ETF’s have been a net positive for the market over the past couple of decades. They provide a cheap, direct, and easy way for investors to build exposure to the wider equity or bond markets, or a particular segment defined by types of stock, specific sector, countries, commodities, etc.. Unsurprisingly, ETF’s assets under management have grown at a blistering pace, from $3.4t in 2016 to over $10.0t by the end of 2021 and, according to a survey conducted by pwc, they are expected to reach around $20t by 2026 – that is nearly half of the entire stock market capitalization in the US.
However, they are a mostly static instrument operating in a very dynamic marketplace. ETFs, particularly the most popular index-tracking ones, are hardwired at conception and trade whenever the index manager modifies its members or weights. Which has some benefits – portfolio management and administrative costs are minimal which leads to thin fees, and trading costs and inefficiencies are very low.
This makes ETFs ill-prepared to react to changes in the market. Despite the multiple benefits of ETFs, these instruments do not react to material changes in the market. The simplest example is when one of its components significantly outperform over a short period of time – the regularly active investor would consider realizing profits and moving the proceeds to a different position, whereas the ETF will just keep the position “as is.” The same principle can be followed for changes in macroeconomic conditions, valuations, company-specific news, etc.
Our ETF Optimizer combines the benefits of ETFs with an active stock selection process that follows these market dynamics. We understand that some investors look for ETFs as means to “buy” a certain market direction – for example, investors motivated by growth could invest in Vanguard’s VUG ETF or investors looking for attractive valuations may prefer the VOOV. That is a big part of a credible long-term investment strategy, and we look to maintain that. Nevertheless, instead of looking at the entire roster of 100s of names, we select the 10 most likely to outperform the ETF’s universe over the next month.
We re-calculate our preferences every week, anticipating the next-month’s relative performance of the entire ETF’s roster. We feed our model 100s of variables over the trailing decade, and let the machine learn which and how stock-price performance patterns and trends are formed. We perform our back-testing over the past five years and reduce the analysis to a short list of ten names and associated weights that are likely to outperform.
Our lists are algorithm-based suggestions to consider. As with every investment, there are a number of associated risks including underperformance of the wider market or the specific ETF. our work is based on likelihood of repeating a pattern and is not, and does not pretend to be in any way, an investment recommendation.