Times have changed, however. For example, back in 1987, this process was not automated. We calculated 200 relative strength charts by hand each week. Through the use of technological advancements, today we calculate more than 10 million relative strength charts every single night.
And that sort of technological advancement is critical, because better, faster software means that, within minutes, a fundamentally recommended list of stocks can be loaded into a platform, and relative strength can apply to the whole list to identify which of those fundamentally-sound names are technically sound.
Over the years, this type of analysis has helped identify which asset classes have been ranked at the top and which have been ranked at the bottom.
Most platforms' screening and ranking capabilities look at six broad asset classes:
- US equity
- International equity
- Fixed income
- Commodities
- Currencies
- Cash.
For example, our platform showed that, from the end of 2011 until early 2016, US equity was the top ranked asset class, although volatility in early 2016 resulted in US equity losing its grip on the top spot for a few months last year.
By the end of 2016, US equity was again the top-ranked asset class in our platform, having achieved the top position in mid-August.
Going deeper
Meanwhile, by the end of 2016, markets generally reached all-time highs. But only an in-depth analysis of the underlying sectors can help managers and advisers work out where the great performance has come from.
By peering deeper into the US equity market, it is clear that the technology sector, along with energy, financials and industrials, are coming out as strongest from a relative strength perspective, while healthcare and real estate are at the bottom of the rankings.
When thinking about constructing portfolios today, the platform ('robo') generated relative strength analysis would suggest that investors want to be overweight in US equity, and in particular those aforementioned top-ranked sectors.
These observations, however, would not be actionable for clients if portfolio managers and financial advisers could not fully see the context - historically and behaviorally - against the other, cross-asset underpinnings of the markets, and set against the individual client's own financial needs and aspirations.
This is why any outputs from platforms should be incorporated into the research as objective recommendations to clients.
This is where human and machine elements can be seen as working in step together to achieve the best possible result for the end client.
For example, we have worked with tens of thousands of advisers over the past 30 years and over 8,000 advisers are actively using our platform today to help guide investment decisions.
