Looking beyond the $300b risk of capital outflow prediction

by Andreas Roell


By now, most of us have likely become aware of JP Morgan’s prediction that approximately $300b of capital in equity markets are at the risk of outflow. As a quick reminder, this forecast is based on the traditional 60/40 approach of mutual funds, which are in need to rebalance sometime during the course of the end of the year, as a result of the growth of the stock markets.


While this forecast captured headlines, I felt that it is important to absorb this analysis in a way that considers various perspectives. Having led an algorithmic-focused asset management firm for over four years now, I have learned that human traders have a emotional bias related to their analysis of the market. It is obviously something that algorithmic teams like AlphaTrAI pride ourselves to be independent of.


So when thinking about the emotional impact of this story, it will likely cause some extreme reactions in terms of shifting out of, going short on equities, or simply sitting out via cash allocations. As a fundamental reminder, stock markets pride themselves on having priced the future in real time. This would mean that we should be sitting comfortable as human intelligence and emotional triggers should soften this capital outflow before it even happens. Couple that with mutual funds equity estimated share to be about 19% (source: $7t from Business Insider divided by stock market value found here).


Just to put this into perspective, on March 16, 2020 the Dow Jones approximately had its largest loss in market capitalization of $875b, representing a 13% drop. That is 190% higher than the $300b forecasted here.


Why do I even want to discuss this topic here? As mentioned earlier, we are still finding ourselves in a financial trading world dominated by humans. On top of that, the share of “hobby traders” aka Retail Traders now account for almost 25% of trades that is up from 10% in 2019.


With that, our financial markets are highly exposed to human limitations of decision making. I classify these as limited data processing and analysis capabilities. In my work, I have obviously been exposed to a machine’s ability to create a more holistic market perspective behind specific market events. This was completely missing for me when I read all of the news stories about JP Morgan’s forecast. Maybe it was our world’s need for a headline grabbing story, or maybe it was just our human limitations to holistically analyze a situation in terms of relational context. This made me want to help and share what I see how algorithms adapt when market rotations in general occur. They don’t treat a situation as an all-or-nothing decision. They evaluate and dynamically decide based on the magnitude of an event.  They classify the risk, and diversify into multiple solutions. It is this composure that I believe will help humans to generally cope with events like this.