution of (ideally algorithm-based) trading and investment strategies.

Before we look more at (a) and (b), the top level of WealthTech solutions, let us quickly summarize the different stages digital wealth management has offered so far. This view is similar to what Deloitte concluded in their 2016 report “The expansion of robo-advisory in wealth management”.¹

Clients could enter an online form and then be presented with a so-called risk or investment profile, and could pick recommended products accordingly. This was the first level of robo-advice, while the second level selects the products for the client. The third level, basically what most popular robo-advisors now offer, is a more dynamic form of bundling the products.

Is level-three robo-advice better or worse than human advice? In reality, it is pretty much the same, as the computer simply matches the profile to a portfolio predefined by humans, so no rocket science there. Also, the fact that mainly exchange traded funds (ETFs) are bundled and no active, direct investment or portfolio management in equities is being provided is often criticized. ETFs also come with external and internal costs, which clients are often not aware of.

The fourth level of robo-advice and digital wealth management goes way beyond bundling off-the-shelf products. Algorithms, from robust and simple to highly complex cutting-edge technologies, build – or are – the investment product or solution. The algos create the portfolio, react to terabytes of historical and/or live data, and ideally execute the orders at better fill-prices and lower costs for the client. Let’s take a closer look.

Financial Algorithms – Pillar One of Next-Level Digital Wealth Management

Building wealth is easy. We buy something – stocks, funds, real estate, gold, oil, etc. – at a certain time and we sell it later at a higher price, and then repeat. Losing wealth is even easier. Often we have to sell at a lower price and/or at a much later point in time.

Top-level digital wealth management should replace discretionary decisions (speculation, hope) with fact-based, reproducible rules (algos). The goal is to end up with more realistic expectations in terms of performance and, even more important, risk. Be aware, bad algos can lose all your money as fast as bad human advisors can.

In stock trading, the never-ending discussion is how we (humans) could predict better which stock will go up. We base such guesses on things like trading experience, fundamental analysis, value investing, portfolio theories, efficient market ideologies, sentiment analysis or technical analysis. One, some or all of these approaches can be part of WealthTech, not just technical analysis (a big misconception). The important part is to use the facts (science) in combination with practical experience. Machines are crucial, especially for AI, but after all they are not much more than tools to prove which investment approach (mostly ideas from experienced traders, portfolio managers and quants) is going to work and which one isn’t!

Backtesting is one element which helps to dig deeper and quantify any idea or rationale behind a certain idea by testing it against real historical data. The most important goal is not to find the holy grail (which you won’t), but to see what wouldn’t have worked; what would have led you to lose money? Once you see that 99 out of 100 investment approaches fail at a certain time or market condition, even those that sound logical or that one can find in dozens of investment books, you really understand the power of backtesting and stop risking your own and your clients’ money until you can quantify risk realistically.

In modern times, we use internet search and GPS navigation to find a restaurant and not a printed street map from the 1980s. Those GPS devices are very high-tech without us even noticing it – GPS requires quite a lot of satellites in space and calculates locations using Einstein’s gravitational deflection of light theories – and we have all of this on our smartphones. Many discretionary investment approaches are like the street map from the 1980s or earlier (like the so-called “modern” portfolio theory from 1952). It may or may not work; 99 out of 100 times it simply doesn’t.

Automated Execution – Pillar Two of Next-Level Digital Wealth Management

If the right strategy (or algorithm) is key, how important is a flawless, quick and efficient execution of that strategy (i.e. the actual entries and exits at the stock exchange)? What would you say: does 95% depend on the strategy itself and 5% on the execution, or is the execution’s relevance greater or less?

Let us crash into that alley tree again, with our car on our way to the appointment. What did the airbag electronic in our car need to do to save our life? The first part was like the strategy or algo part, if we want to compare it with our trading analogy: a couple of sensors reported some data which the onboard computer interpreted correctly as a crash. In trading, we would have received a buy or sell signal for a stock, but in our car we got a “release that gas immediately” signal for the valve that shot the gas into the airbag.

If the opening of that valve, in other words the execution of that crash signal, had not worked or we had been delayed by just another 100 ms, we would not have survived that crash despite the correct decision to activate the airbag. The same is true for our money; it is lost too, if we do not enter and exit exactly and as efficiently as planned, due to the lack of precise, reliable execution.

HFT is not only a buzzword, it negatively affects every single stock position being entered or exited “normally” these days, no matter whether you are buying 10 shares for yourself or you want to accumulate a million shares for a pension fund. Front running, one example of HFT, can mean that a good strategy ends up with a loss of −5% that year rather than the profit of +5% fair fills would have produced (no matter whether based on a discretionary or algorithm-based strategy).

The real-world outcome of modern investment and trading strategies can depend 25% to 75% (or more) on its execution part alone, so it is not enough just to know what to do, but to do it precisely and efficiently. Times of ad-hoc orders in the heat of the moment on a trading floor – or giving your broker a casual telephone call after lunch – are over.

Digitizing Wealth Management Progresses Rapidly

Robo-advisors that match online-entered personal risk profiles to a set of bundled ETFs were just the beginning of digital wealth management. Financial super algos and smart, precise order executions are the future, a future that some WealthTech companies already provide today.