Why is higher return higher risk

Can risk and return be predicted?

An overwhelming number of empirical studies that have been carried out since Samuelson's essay show that it is hardly possible to usefully predict price developments. But what about the forecast of risks?

Risks can be predicted with greater accuracy than returns

In 1963, the mathematician Benoit Mandelbrot was the first to report that the range of fluctuations in course changes has a certain system: Large fluctuations are usually followed by large fluctuations and small fluctuations are usually followed by small ones. This phenomenon, known as volatility clustering, has a more decisive impact on investing than one might initially assume. Because this means that today's risk is indicative of the risk to be expected for tomorrow. The risk dynamics are fundamentally different from the structureless ups and downs of prices, because the probability that there will be increased volatility in the market tomorrow is greater than 50 percent if the markets were already very volatile today. Risk forecasts do not behave like random coin flips. Or, in the words of Samuelson: Market risks are not martingales.

The financial economist and Nobel laureate Robert Engle was the first to research methods for the systematic modeling of volatility clusters and to develop corresponding methods for risk forecasting. The knowledge that volatility forms clusters over time is particularly useful because it makes it easier to “circumnavigate” phases of increased volatility. As already mentioned, investments with higher long-term risks usually have a higher potential return. However, positive excess risks are typically associated with below-average price developments. The cluster behavior of excess risks allows future risk behavior to be forecast with a certain degree of reliability. If the risk forecasts for an asset class indicate positive excess risks, its portfolio share can be reduced accordingly and the risk can be dampened.

A plea for considering risk-adjusted returns

Since different investment strategies come with different risks, it does not make sense to simply compare different strategies on the basis of their historical returns. Ultimately, risk is the “currency” with which investors acquire potential returns. Therefore, comparisons of returns should use the riskiness of investments, rather than comparing apples with oranges.

A sensible approach is to consider the so-called excess return, i.e. the difference between the investment return and that of a risk-free investment, and to calculate how much excess return was achieved per "currency unit" used. The so-called Sharpe ratio, the quotient of excess return due to volatility, was proposed by the finance economist and Nobel Prize winner William Sharpe as a benchmark for comparing alternative investment strategies. The Sharpe ratio is a measure of a risk-adjusted return that allows the returns of strategies with different risk profiles to be compared.

Risk-based portfolio management

At Scalable Capital we understand that performance must always be seen in the context of the risk of the underlying investment strategy and that the individual risk preference of an investor is the starting point for a tailor-made portfolio of the best ETFs (what are ETFs?). The proprietary risk management model of Scalable Capital heed both. Recommends based on his prior knowledge and experience in dealing with financial market instruments, his personal circumstances and his individual investment goals Scalable Capital one of 23 possible risk categories for each investor. The investor can choose the recommended option or a lower-risk option. The numerical value (3 to 25), which designates a risk category, reflects the maximum percentage loss, the so-called Value-at-Risk (VaR), which will not be exceeded with a probability of 95 percent over the course of the year. That means: On average, the VaR limit should only be broken once every 20 years.

Scalable Capital regularly monitors the risks of all client portfolios and checks whether they match the individual risk category. If the risk projection created using simulation analysis signals that the risk deviates from the VaR limit specified by the investor, we adjust the portfolio accordingly. For example, if the limit is imminent, risk is automatically reduced by reducing the proportions of asset classes with high excess risk; If the value falls below this, however, we increase the proportion of riskier asset classes in the portfolio. This does not mean that we immediately act frantically with every correction. But we adapt the portfolio to the market situation if it changes permanently. This keeps the risk under control in the long term. Diversification effects are always taken into account.

Schematic representation of a risk-based portfolio adjustment

If the risk projection signals that the risk limit has been exceeded (left graphic), the risk is reduced by switching to lower-risk investments (right graphic)

This systematic adjustment of the portfolio means that our investors are not directly exposed to the fluctuations in risk on the financial markets, but rather have an investment risk that is as constant as possible and within their risk tolerance. Not only does this increase the likelihood of generating better risk-adjusted returns, it also lets the investor sleep better.

You can read more about the topic here: Dynamic risk management.
1: https://www.institutional.vanguard.co.uk/documents/case-for-index-fund-investing-uk.pdf/

Benjamin Graham, investor and mentor of Warren Buffett: "The essence of investing is managing risk, not managing returns," and that understanding is central to successful investment management.